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Deep Learning - Neural Networks (DL001)


Description
This course is an introduction to Neural Networks which will help you know the basic architecture and learn about topics like CNN & RNN.

Topics in this course -
Neural Networks: Introduction
Perceptron/Neurons Layers
Neural Network structure
Forward propagation
Back-propagation
Activation functions
Shallow and deep neural network
Building a network from scratch
Overfitting in a neural network

Pre-requisite: Basic Understanding of Vectors, Matrices, Differentiation.
Content
  • Mathematics (Online Links) for Reference
  • Online links for Maths required for Neural Network
  • Jupyter Notebook
  • Neural Network
  • How to install the library with required backend using cpu / gpu ?
  • Classification Example
  • Image classification example
  • Keras Tuner for tuning NN
  • Assignment 1
  • DL-1 Training Feedback
  • Topics Wise Videos
  • Neural Networks: Introduction
  • Perceptron/Neurons Layers
  • Neural Network structure
  • Forward propagation
  • Back-propagation
  • Activation functions -- Sigmoid, Tanh & Relu
  • Shallow and deep neural network
Completion rules
  • All units must be completed
  • Leads to a certificate with a duration: Forever
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