What is Computer Vision? Learn the Basics

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What is Computer Vision?

Computer vision is a field of study which enables computers to replicate the human visual system. It’s a subset of artificial intelligence which collects information from digital images or videos and processes them to define the attributes. The entire process involves image acquiring, screening, analysing, identifying and extracting information. This extensive processing helps computers to understand any visual content and act on it accordingly. 

Computer vision projects translate digital visual content into explicit descriptions to gather multi-dimensional data. This data is then turned into computer-readable language to aid the decision-making process. The main objective of this branch of artificial intelligence is to teach machines to collect information from pixels. 

Let’s dive deeper with the help of an example. 

Automatic cars aim at reducing the need for human intervention while driving, through various AI systems. Computer vision is part of such a system which focuses on imitating the logics behind human vision to help the machines take data-based decisions. CV systems will scan live objects and categorise them, based on which the car will keep running or make a stop. If the car comes across an obstacle or a traffic light, it will analyse the image, create a 3D version of it, consider the features and decide on an action- all within a second.

Why is Computer Vision Important?

From selfies to landscape images, we are flooded with all kinds of photos today. According to a report by Internet Trends, people upload more than 1.8 billion images everyday, and that’s just the number of uploaded images. Imagine what the number would come to if you consider the images stored in phones. We consume more than 4,146,600 videos on YouTube and send 103,447,520 spam mails everyday. Again, that’s just a part of it – communication, media and entertainment, the internet of things are all actively contributing to this number. This abundantly available visual content demands analysing and understanding. Computer vision helps in doing that by teaching machines to “see” these images and videos.

Additionally, thanks to easy connectivity, the internet is easily accessible by all today. Children are especially susceptible to online abuse and “toxicity”. Apart from automating a lot of functions, computer vision also ensures moderation and monitoring of online visual content. One of the main tasks involved in online content curation is indexing. Since the content available on the internet is mainly of two types, namely text, visual, and audio categorisation becomes easy. Computer vision uses algorithms to read and index images. Popular search engines like Google and Youtube use computer vision to scan through images and videos to approve them for featuring. By way of doing so, they not only provide users with relevant content but also protect against online abuse and “toxicity”.

Origin of Computer Vision

Computer vision is not a new concept; in fact, it dates back to the 1960s. It all started with an MIT project -“Summer Vision Project” which analysed scenes to identify objects. David Marr, the celebrated neuroscientist, laid down the building blocks of computer vision, taking a cue from the functions of the cerebellum, hippocampus, and cortex of human perception. He has been dubbed the father of computer vision since, and the field has evolved to include much more complicated functionalities.  

Computer Vision Basic Functions

Depending on the uses, computer vision has the following uses:

computer vision benefits

How to learn Computer Vision? 

  1. Laying the Foundation: Probability, statistics, linear algebra, calculus and basic statistical knowledge are prerequisites of getting into the domain. Similarly, knowledge of programming languages like Python and MATLAB will help you grasp the concepts better. 
  2. Digital Image Processing: Learn how to compress image and videos using JPEG and MPEG files. Knowledge of basic image processing tools like histogram equalisation, median filtering and more are required. Once you know the basics of image processing and restoration, you will be ready to pick up the more critical skills of computer vision.
  3. Machine Learning Basics: Knowledge of Convoluted Neural Networks, fully connected neural networks, support vector machines, recurrent neural networks, generative adversarial network, and autoencoders are necessary to get started with computer vision.
  4. Basic Computer Vision: The next step in the process is to decode the mathematical models involved in the image and video formulations. Once you understand how pattern recognition and signal processing works, you can get into advanced learning.

How to become a Computer Vision Engineer?

Computer vision engineers are in high demand in the market today, thanks to the enormous amount of visual content that needs to be worked upon. What exactly does a computer engineer do?

A computer vision engineer creates and uses vision algorithms to work on the pixels of any visual content (images, videos and more)

They use a data-based approach to develop solutions.

They usually come with a background in AIML and have experience working on a variety of systems, including segmentation, machine learning, and image processing.

If you want to become a computer vision engineer, you need to pick up the basic skills of the domain and work on projects that will give you a hands-on experience of industry-relevant problem-solving. Great Learning’s Deep Learning certificate program introduces you to all the basics of the domain and sets you on the path of becoming a computer vision engineer. 

Job Description of Computer Vision Engineer

The ideal candidate must have a sound knowledge of machine learning algorithms, principles and their application. He/she should have experience working on Deep Learning architectures like CNN, GAN, autoencoders, and more. He/she should also be familiar with deep learning frameworks like TensorFlow and PyTorch. He/she must also have a good understanding of object detection and localisation models like YOLO, RCNN, Mask-RCNN and more.

Requirements:

Knowledge of process automation and AI pipeline designing.

1+ years of experience in Artificial Intelligence projects

Programming skills (Python, C++, MATLAB) is a must

Ability to drive projects independently and with the team

Working knowledge of tools like git, docker etc.

Excellent written and verbal communication skills

Degrees in computer science, electrical engineering preferred

Computer Vision functions

Which language is best suited for computer vision?

We have several programming language choices for computer vision – OpenCV using C++, OpenCV using Python, or MATLAB. However, most engineers have a personal favourite, depending on the task they perform. Beginners often pick OpenCV with Python for its flexibility. It’s a language most programmers are familiar with, and owing to its versatility is very popular among developers.

Computer vision experts recommend Python for the following reasons:

– Easy to Use: Python is easy to learn, especially for beginners. It is one of the first programming languages learnt by most users. This language is also easily adaptable for all kinds of programming needs.

– Most Used computing language: Python offers a complete learning environment for people who want to use it for various kinds of Computer Vision and Machine Learning experiments. Its numpy, scikit-learn, matplotlib and OpenCV provides an exhaustive resource for any computer vision applications.

– Debugging and Visualisation: Python has an in-built debugger, ‘PDB’ which makes debugging codes in this programming language more accessible. Similarly, Matplotlib is a convenient resource for visualisation.

– Web Backend Development:  Frameworks like Django, Flask, and Web2py are excellent web page builders. Python is compatible with these frameworks and can be easily tweaked to fit your requirements.

MATLAB is the other programming language popular with computer experts. Let’s look into the advantages of using MATLAB:

– Toolboxes: MATLAB has one the most exhaustive toolboxes; whether it is a statistical and machine learning toolbox, or an image processing toolbox, MATLAB has one included for all kinds of needs. The clean interfaces of each of these toolboxes enables you to implement a range of algorithms. MATLAB also has an optimisation toolbox which ensures that all algorithms perform at their best.

– Powerful Matrix Library: Images and other visual content contains multi-dimensional matrices along with linear algebra in different algorithms which becomes easier to work within MATLAB. The linear algebra routines included in MATLAB work fast and effective.

