TensorFlow Training

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

Review of Machine Learning Algorithms

Regression
Classification
Clustering

Reinforcement Learning
Underfitting and Overfitting
Optimization
Convex Optimization

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

Defining Deep Learning
Defining Deep Networks
Common Architectural Principals of Deep Networks
Reinforcement Learning application in Deep Networks
Parameters
Layers
Activation Functions – Sigmoid, Tanh, ReLU
Loss Functions
Optimization Algorithms
Hyperparameters
Summary