AI & Deep Learning with TensorFlow Training

• Understand the Autoencoders and varitional Autoencoders
• Learn to apply the Analytical mathematics to the data
• Understand what Neural networks are?
• Learn about Autoencoders and discuss their Applications
• Learn about the application of Convolutional Neural Networks
• Develop understanding of Unsupervised Learning
• Learn how to run a “Hello World” program in TensorFlow
• Describe Deep Learning
• Learn about TFlearn implementation
• Learn the implementation procedure of Collaborative Filtering with RBM
• Understand what Restricted Boltzmann Machine is?
• Learn about Autoencoders and discuss their Applications

• The Developers aspiring to be a 'Data Scientist'
• The Business Analysts willing to understand Deep Learning (ML) Techniques
• The Analytics Managers willing to lead the team of analysts 
• The Information Architects willing to gain expertise in Predictive Analytics
• Analysts wanting to understand Data Science methodologies
• The Professionals willing to captivate and analyze Big Data

1 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

2. 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

3. 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

4. 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

5. 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

6. 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

7. Restricted Boltzmann Machine (RBM) & Autoencoders

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

8. 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

9. 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