– Debugging and Visualisation: Since there is a single integrated platform for coding in MATLAB, writing, visualising and debugging codes become easy.

– Excellent Documentation: MATLAB enables you to document your work adequately so that it is accessible later. Documentation is essential not just for future reference but also to help coders work faster. MATLAB’s documentation allows users to work twice the speed of OpenCV.

Computer Vision experts also gravitate towards OpenCV for the following reasons:

– Zero Cost: OpenCV comes at free of cost and what’s better than saving a little money? You can use it for commercial applications, even check the source for corrections. The most significant advantage of using OpenCV is that you don’t have to make your project open source.  

– Exhaustive Library: OpenCV has the most extensive collection of algorithms. The transparent API makes OpenCL devices compliant on devices and optimises performance. 

– Platform and Devices: A number of embedded vision applications and mobile apps prefer OpenCV as their vision library of choice for its performance-focused design. You can use it across all platforms and devices.

– Large Community: OpenCV is used by over 9 million people who are continually updating and helping each other through blogs and forums. A significant advantage of using OpenCV is that you will always find support from the community. Since companies like Google, Intel and AMD fund its development, OpenCV is evolving fast.

What are the applications of Computer Vision?

– Medical Imaging: Computer vision helps in MRI reconstruction, automatic pathology, diagnosis, machine aided surgeries and more.

– AR/VR: Object occlusion (dense depth estimation), outside-in tracking, inside-out tracking for virtual and augmented reality. 

– Smartphones: All the photo filters (including animation filters on social media), QR code scanners, panorama construction, Computational photography, face detectors, image detectors (Google Lens, Night Sight) that you use are computer vision applications.

– Internet: Image search, geolocalisation, image captioning, ariel imaging for maps, video categorisation and more.

Computer Vision Challenges 

Computer vision might have emerged as one of the top fields of machine learning, but there are still several obstacles in its way of becoming a leading technology. Human vision is a complicated and highly effective system which is difficult to replicate through technology. However, that’s not to say that computer vision will not improve in the future, but for now, we are facing the following challenges:

Reasoning Issue: Modern neural network-based algorithms are complex system whose functionings are often obscure. In situations like these, it becomes tough to find the logic behind any task. This lack of reasoning creates a real challenge for computer vision experts who try to define any attribute in an image or video.

Privacy and Ethics: Vision powered surveillance is a serious threat to privacy in a lot of countries. It exposes people to unauthorised use of data. Face recognition and detection is prohibited in some countries because of these problems. 

Fake Content: Like all other technologies, computer vision in the wrong hands can lead to dangerous problems. Anybody with access to powerful data centres is capable of creating fake images, videos or text content.

Adversarial Attacks: These are optical illusions for the computer. When an attacker creates a faulty machine learning model, they intend the machine using it to fail. These flawed models are difficult to identify and can cause serious damage to any system.

Computer Vision basics

Future of Computer Vision

Computer vision is a fast-developing field and has gathered a lot of attention from various industries. It will be able to function on a broader spectrum of content in the future. The domain already enjoys a steady market of 2.37 million US dollars and is expected to grow at a 47% CAGR till 2023. With the amount of data we are generating every day, it’s only natural that machines will use that data to craft solutions. 

Once computer vision experts can resolve the current problems of the domain, we can expect a trustworthy system that automates content moderation and monitoring. With corporate giants like Google, Facebook, Apple and Microsoft investing in computer vision, it’s only a matter of time before it takes over the global market.

Your Essential Weekly Guide to Data Science- October II

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Data science is one of the fastest evolving domains today, with professionals striving to find practical solutions for the digital economy. Dubbed as the ‘sexiest job of the 21st century’, Data science garners a surprising amount of questions. As professionals and students try to demystify the domain, we bring the latest developments and changes in it to help you stay updated. Keep reading to learn all about the top trends. 

Python 3.9: New Changes Data Scientists should Expect

Python Software Foundation released Python 3.8 on Monday, which has new features to deliver an enhanced developer experience. The changelog for Python 3.9 was introduced simultaneously, prompting data scientists worldwide to become familiarised with the changes. This article focuses on all the new advancements and modifications in the new version of the changelog. 

Synthetic data: A new frontier for data science

GDPR has made data capturing and distributing difficult for all Europe based organisations. This scarcity of data has affected businesses and data scientists alike. Synthetic data has emerged as a promising solution to this problem. Sophisticated synthetic data which is anonymously generated and is based on ghost individual can be used effectively by data scientists to replace traditional historical data.

L1ght Saves Kids From Online Toxicity, Using Data Science And AI 

Children spending more time on the internet is a growing concern for parents. L1ght has developed a platform that ensures children are protected against toxic content online. This platform uses deep learning, a subset of machine learning and data science to screen through images, texts, video, voice and sound in real-time to identify the ‘anti-toxic’ categories. The need for ‘toxicity’ moderation has pushed L1ght to come up with solutions that protect children against online abuse.

Data Scientists in MNCs Vs SMEs: Here’s What the Community Says

The data scientist community is divided over what is a better place for working – startups or MNCs. While some think that in SMEs, the designations don’t quite match the job they do, others believe small enterprises offer more to learn. MNCs, on the other hand, provide a clear roadmap for climbing up the ladder. The domain is still evolving, and professionals are trying to understand how the working conditions vary from one industry to the other.

Stanford pilots data science fellowship program

Standford is offering a data science fellowship program to eligible candidates in an attempt to take data science research ahead. Last summer, the university ran a pilot program to train students on data-driven solution discovery. This cohort of students had different educational background, but that did not stop them from learning the essentials of the domain and gather valuable findings. 

If you are interested in more such news on data science, check out our Data Science Roundup page.

Deep Learning Tutorial: What it Means

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  1. What is Deep Learning?
  2. Why is Deep Learning important?
  3. How does Deep Learning work?
  4. What is the difference between Deep Learning and Machine Learning?
  5. How to get started with Deep Learning?
  6. Top Open Source Deep Learning Tools
  7. Commonly-Used Deep Learning Applications

 What is Deep Learning?

Deep Learning is a subset of Artificial Intelligence – a machine learning technique that teaches computers and devices logical functioning. Deep learning gets its name from the fact that it involves going deep into several layers of network, which also includes a hidden layer. The deeper you dive, you more complex information you extract.

Deep learning methods rely on various complex programs to imitate human intelligence. This particular method teaches machines to recognise motifs so that they can be classified into distinct categories. Pattern recognition is an essential part of deep learning and thanks to machine learning, computers do not even need to depend on extensive programming. Through deep learning, machines can use images, text or audio files to identify and perform any task in a human-like manner. 

