About this course
Deep learning allows machines to solve relatively complex problems even when using data that is diverse, less structured or interdependent. Deep learning is a form of machine learning that is inspired and modeled on how the human brain works. In this course you will be introduced to the basics of deep learning and learn how it compares to other techniques. During the course you will also understand the applications of deep learning in various fields and learn more about different frameworks used for neural networks.
Hyper parameter tuning
Deep Neural Networks
Neural Network and deep learning
- 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
- Introduction to Neural Networks
- Building Blocks of Neural Networks
- TensorFlow, Keras, and Tensorboard
- Babysitting the Neural Network
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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