How to train ANNs

• Introduction
• Perceptron Learning Rule
• Gradient Descent Rule
• Minimize Cost Function
• Tuning Learning Rate
• Stochastic vs Batch Gradient Descent

Multi-layer ANN

• Intro to MLP
• Forward propagation
• Minimize Cost Function
• Backpropagation
• Convergence in a neural net
• Overfitting and Capacity
• Hyperparameters in an ANN

Introduction to TenserFlow

• Intro to TenserFlow
• Computational Graph
• Key highlights
• Creating a Graph
• Regression example
• Gradient Descent
• Saving and Restoring Models
• Tf.layers API
• Keras-based networks
• TensorBoard

Deep Neural Nets

• Vanishing/Exploding Gradients
• Xavier Initialization
• Leaky ReLUs and ELUs
• Batch Normalization
• Transfer Learning
• Unsupervised Pre-training
• Optimizers
• Regularization
• Dropout

Convolution Neural Networks

• Intro to CNNs
• Django Exception
• Kernel filter
• Feature Maps
• Polling
• CNN Architecture
• Implement CNN in TenserFlow

Recurrent Neural Network

• Intro to RNNs
• Unfolded RNNs
• Basic RNN cell
• Dynamic RNN
• Training RNNs
• Time-series predictions
• LSTM
• Word Embeddings
• Seq2Seq Models
• Implement RNN in TensorFlow

Other forms of Deep Learning

• Autoencoders
• Reinforcement Learning(RL)
• Generative Adversarial Networks(GANs)