Deep-Learning-Applications

All the self-driving cars you see, personalised recommendations you come across, and voice assistants you use are all examples of how deep learning is affecting our lives daily. If appropriately trained computers can successfully imitate human performance and at times, deliver accurate results – the key here is exposure to data. Deep learning focuses on iterative learning methods that expose machines to huge data sets. By doing so, it helps computers pick up identifying traits and adapt to change. Repeated exposure to data sets help machines understand differences, logics and reach a reliable data conclusion. Deep learning has evolved in recent times to become more reliable with complex functions. It’s no wonder that this particular domain is garnering a lot of attention and attracting young professionals.

Why is Deep Learning Important?

To say Deep Learning is important is, to say nothing about its growing popularity. It contributes heavily towards making our daily lives more convenient, and this trend will grow in the future. Whether it is parking assistance through technology or face recognition at the airport, deep learning is fuelling a lot of automation in today’s world.

However, deep learning’s relevance can be linked most to the fact that our world is generating exponential amounts of data today, which needs structuring on a large scale. Deep learning uses the growing volume and availability of data has been most aptly. All the information collected from these data is used to achieve accurate results through iterative learning models.

The repeated analysis of massive datasets eradicates errors and discrepancies in findings which eventually leads to a reliable conclusion. Deep learning will continue to make an impact in both business and personal spaces and create a lot of job opportunities in the upcoming time. 

How does Deep Learning work?

At its core, deep learning relies on iterative methods to teach machines to imitate human intelligence. An artificial neural network carries out this iterative method through several hierarchical levels. The initial levels help the machines learn simple information, and as the levels increase, the information keeps building. With each new level machines pick up further information and combines it with what it had learnt in the last level. At the end of the process, the system gathers a final piece of information which is a compound input. This information passes through several hierarchies and has semblance to complex logical thinking.

Let’s break it down further with the help of an example –

Consider the case of a voice assistant like Alexa or Siri, to see how it uses deep learning for natural conversation experiences. In the initial levels of neural network, when the voice assistant is fed data, it will try to identify voice inundations, intonations and more. For the higher levels, it will pick up information on vocabulary and add the findings of the previous levels to that. In the following levels, it will analyse the prompts and combine all its conclusions. For the topmost level of the hierarchical structure, the voice assistant will have learnt enough to be able to analyse a dialogue and based on that input, deliver a corresponding action. 

Deep learning use in voice assistant

What is the difference between Deep Learning and Machine Learning?

Though often used interchangeably, deep learning and machine learning are both part of artificial intelligence and are not the same thing. Machine Learning is a broader spectrum which uses data to define and create learning models. Machine learning tries to understand the structure of data with statistical models. It starts with data mining where it extracts relevant information from data sets manually after which it uses algorithms to direct computers to learn from data and make predictions. Machine learning has been in use for a long time and has evolved over time. Deep Learning is a comparatively new field which focuses only on neural networking to learn and function. Neural networking, as discussed earlier, replicates the human neurals artificially to screen and gather information from data automatically. Since deep learning involves end-to-end learning where raw data is fed to the system, the more data it studies, the more precise and accurate the results are. 

This brings us to the other difference between deep learning and machine learning. While the former can scale up with larger volumes of data, machine learning models are limited to shallow learning where it reaches a plateau after a certain level, and any more addition of new data makes no difference. 

Following are the key differences between the two domain:

  • Data Set Size – Deep Learning doesn’t perform well with a smaller data set. Machine Learning algorithms can process a smaller data set though (still big data but not the propensity of a deep learning data set) without compromising its performance. The accuracy of the model increases with more data, but a smaller data set may be the right thing to use for a particular function in traditional machine learning. Deep Learning is enabled by neural networks constructed logically by asking a series of binary questions or by assigning weights or a numerical value to every bit of data that passes through the network. Given the complexity of these networks at its multiple layers, deep learning projects require data as large as a Google image library or an Amazon inventory or Twitter’s cannon of tweets.
  • Featured Engineering – An essential part of all machine learning algorithms, featured engineering and its complexity marks the difference between ML and DL. In traditional machine learning, an expert defines the features to be applied in the model and then hand-codes the data type and functions. In Deep Learning, on the other hand, featured engineering is done at sub-levels, including low to high-level features segregation to be fed to the neural networks. It eliminates the need for an expert to define the features required for processing by making the machine learn low-level features as simple as shape, size, textures, and pixel values, and high-level features such as facial data points and a depth map.
  • Hardware Dependencies – Sophisticated high-end hardware is required to carry the heavyweight of matrix multiplication operations and computations that are the trademark of deep learning. Machine learning algorithms, on the other hand, can be carried out on low-end machines as well. Deep Learning algorithms require GPUs so that complex computations can be efficiently optimized.
  • Execution Time – It is easy to assume that a deep learning algorithm will have a shorter execution time as it is more developed than a machine learning algorithm. On the contrary, deep learning requires a larger time frame to train not just because of the enormous data set but also because of the complexity of the neural network. A machine learning algorithm can take anything from seconds to hours to train, but a deep learning algorithm can go up to weeks, in comparison. However, once trained, the runtime of a deep learning algorithm is substantially less than that of machine learning.

An example would make these differences easier to understand:

Consider an app which allows users to take photos of any person and then helps to find apparels that are the same or similar to the ones featured in the photo. Machine learning will use data to identify the different clothing item featured in the photo. You have to feed the machine with the information. In this case, the item labelling will be done manually, and the machine will categorise data based on predetermined definitions.

In the case of deep learning, data labelling do not have to be done manually. Its neural network will automatically create its model and define the features of the dress. Now, based on that definition, it will scan through shopping sites and fetch you other similar clothing items. 

How to get started with Deep Learning?

Before getting started with Deep Learning, candidates must ensure that their mathematical and programming language skills are in place. Since Deep Learning is a subset of artificial intelligence, familiarity with the broader concepts of the domain is often a prerequisite. Following are the core skills of Deep Learning:

  • Maths: If you are already freaking out at the sheer mention of maths, let me put your fears to rest. The mathematical requirements of deep learning are basic, the kind that’s taught at the undergraduate level. Calculus, probability and linear algebra are few of the examples of topics that you need to be through with. For professionals who are keen on picking up deep learning skills but do not have a degree in maths, there are plenty of ebooks and maths tutorials available online, which will help you learn the basics. These basic maths skills are required for understanding how the mathematical blocks of neural network work. Mathematical concepts like tensor and tensor operations, gradient descent and differentiation is crucial to neural networking. Refer to books like Calculus Made Easy by Silvanus P. ThompsonProbability Cheatsheet v2.0The best linear algebra booksAn Introduction to MCMC for Machine Learning to understand the basic concepts of maths.
  • Programming Knowledge: Another prerequisite of grasping Deep Learning is knowledge of various programming languages. Any deep learning book will reveal that there are several applications for Deep Learning in Python as it is a highly interactive, portable, dynamic, and object-oriented programming language. It has extensive support libraries that limit the length of code to be written for specific functions. It is easily integrated with C, C++, or Java and its control capabilities along with excellent support for objects, modules, and other reusability mechanisms makes it the numero uno choice for deep learning projects. Easy to understand and implement, aspiring professionals in the domain start with Python as it is open-sourced. However, several questions have been raised on its runtime errors and speed. While Python is used in several desktop and server applications, it is not used for many mobile computing applications. While Python is a popular choice for many, owing to its versatility, Java and Ruby are equally suitable for beginners. Books like Learn to Program (Ruby)Grasshopper: A Mobile App to Learn Basic Coding (Javascript)A Gentle Introduction to Machine Fundamentals and Scratch: A Visual Programming Environment From MIT are some of the online resources you can refer to pick up coding skills. Great Learning, one of India’s most premier ed-tech platforms, even has a python curriculum designed for beginners who want to transition smoothly from non-technical backgrounds into Artificial Intelligence and Machine Learning. Following is a breakdown of the course showcasing what it covers:

