AI & Deep Learning with Tensor flow Training Course

  Lecture-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

  Lecture-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

  Lecture-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

  Lecture-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

  Lecture-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

  Lecture-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

  Lecture-7 Restricted Boltzmann Machine (RBM) & Autoencoders

·       Restricted Boltzmann Machine

·       Applications of RBM

·       Collaborative Filtering with RBM

·       Introduction to Autoencoders

·       Autoencoders applications

·       Understanding Autoencoders

  Lecture-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

  Lecture-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