Course Description:

Deep learning is an artificial intelligence function that imitates the workings of the human brain in processing data and creating patterns for use in decision making. Deep learning is a subset of machine learning in artificial intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabeled. Also known as deep neural learning or deep neural network.

Course Content:

Tensorflow installation
  • Tensorflow installation 2.0
  • Tensorflow installation 1x with Virtual Environment
  • Tensorflow 2.0 function
  • Tensorflow 2.0 neural network creation
  • Tensorflow 1x functions
  • Tensorflow 1x neural network and its function
  • Keras introduction
  • Keras in depth with neural network creation
  • Pytorch
  • Pytorch installation
  • Pytorch functional overview
  • Pytorch neural network creation
  • Artificial Neural Network
  • What is Neuron?
  • Simple perceptron
  • What is Activation Function?
  • What is Optimizers?
  • Types of Activation Functions
  • Types of Optimizers
  • How Neural Network Work
  • How Neural Network Learn
  • Various neural network Architecture
  • Sigmoid
  • Softmax
  • Relu
  • Tanh
  • Cross Entropy
  • Gradient Descent
  • Stochastic Gradient Descent
  • Full vs Batch vs Stochastic Gradient Descent
  • Back Propagation
  • Evaluating ANN
  • Improving and Fine Tuning ANN
  • Build a Regression based Prediction Model Using
    Keras and TensorFlow
    Convolutional Neural Networks
  • Introduction to CNN
  • Architecture of CNN
  • Why CNN is better for Deep Learning?
  • How do CNN work?
  • Convolutional layer
  • Relu Layer
  • Max pooling Layer
  • Flattening
  • Practical implementation for CNN
  • Recurrent Neural Networks
  • Introduction to RNN
  • Architecture of RNN
  • How do RNN works
  • Building a Story writer using character level RNN
  • LSTM (Long and Short Term Memory)
  • Introduction to LSTM
  • Architecture of LSTM
  • How do LSTM works
  • Cell State
  • Hidden state
  • Forget Gate
  • Input Gate
  • Output Gate
  • Practical implementation for LSTM
  • GRU (Gated Recurrent Unit)
  • Introduction to GRU
  • Architecture of GRU
  • How do GRU works
  • Practical implementation for GRU