Great Learning Python Course structure

  • Cloud Computing: Since almost all kinds of computing are hosted by cloud today, basic knowledge of cloud is essential to master Deep Learning. Beginners can start by understanding how cloud service providers work. Dive deep into concepts like compute, databases, storage and migration. Familiarity with major cloud service providers like AWS and Azure will also give you a competitive advantage. Cloud computing also requires an understanding of networking which a concept closely associated with Machine Learning. Clearly, these techniques are not mutually exclusive and familiarising yourself with these concepts will help you each of the skills faster.

Now that we have covered the fundamentals of deep learning, it is time to dive deeper into the different ways in which Deep Learning can be put to use. 

Deep Learning Types

  • Deep Learning for Computer Vision: Deep Learning methods used to teach computers image classification, object identification and face recognition involves computer vision. Simply put, computer vision tries to replicate human perception and its various functions. Deep learning does that by feeding computers with information on: 

1. Viewpoint variation: This is where an object is viewed from different perspectives so that its three-dimensional features are well recognised.

2. Difference in illumination: This refers to objects viewed in different lighting conditions.

3. Background clutter: This helps to distinguish obscure objects from a cluttered background.

4. Hidden parts of images: objects which are partially hidden in pictures need to be identified.

  • Deep Learning for Text and Sequence: Deep learning is used in several text and audio classifications, namely like speech recognition, sentiment classification, Machine translation, DNA sequence analysis, video activity recognition and more. In each of these cases, sequence models are used to train computers to understand, identify and classify information. Different kinds of recurrent neural networks like many-to-many, many-to-one and one-to-many are used for sentiment classification, object recognition and more. 
  • Generative Deep Learning: Generative models are used for data distribution through unsupervised learning. Variational Autoencoder (VAE) and Generative Adversarial Networks (GAN) aims at distributing data optimally so that computers can generate new data points from different variations. VAE maximises the lower limit for data-log likelihood, whereas GAN tries to strike a balance between Generator and Discriminator. 

Top Open Source Deep Learning Tools

Of the various deep learning tools available, these are the top freely available ones:

  1. TensorFlow: one of the best frameworks, TensorFlow is used for natural language processing, text classification and summarization, speech recognition and translation and more. It is flexible and has a comprehensive list of libraries and tools which lets you build and deploy ML applications. TensorFlow finds most of its application in developing solutions using deep learning with python as there are several hidden layers (depth) in deep learning in comparison to traditional machine learning networks. Most of the data in the world is unstructured and unlabeled that makes Deep Learning TensorFlow one of the best libraries to use. A neural network nodes represent operations while edges stand for multidimensional data arrays (tensors) flowing between them.
  2. Microsoft Cognitive Toolkit: Most effective for image, speech and text-based data, MCTK supports both CNN and RNN. For complex layer-type, users can use high-level language, and the fine granularity of the building blocks ensures smooth functioning.
  3. Caffe: One of the deep learning tools built for scale, Caffe helps machines to track speed, modularity and expression. It uses interfaces with C, C++, Python, MATLAB and is especially relevant for convolution neural networks. 
  4. Chainer: A Python-based deep learning framework, Chainer provides automatic differentiation APIs based on the define-by-run approach (a.k.a. dynamic computational graphs). It can also build and train neural networks through high-level object-oriented APIs. 
  5. Keras: Again, a framework that can work both on CNN and RNN, Keras is a popular choice for many. Built on Python, it is capable of running on TensorFlow, CNTK, or Theano. It supports fast experimentation and can go from idea to result without any delay. The default library for Keras is TensorFlow. Keras is dynamic as it supports both recurrent networks and Convolutional Neural Networks and can also work on a combination of the two. Keras is popular for its user-friendliness guaranteed by its simple API. It is easier to debug Keras models as they are developed in Python. The compact models provide ease of extensibility with new modules that can be directly added as classes and functions in a building blocks kind of configuration.
  6. Deeplearning4j: Also a popular choice, Deeplearning4j is a JVM-based, industry-focused, commercially supported, distributed deep-learning framework. The most significant advantage of using Deeplearning4j is speed. It can skim through massive volumes of data in very little time.

Commonly-Used Deep Learning Applications

  • Virtual Assistants – Amazon Echo, Google Assistant, Alexa, and Siri are all exploiting deep learning capabilities to build a customized user experience for you. They ‘learn’ to recognize your voice and accent and present you a secondary human experience through a machine by using deep neural networks imitating not just speech but also the tone of a human. Virtual assistants help you shop, navigate, take notes and translate them to text, and even make salon appointments for you.
  • Facial Recognition – The iPhone’s Facial Recognition uses deep learning to identify data points from your face to unlock your phone or spot you in images. Deep Learning helps them protect the phone from unwanted unlocks and making your experience hassle-free even when you have changed your hairstyle, lost weight, or in poor lighting. Every time you unlock your phone, deep learning uses thousands of data points to create a depth map of your face and the inbuilt algorithm uses those to identify if it is really you or not.
  • Personalization – E-Commerce and Entertainment giants like Amazon and Netflix, etc. are building their deep learning capacities further to provide you with a personalized shopping or entertainment system. Recommended items/series/movies based on your ‘pattern’ are all based on deep learning. Their businesses thrive on pushing out options in your subconscious based on your preferences, recently visited items, affinity to brands/actors/artists, and overall browsing history on their platforms.
  • Natural Language Processing – One of the most critical technologies, Natural Language Processing is taking AI from good to great in terms of use, maturity, and sophistication. Organizations are using deep learning extensively to enhance these complexities in NLP applications. Document summarization, question answering, language modelling, text classification, sentiment analysis are some of the popular applications that are already picking up momentum. Several jobs worldwide that depend on human intervention for verbal and written language expertise will become redundant as NLP matures.
  • Healthcare – Another sector to have seen tremendous growth and transformation is the healthcare sector. From personal virtual assistants to fitness bands and gears, computers are recording a lot of data about a person’s physiological and mental condition every second. Early detection of diseases and conditions, quantitative imaging, robotic surgeries, and availability of decision-support tools for professionals are turning out to be game-changers in the life sciences, healthcare, and medicine domain.
  • Autonomous Cars – Uber AI Labs in Pittsburg are engaging in some tremendous work to make autonomous cars a reality for the world. Deep Learning, of course, is the guiding principle behind this initiative for all automotive giants. Trials are on with several autonomous cars that are learning better with more and more exposure. Deep learning enables a driverless car to navigate by exposing it to millions of scenarios to make it a safe and comfortable ride. Data from sensors, GPS, geo-mapping is all combined together in deep learning to create models that specialize in identifying paths, street signs, dynamic elements like traffic, congestion, and pedestrians.
  • Text Generation – Soon, deep learning will create original text (even poetry), as technologies for text generation is evolving fast. Everything from the large dataset comprising text from the internet to Shakespeare is being fed to deep learning models to learn and emulate human creativity with perfect spelling, punctuation, grammar, style, and tone. It is already generating caption/title on a lot of platforms which is testimony to what lies ahead in the future.
  • Visual Recognition – Convolutional Neural Networks enable digital image processing that can further be segregated into facial recognition, object recognition, handwriting analysis, etc. Computers can now recognize images using deep learning. Image recognition technology refers to the technology that is based on the digital image processing technology and utilizes artificial intelligence technology, especially the machine learning method, to make computers recognize the content in the image. Further applications include colouring black and white images and adding sound to silent movies which has been a very ambitious feat for data scientists and experts in the domain.

