Deep Learning Online Training

Deep Learning Online Training

Introduction to Deep Learning

  • Deep Learning: A revolution in Artificial Intelligence
  • Limitations of Machine Learning
  • What is Deep Learning?
  • Advantage of Deep Learning over Machine learning
  • 3 Reasons to go for Deep Learning
  • Real-Life use cases of Deep Learning
  • 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
  • Regression
  • Classification
  • Clustering
  • Reinforcement Learning
  • Underfitting and Overfitting
  • Optimization

Understanding the Fundamentals of Neural Networks Using Tensorflow

  • How Deep Learning Works?
  • Activation Functions
  • Illustrate Perceptron
  • Training a Perceptron
  • Important Parameters of Perceptron
  • What is Tensorflow?
  • Tensorflow code-basics
  • Graph Visualization
  • Constants, Placeholders, Variables
  • Creating a Model
  • Step by Step – Use-Case Implementation

Deep Dive into Neural Networks Tensorflow

  • Understand limitations of A Single Perceptron
  • Understand Neural Networks in Detail
  • Illustrate Multi-Layer Perceptron
  • Backpropagation – Learning Algorithm
  • Understand Backpropagation – Using Neural Network Example
  • MLP Digit-Classifier using TensorFlow
  • TensorBoard

Master Deep Networks

  • Why Deep Learning?
  • SONAR Dataset Classification
  • What is Deep Learning?
  • Feature Extraction
  • Working of a Deep Network
  • Training using Backpropagation
  • Variants of Gradient Descent
  • Types of Deep Networks

Convolution 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) & Autoencoders

  • Restricted Boltzmann Machine
  • Applications of RBM
  • Collaborative Filtering with RBM
  • Introduction to Autoencoders
  • Autoencoders applications
  • Understanding Autoencoders

Keras

  • Define Keras
  • How to compose Models in Keras
  • Sequential Composition
  • Functional Composition
  • Predefined Neural Network Layers
  • What is Batch Normalization
  • Saving and Loading a model with Keras
  • Customizing the Training Process
  • Using TensorBoard with Keras
  • Use-Case Implementation with Keras

TFlearn

  • Define TFlearn
  • Composing Models in TFlearn
  • Sequential Composition
  • Functional Composition
  • Predefined Neural Network Layers
  • What is Batch Normalization
  • Saving and Loading a model with TFlearn
  • Customizing the Training Process
  • Using TensorBoard with TFlearn
  • Use-Case Implementation with TFlearn