Introduction to Deep Learning
  • Deep Learning: A revolution in Artificial Intelligence
  • Limitations of Machine Learning
  • Discuss the idea behind Deep Learning
  • Advantage of Deep Learning over Machine learning
  • 3 Reasons to go Deep
  • Real-Life use cases of Deep Learning
  • Scenarios where Deep Learning is applicable
  • The Math behind Machine Learning: Linear Algebra
  • Scalars
  • Vectors
  • Matrices
  • Tensors
  • Hyperplanes
  • The Math Behind Machine Learning: Statistics
  • Probability
  • Conditional Probabilities
  • Posterior Probability
  • Distributions
  • Samples vs Population
  • Resampling Methods
  • Selection Bias
  • Likelihood
Fundamental of Nural Network
  • Defining Neural Networks
  • The Biological Neuron
  • The Perceptron
  • Multi-Layer Feed-Forward Networks
  • Training Neural Networks
  • Backpropagation Learning
  • Gradient Descent
  • Stochastic Gradient Descent
  • Quasi-Newton Optimization Methods
  • Generative vs Discriminative Models
  • Activation Functions
  • Linear
  • Sigmoid
  • Tanh
  • Hard Tanh
  • Softmax
  • Rectified Linear
  • Loss Functions
  • Loss Function Notation
  • Loss Functions for Regression
  • Loss Functions for Classification
  • Loss Functions for Reconstruction
  • Hyperparameters
  • Learning Rate
  • Regularization
  • Momentum
  • Sparsity
Introduction to TensorFlow
  • What is TensorFlow?
  • Use of TensorFlow in Deep Learning
  • Working of TensorFlow
  • How to install Tensorflow
  • HelloWorld with TensorFlow
  • Running a Machine learning algorithms on TensorFlow
Convolutional Neural Networks (CNN)
  • Introduction to CNNs
  • CNNs Application
  • Architecture of a CNN
  • Convolution and Pooling layers in a CNN
  • Understanding and Visualizing a CNN
  • Transfer Learning and Fine-tuning Convolutional Neural Networks
Recurrent Neural Networks (RNN)
  • Intro to RNN Model
  • Application use cases of RNN
  • Modelling sequences
  • Training RNNs with Backpropagation
  • Long Short-Term memory (LSTM)
  • Recursive Neural Tensor Network Theory
  • Recurrent Neural Network Model
Restricted Boltzmann Machine(RBM) and Autoencoders
  • Restricted Boltzmann Machine
  • Applications of RBM
  • Collaborative Filtering with RBM
  • Introduction to Autoencoders
  • Autoencoders applications
  • Understanding Autoencoders
  • Variational Autoencoders
  • Deep Belief Network