Great Learning’s Deep Learning Certificate Program is a comprehensive course which teaches you the essentials of the domain and its industry applications. Its hands-on projects and live sessions help students pick up the key functionalities effectively, even if they have no prior technical knowledge. 

GL Excelerate- 4th Edition

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We, at Great Learning, organized our 4th edition of GL Excelerate in Bengaluru recently. Our career fairs are platforms where professionals find relevant openings and reach out to hiring companies. This time, the event was attended by more than 350 students and 18 hiring companies. More than 500 interviews were conducted, which resulted in offers for 20 candidates from top companies like HSBC, Dell, Infosys, Tech Mahindra, Wipro, and many more. More than 100 final rounds of interviews are still in line, following which even more students are expected to get job offers.

GL Excelerate career fairs have always been successful in bringing together professionals and hiring companies so that both can benefit from it, and it was no different this time. The 4th edition of GL Excelerate saw a significant confluence of professionals who are skilled for the future and are ready to take on roles as Business Analysts, Data Analysts, Data Scientists, Machine Learning Engineers, and more. Hiring companies were also able to find and hire the best candidates for relevant openings. Here’s what some of our hiring partners thought about the event:

“Healthcare and Pharma manufacturing industries are going through a massive transformation, all thanks to government regulations and value-based care models. They have a huge demand for people who know data and domain, and new-age tools such as Machine-Learning and AI to solve real-world problems. It’s exciting to be part of an event that’s focused on professionals who have skills of the future. We’ve already interviewed 40+ candidates here, and are likely to make an offer to some of them. Also, we shall continue to participate in the upcoming GL Excelerate editions- a great platform to seek new talents.”

– Preeti Verma, Genpact

GL_Excelerate

“Data Science and AIML are one of the best career moves for experienced professionals as those are nextgen technologies. Most importantly, we were happy to see so many candidates at GL who are interested to upskill themselves and make themselves future-ready. We definitely would like to participate in future events like these. Thanks for arranging this event.”

– Prithviraj Koley, Tech Mahindra

“Great Learning’s career fair – GL Excelerate, gave us the opportunity to come across highly skilled data scientists in large numbers. Out of the 74 resumes shortlisted, 30 odd folks cleared the first round. We are hoping they do well in the subsequent rounds and eventually get hired in TheMathCompany. We look forward to a continued relationship with GL Excelerate and hope we can join hands for yet another season of the career fair soon!”

– Eva Sharma, TheMathCompany

GL_Excelerate4

Our career fairs are part of a much bigger initiative – GL Excelerate support, through which we guide our students to become job-ready. From helping them build their resume to connecting them with hiring companies, GL Excelerate aims at providing the best career support. Our most recent career fair was one of the many that we have planned for our students. Visit our GL Excelerate page and learn more about how candidates benefit from it.

Your Essential Weekly Guide to Data Science

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Data science enthusiasts are always looking for new developments and advances made in the field. With that in mind, we try to bring you data science news that showcase the most relevant trends for data science professionals. This week we have curated articles that talk about the learning resources, popular skills, job requisites, entrepreneurial ventures and more.  Read along.

10 Great Python Resources for Aspiring Data Scientists

Data scientists are often required to know more than one programming language. However, in most cases, Python is a language of choice for many. Since Python is extremely versatile, a thorough knowledge of this language helps data scientists in a number of tasks – hence its popularity. These Python resources are a great way of learning the language, especially if you are getting started in the field.

How to maintain your big data analytics software

When a company buys or subscribes to an analytics solution, keeping the work current can be a challenge. But maintaining that software is key to helping it enjoy a long, useful life. However, there are several challenges in the way to that. This article breaks down the essentials of analytics maintenance softwares so that companies can easily keep using those for a long period of time.

Why the newly minted data scientist wants a new job

Data science is one of the hottest jobs of the 21st century and professionals walking into this field often don’t require a strong background in technology. However, candidates picking up data science skills frequently face a mismatch of job expectations and reality. A large number of data scientists are looking for new jobs since they feel they are stuck with a job that does not utilise their skillset correctly.

Skills A Data Scientist Must Have To Land A Job: AIM Skills Study 2019

In a recently published report, AIM showcases the top skills required for a career in data science. Since data science is an evolving field, the trending subset of skills and tools keep changing every year. These skill sets mentioned in this article are the most relevant ones of 2019 and the upcoming years. Python has emerged as a majorly preferred programming language, while GPU hardware and CUDA knowledge have become essential to work with huge sets of data. 

Top 6 Priorities Data Science Startup Founders Shouldn’t Ignore

As an increasing number of data science experts opt for entrepreneurial ventures, startup companies flourish all over the country. Data science entrepreneurship requires a lot of patience, perseverance and above all, knowledge of the latest trends of the domain. This article lists a number of data science priorities that professionals should not ignore in order to achieve success. 

If you are looking for similar articles on data science, keep an eye on this space. We bring you more!

What is TensorFlow? The Machine Learning Library Explained

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The deep learning artificial intelligence research team at Google, Google Brain, in the year 2015 developed a software library which they named as TensorFlow for Google’s internal use. This Open-Source Software library is used by the research team to perform several important tasks.

