ARTIFICIAL INTELLIGENCE

Introduction to Neural Networks and Deep Learning

Deep learning has enabled innovation and transformation across a broad range of industries. From anomaly detection to video analysis, businesses have been able to leverage artificial intelligence to gain competitive advantage and even change the way their markets approach the customer experience. Deep learning, which is a specialized and advanced form of machine learning, performs what is considered “end-to-end learning”.

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About the course

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A deep learning algorithm is given massive volumes of data, typically unstructured and disparate, and a task to perform such as classification. The resulting model is then capable of solving such complex tasks such as recognizing objects within an image and translating speech in real time.

Three major drivers caused the breakthrough of (deep) neural networks: the availability of huge amounts of training data, powerful computational infrastructure, and advances in academia. Thereby deep learning systems start to outperform not only classical methods, but also human benchmarks in various tasks like image classification or face recognition. This creates the potential for many disruptive new businesses leveraging deep learning to solve real-world problems.

In this course you will learn what is deep learning and why it is needed. During the course you will also understand the applications of deep learning in various fields and learn more about different frameworks for neural networks.

Skills you will gain

  • Gradient Descent
  • Perceptron
  • Neural Networks
  • Activation
  • Loss functions
  • Batch Normalization
  • Hyper parameter tuning
  • Deep Neural Networks
  • Tensor Flow
  • Keras

Course Syllabus

Module 1

Neural Network and deep learning

15.0 Hrs

  • Why data driven?
  • Traditional Method - K Nearest Neighbor Approach
  • Parameters vs Hyper parameters
  • KNN never used on images
  • Parametric Approach
  • Scalars, Vectors, Matrices, And Tensors
  • Linear operations on Vectors and Matrices
  • Vector Properties: Vector norms, some special vectors and matrices
  • Working with Google Colabs
  • Functions and derivatives
  • Optimizing a continuous function
  • Components of Supervised Machine learning
  • Components of Supervised ML: Model, Parameters, and Hyper parameters
  • Components of Supervised ML: Loss functions
  • Regularization
  • Introduction to Neural Networks
  • Building Blocks of Neural Networks
  • TensorFlow, Keras, and Tensorboard
  • Babysitting the Neural Network
  • Show more

Project

Bank Customer Churn Modeling

Course certificate

Get Introduction to Neural Networks and Deep Learning course completion certificate from Great learning which you can share in the Certifications section of your LinkedIn profile, on printed resumes, CVs, or other documents.

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