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Perceptron, Perceptron connections, Activation functions, Heavy sided, Sigmoid, logit

Perceptron learning algorithm, rate and epoch, Binary classification with Perceptron

Document classification with Perceptron, Limitations of Perceptron, Linearly seperable concept

ANN: Artificial Neural networks, Neural Net, Neural Decision boundaries, ANN for XOR

Feedforward and feedback ANN, Multilayer Perceptron, Hidden layer, Network Diagram, MSE cost function

Minimising cost function, Backpropagation, Training FF with BP, Forward Propagation steps, Error calculation

Updating weights, Classifying handwritten digits, Keras, Features of Keras, Keras backend

Keras Neural Networks, Layers, Deep learning trends, probabilistic modeling, Logistic regression using keras

Kernel methods, Decision boundary, Kernel function, Learning Deep, Keras workflow, sequntial layers

Compilation, Fit, Using Layers, optimiser, Preprocessing, Preparing the labels, Overfitting

Tensors, 1D, 2D and 3D tensors, Image processing, Time Series and uses, Video data, tensor operations

Elementwise operations, Broad casting, Tensor dot, dot operation, Tensor re-shaping, Transpose

Geometric interpretation of tensor operations, Deep learning algorithm, engine of neural networks

Derivatives, SGD, mini-batch, Issues with SGD, batch SGD, loss surface, GD intuition, Optimizers

Momentum, Local and Global Minimum, Naive implementation, Chaining derivatives, Topology selection

Choosing Loss function, Projects: Classifying reviews, Classifying news wires, Predicting house prices

MLPs, RNN, CNN, ReLU, Regulariztion in Deep Networks, Dropout, Softmax activation, tanh, Sigmoid

Choices of Lossfunction, Plotting the model, Conv2D, Convolution, Convolution process, Pooling

MaxPoolin2D, Pooling and Compression, Performance evaluation, RNN digit classification, SimpleRNN, LSTM Model

Sentiment Analysis using LSTM, GRU, Stacked RNN, Keras Functional API, Flattend and Dropout

Tensorflow, Dimensions, Immutability, Variables, Computational Graph, Sessions, Correct the Initialization errors

Global Variable Initializser, Assign values to Variables, Guidelines and Tensor types, Feed values, run graph

Eval and run, Evaluating multiple nodes, Dependencies between nodes, Solutions to multiple runs

clossing session, NN Architecture, using activation function in Tensorflow, Swish Activation

Project: US Census Bureau data sets, re-structure for iterations, Project: Fashion MINIST data set classification

Deep Learning Syllabus