TensorFlow is at present the most popular software library. There are several real-world applications of deep learning that makes TensorFlow popular. Being an Open-Source library for deep learning and machine learning, TensorFlow finds a role to play in text-based applications, image recognition, voice search, and many more. DeepFace, Facebook’s image recognition system, uses TensorFlow for image recognition. It is used by Apple’s Siri for voice recognition. Every Google app that you use has made good use of TensorFlow to make your experience better.

What are the Applications of TensorFlow?

  • Google use Machine Learning in almost all of its products: Google has the most exhaustive database in the world. And they obviously would be more than happy if they could make the best use of this by exploiting it to the fullest. Also, if all the different kinds of teams — researchers, programmers, and data scientists — working on artificial intelligence could work using the same set of tools and thereby collaborating with each other, all their work could be made much simpler and more efficient. As technology developed and our needs widened, such a toolset became a necessity. Motivated by this necessity, Google created TensorFlow  — a solution that they have been long waiting for.
  • TensorFlow bundles together the study of Machine Learning and algorithms and will use it to enhance the efficiency of its products — by improving their search engine, by giving us recommendations, by translating to any of the 100+ languages, and more.

What is Machine Learning?

A computer can perform various functions and tasks relying on inference and patterns as opposed to the conventional methods like feeding explicit instructions, etc. The computer employs statistical models and algorithms to perform these functions. The study of such algorithms and models is termed as Machine Learning.

Deep learning is another term that one has to be familiar with. A subset of Machine Learning, deep learning is a class of algorithms that can extract higher-level features from the raw input. Or, in simple words, they are algorithms that teach a machine to learn from examples and previous experiences. 

Deep learning is based on the concept of Artificial Neural Networks, ANN. Developers use TensorFlow to create many multiple layered neural networks. Artificial Neural Networks, ANN, is an attempt to mimic the human nervous system to a good extent by using silicon and wires. The intention behind this system is to help develop a system that can interpret and solve real-world problems like a human brain would. 

What makes TensorFlow popular?

  • It is free and open-sourced: TensorFlow is an Open-Source Software released under the Apache License. An Open Source Software, OSS, is a kind of computer software where the source code is released under a license that enables anyone to access it. This means that the users can use this software library for any purpose — distribute, study and modify — without actually having to worry about paying royalties.
  • When compared to other such Machine Learning Software Libraries — Microsoft’s CNTK, or Theano — TensorFlow is relatively easy to use. Thus, even new developers with no significant understanding of machine learning can now access a powerful software library instead of building their models from scratch.
  • Another factor that adds to its popularity is the fact that it is based on graph computation. Graph computation allows the programmer to visualize his/her development with the neural networks. This can be achieved through the use of Tensor Board. This comes in handy while debugging the program. The Tensor Board is an important feature of TensorFlow as it helps monitor the activities of TensorFlow– both visually and graphically. Also, the programmer is given an option to save the graph for a later use.  

Tensors

All the computations associated with TensorFlow involves the use of tensors. This leads to an interesting question :

What are Tensors?

It is a vector/matrix of n-dimensions representing types of data. Values in a tensor hold identical data types with a known shape. This shape is the dimensionality of the matrix. A vector is a one-dimensional tensor; matrix a two-dimensional tensor. Obviously, a scalar is a zero dimensional tensor.

In the graph, computations are made possible through interconnections of tensors. The  mathematical operations are carried by the node of the tensor whereas the input-output relationships between nodes are explained by a tensor’s edge.

Thus TensorFlow takes an input in the form of an n-dimensional array/matrix (known as tensors) which flows through a system of several operations and comes out as output. Hence the name TensorFlow. A graph can be constructed to perform necessary operations at the output.

Applications

Below are listed a few of the use cases of TensorFlow:

 

  • Voice and speech recognition: The real challenge put before programmers was that a mere hearing of the words will not be enough. Since, words change meaning with context, a clear understanding of what the word represents with respect to the context is necessary. This is where deep learning plays a significant role. With the help of Artificial Neural Networks or ANNs, such an act has been made possible by performing word recognition, phoneme classification, etc.

Thus with the help of TensorFlow, artificial intelligence-enabled machines can now be trained to receive human voice as input, decipher and analyze it, and perform the necessary tasks. A number of applications makes use of this feature. They need this feature for voice search, automatic dictation, and more.

Let us take the case of Google’s search engine as an example. While you are using Google’s search engine, it applies machine learning using TensorFlow to predict the next word that you are about to type. Considering the fact that how accurate they often are, one can understand the level of sophistication and complexity involved in the process.

  • Image recognition: Apps that use the image recognition technology are probably the ones that popularized deep learning among the masses. The technology was developed with the intention to train and develop computers to see, identify, and analyze the world like how a human would.  Today, a number of applications finds these useful — the artificial intelligence enabled camera on your mobile phone, the social networking sites you visit, your telecom operators, to name a few.

In image recognition, Deep Learning trains the system to identify a certain image by exposing it to a number of images that are labelled manually. It is to be noted that the system learns to identify an image by learning from examples that are previously shown to it and not with the help of instructions saved in it on how to identify that particular image.

Take the case of Facebook’s image recognition system, DeepFace. It was trained in a similar way to identify human faces. When you tag someone in a photo that you have uploaded on Facebook, this technology is what that makes it possible. 

Another commendable development is in the field of Medical Science. Deep learning has made great progress in the field of healthcare — especially in the field of Ophthalmology and Digital Pathology. By developing a state of the art computer vision system, Google was able to develop computer-aided diagnostic screening that could detect certain medical conditions that would otherwise have required a diagnosis from an expert. Even with significant expertise in the area, considering the amount of tedious work one has to go through, chances are that the diagnosis vary from person to person. Also, in some cases, the condition might be too dormant to be detected by a medical practitioner. Such an occasion won’t arise here because the computer is designed to detect complex patterns that may not be visible to a human observer.    

TensorFlow is required for deep learning to efficiently use image recognition. The main advantage of using TensorFlow is that it helps to identify and categorize arbitrary objects within a larger image. This is also used for the purpose of identifying shapes for modelling purposes. 

  • Time series: The most common application of Time Series is in Recommendations. If you are someone using Facebook, YouTube, Netflix, or any other entertainment platform, then you may be familiar with this concept. For those who do not know, it is a list of videos or articles that the service provider believes suits you the best. TensorFlow Time Services algorithms are what they use to derive meaningful statistics from your history.

Another example is how PayPal uses the TensorFlow framework to detect fraud and offer secure transactions to its customers. PayPal has successfully been able to identify complex fraud patterns and have increased their fraud decline accuracy with the help of TensorFlow. The increased precision in identification has enabled the company to offer an enhanced experience to its customers. 

A Way Forward

With the help of TensorFlow, Machine Learning has already surpassed the heights that we once thought to be unattainable. There is hardly a domain in our life where a technology that is built with the help of this framework has no impact.

 From healthcare to entertainment industry, the applications of TensorFlow has widened the scope of artificial intelligence to every direction in order to enhance our experiences. Since TensorFlow is an Open-Source Software library, it is just a matter of time for new and innovative use cases to catch the headlines.

Your Essential Weekly Guide to Data Science and Analytics- September Part III

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Owing to its diverse set of applications, data science has emerged as one of the most in-demand career paths for young professionals. Upskilling in data science will certainly set you in the path of a high-flying career. However, upskilling alone is never enough in an ever-evolving domain like data science. You have to closely follow the latest trends and technological developments – which we understand can be quite a task sometimes, which data science blogs to follow, which trends to watch? We have put together a list of news articles that highlight the most impactful developments and trends. Read through to stay updated.

Top 10 Data and Analytics Trends to Watch Out in 2020

With 2020 already a mere last quarter away, data science enthusiasts are trying to look into the trends that will dominate the domain in the coming year. Augmented Analysis is among the top trends of data science in 2020. It combines ML and AI applications to change how analytics content is created, devoured and shared. The augmented analysis will be driving data science, ML platforms and embedded analytics. Data analysis automation, continuous intelligence, NLP and conversational analytics are among the other data science trends that will take over the market.

Explorium secures $19M funding to automate data science and machine learning-driven insights

Explorium, an Israel based startup has received a $19 million funding to work towards automating its data science and machine learning platforms. “Just as a search engine scours the web and pulls in the most relevant answers for your need, Explorium scours data sources inside and outside your organization to generate the features that drive accurate models”, says co-founder and CEO Maor Shlomo. The company works in three stages- data enrichment, feature engineering and predictive modelling to help companies derive insights and add features to their applications

Data science safeguards digital transactions

The mass adoption of smartphones and other smart devices has lead to the digitization of money worldwide. Philippines is among the countries to head towards a digital economy steadfastly. While digitization of money brings convenience, it also exposes organizations and individuals to cyber crimes. Digital payment platforms are focusing on ways to secure financial transactions from any kind of cyber attack. Data science applications are helping in analysing and predicting ways of doing that.

“For consumers, there is really no substitute to awareness,” said Nagesh Devata, general manager of Southeast Asia Cross Border Markets for PayPal. “For enterprises, we need to move from reactive to predictive models in risk management.”

Which Data Science Skills are core and which are hot/emerging ones?

Recent surveys revealed that professionals think that 30 data science skills are the most coveted in data science resumes. Tensor flow, deep learning, Apache Spark, Pytorch are among the top data science tools and skills that are becoming increasingly relevant today. SQL and ETL Data Preparation, on the other hand, are losing popularity as more advanced technologies and tools are taking over.

University of Virginia’s data science school gets state approval

University of Virginia has finally received an approval for its data science school from the State Council of Higher Education for Virginia. “I am delighted that the School of Data Science has cleared its final hurdle and can officially move forward,” said UVA President Jim Ryan in a statement. “I want to thank the State Council of Higher Education for Virginia for sharing our excitement in this proposal, and Phil Bourne and his team at the Data Science Institute for their hard work.” The approval was pending for 8 months after they had received a $120 million donation.

If you found these articles to be interesting then head over to our Data Science blog to get more updates.

Introduction to Data Visualisation- Why is it Important?

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Ever come across a pie chart or any other graphic image depicting information? I’m sure, you must have. They are one of the most common ways of putting forward statistics results that could be understood by many – but did you ever stop to think, wait, what are these visuals called? What is its purpose? And most importantly, are they used in business growth scenarios? Firstly, the bar graphs, pie charts, or any other method of representing information is known as Data visualization. Surprisingly, it is one of the most common mathematics topics people come across in their day-to-day life.

What is data visualization?

Yes, you know what data visualization is, but by definition, it means much more. In simple words, data visualization is a graphical representation of any data or information. Visual elements such as charts, graphs, and maps are the few data visualization tools that provide the viewers with an easy and accessible way of understanding the represented information. In this world governed by Big Data, data visualization enables you or decision-makers of any enterprise or industry to look into analytical reports and understand concepts that might otherwise be difficult to grasp.

Why is data visualization important?

By now, you would have understood how data visualization simplifies the way information is presented. However, is that the only power of data visualization? Not really. As the world is changing, the need for information is changing as well. Here are a few benefits of data visualization:
● Easily, graspable information – Data is increasing day-by-day, and it is not wise for anyone to scram through such quantity of data to understand it. Data visualization comes handy then.
● Establish relationships – Charts and graphs do not only show the data but also established co-relations between different data types and information.
● Share – Data visualization is also easy to share with others. You could share any important fact about a market trend using a chart and your team would be more receptive about it.
● Interactive visualization – today, when technological inventions are making waves in every market segment, regardless of big or small, you could also leverage interactive visualization to dig deeper and segment the different portions of charts and graphs to obtain a more detailed analysis of the information being presented.
● Intuitive, personalized, updatable – Data visualization is interactive. You could click on it and get another big picture of a particular information segment. They are also tailored according to the target audience and could be easily updated if the information modifies.

What are different Data Visualization Tools?

Data visualization tool helps in, well, visualizing data. Using these tools, data and information can be generated and read easily and quickly. Many data visualization tools range from simple to complex and from intuitive to obtuse.

● Tableau Desktop – A business intelligence tool which helps you in visualizing and understanding your data.
● Zoho Reports – Zoho Reports is a self-service business intelligence (BI) and analytics tool that enables you to design intuitive data visualizations.
● Microsoft Power BI – Developed by Microsoft, this is a suite of business analytics tools that allows you to transform information into visuals.
● MATLAB – A detailed data analysis tool that has an easy-to-use tool interface and graphical design options for visuals.
● Sisense – A BI platform that allows you to visualize the information to make better and more informed business decisions.

What are Data Visualization Techniques?

Here are a few data visualizations that you must know:
● Know the target audience – this shouldn’t come as a surprise. Designing a chart of a graph should always be done based on the audience that will view it.
● Create a goal – or more like a logical narrative. Ensure to set clear goals that must be conveyed through the infographic. Also, the relevant content type is a must.
● Choose the chart type – A pie chart does not complement every information visually. Similarly, a bar graph does not show every statistic clearly. Choose the chart part accurately to put forth the information.
● Context – Use of colours is encouraged depending upon the context. A decrease in the profit growth could be marked red, whereas green could show the increasing parameter.
● Use tools – Yes, one of the easiest ways to create data visuals is using tools. Use them as they make the charts intuitive as well as easy to read.

What are Data Visualization examples?

What better way to understand data visualization, if not with examples? Here are a few for your reference:
● Government Budget – Government budgets are always tough to understand as they number and more numbers. A recent example is a colour-coded treemap that was designed by The White House during Barack Obama’s presidency, which visually broke down the US’s 2016 the budget for better understanding and put government programs in context.
● World population – How would you present the world population along with their density? Simple, by visual representation. A world map showing the population density is another data visualization example.
● Profit and loss – Business companies often resort to pie charts or bar graphs showing their annual profit or loss margin.
● Films and dialogues – Out of many characters in the film who will have how many dialogues? Data visualization is the answer here. The makers of popular sitcom ‘FRIENDS’ used a pie chart during shooting to ensure that every six characters have an equal number of jokes and dialogues.
● Anscombe’s quartet – It is one of the most well-known and popular, which has four data sets of identical descriptive statistics, but they appear different when graphed.

All of these four data sets have different distributions and consists of 11 points
marked on x and y-axis.

Data Visualization in Statistics

The mathematical topic, statistics is very closely related to data visualization; they both represent data.
● By combining both statistics and data visualization, businesses can transform the data into a valuable asset that drives growth.
● Thereafter, data visualizations along with statistics could be used to establish correlations between different data sets. Businesses often use to co-relate their different departments' results.
● Both of them make it easier to notice any patterns that might be recurring in the data.
● Use of data visualization in statistics optimizes the way business read data and extract relevant insights from it further pursue how to improve brand name.
● It even helps in determining which statistical figures are relevant for work or task and which are not.

Data Visualization using Tableau

Tableau is the US-based data visualization firm that easily connects to almost any data source; it could either be corporate Data Warehouse, Microsoft Excel or web-based data. Also, the data does not have to be accumulated as Tableau allows analysis on real-time data feed. Tableau enables you to connect with various data sources, files, and servers. Tableau also encourages you to work on different file formats such as CSV, JSON, Txt, and even servers such as Tableau Server, MySQL, Amazon Redshift and more.

There are many other benefits of Tableau as well:

● Tableau is a smart tool and even recommends which visualization tool could be used for what purpose. You could navigate through the software, click on the ‘Show Me’ feature available within the tool, which would show different types of graphs and charts with various attributes.
● Tableau also supports map, data of which could be easily modified by you, and unlike other BI tools, you don’t have to break your head to use them. It is relatively easy to use maps in Tableau.
● Tableau has an interactive UI that plays an important role in how you could design and develop data visualization charts. The tool offers numerous fields and chart options such as heat map, Scatter Plot, Packed Bubble, and more.

What is the Scope of Data Visualization in Business?

In this era, when Big Data is making waves everywhere, the scope of data visualization is much more than could be anticipated. A vast amount of data is generated today by organizations and managing, structuring, and reading that data is a hectic task. However, with data visualization technique, it is possible to not only read that data but also leverage it in business.

● Display and reports – This is one of the common uses of data visualization in business. Organizations can create, update, translation, or delete any textual information into a visual context.
● Operational alerting – another scope of data visualization is operational alerting. It enables sales, marketing, and internal processes teams to stay informed about any new promotion, product launch, or more. Data visualization allows sending visual alerts to the teams in real-time.
● Mindmaps – A diagramming tool, mind maps are used in creating and visualizing structure and relationships, classifying ideas used for observing and managing information, arriving at a decision, and solving other business problems.
● Business growth – Probably one of the main scopes of data visualization. Business growth is measured and represented using graphics to better understand how an organization is doing in terms of sales.
● Other sectors – Other scopes of data visualization are in the medical field,
geography, biology, and meteorology that depict different types of data used in these fields.

Finally, data visualization plays an important role in displaying and depicting different and huge amount of data types in simple, understandable structure and layout. With multifold benefit in almost every industrial and commercial field, data visualization is today’s topmost growing technique, which is preferred by a large number of data scientists to visualize and analyze complex data sets.

Your Essentials Guide to Artificial Intelligence – September Part III

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Artificial Intelligence is an often overused yet little understood term in popular culture, evoking visions of a machine dominated world. The reality, however, is far from that – AI is not a disruptive element as it is frequently portrayed to be. It’s creating more job opportunities than it is taking away. Most importantly, it is aiding a wide variety of technological advances. The following news articles reflect how tech giants are pushing AI research forward and how different industries are benefiting from it.

Google Opens an AI Research Lab in Bengaluru to Advance Computer Science Research in India

At its recent annual event, Google for India announced that it is setting up a research lab in India which will focus on Artificial Intelligence and its applications in India. This research lab will be based in India’s tech capital, Bangalore and will be headed by a renowned scientist and ACM (Association for Computing Machinery) fellow, Dr Manish Gupta. The research activities will centre around applying AIML technologies in healthcare, agriculture and education. This lab is expected to help millions of people in the country who do not have access to quality healthcare or education, apart from taking the AI pledges forward.

Samsung Electronics is Actively Investing in Artificial Intelligence in Seven Countries

In a Samsung Newsroom interview recently, Gary Geunbae Lee, Senior Vice President and Head of Samsung Research’s AI Center in Seoul, revealed AI insights that are driving their centres in South Korea (Seoul), the U.S. (Silicon Valley and New York), the U.K. (Cambridge), Canada (Toronto and Montreal) and Russia (Moscow). These centres focus on various facets of AI, ranging from computer vision to language understanding, data analysis, robotics and more. The company aims to fuse AI functionalities with its products to present consumers with more smart solutions. AI ethical compliance is also a key focus area for Samsung, as it believes that AI can cause serious social problems if mishandled and abused.

Microsoft And Qualcomm Debut Their Vision AI Developer Kit  

Microsoft and Qualcomm have come together to build their debut Vision AI Developer Kit for computer vision applications. This platform uses Microsoft Azure ML and Azure IoT Edge platforms to run AI models locally or in the cloud. The hardware uses Qualcomm Snapdragon 603 chip and LDDR4X memory. 

Artificial Intelligence Used to Recognize Primate Faces in The Wild

Studying primitive species like Chimpanzees who have complex social lives can lead to many sociological discoveries but long-term research on primates is often hindered by low lighting, poor image quality and motion blur. Scientists at the University of Oxford are using AI software to recognise and monitor wildlife activities. These AI inputs are expected to aid research and cut down on expenses.

AI Technology is Creating Real Value Across Industries

“AI—like any technology in history—is neutral, it’s what we do with it that counts, so it’s our responsibility, as an AI ecosystem, to drive it in the right direction.” – Says Affectiva CEO Rana el Kaliouby. Affectiva is one of the 50 companies featured in the Forbes list of promising artificial intelligence companies. The founders of all of these companies think that AI has been held in a bad light for way too long, whereas in reality, it’s only as beneficial as we make it. On the other hand, founders also feel that we have inflated expectations from AI, which is after all, not as intelligent as a pattern-matching system. 

If you found these articles to be interesting then head over to our Artificial Intelligence blog  to get more updates.