Machine Learning and Deep Learning: Basic to Intermediate

Course Description

Course Overview:

Deep learning training gives you an in-depth understanding of the architecture of TensorFlow Core, API layers, and the use cases. Master unsupervised learning models, deep learning models and more. Right from installing and configuring TensorFlow, importing data, simple models to develop complex layered models and architectures to crunch huge data sets leveraging the distributed, robust and scalable machine learning framework from Google.

Learn to implement Keras on top of TensorFlow to experiment with deep neural networks and tune machine learning models to produce more successful results with our deep learning with TensorFlow course.

Course Objectives:

Articulate the core architecture and API layers TensorFlow
Construct a computing environment and learn to install TensorFlow
Develop TensorFlow graphs required for everyday computations
Use logistic regression for classification along with TensorFlow
Develop, design and train a multilayer neural network with TensorFlow
Demonstrate Activation functions and Optimizers in detail with hands-on
Demonstrate intuitively convolutional neural networks for image recognition
Design and construct a neural network from simple to more accurate models
Understand recurrent neural networks, its applications and learn how to build these solutions
Understand hyper-parameters and tuning
Learn how to build industry’s leading uses cases eg, Recommendation systems, Speech recognition, commercial grade Image classification and object localization etc….
Lead ML/DL projects based on TensorFlow implementation

Pre-requisites:

Basic knowledge of statistical concepts is desirable.

Target Audience:

Software engineers
Data scientists
Data analysts
Statisticians with an interest in deep learning

Course Duration:

28 hours – 4 days

Course Content:

Introduction

Data science & its importance
Key Elements of Data Science
Artificial Intelligence & Machine Learning Introduction
Who uses AI?
AI for Banking & Finance, Manufacturing, Healthcare, Retail and Supply Chain
What makes a Machine Learning Expert?
What to learn to become a Machine Learning Developer?
Review of Machine Learning: Regression, Classification, Clustering, Reinforcement Learning, Underfitting and Overfitting, Optimization
Deep Learning: A revolution in Artificial Intelligence
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

Neural Networks Basics

How Neural Networks Work?
Various activation functions – Sigmoid, Relu, Tanh
Perceptron and Multi-layer Perceptron
What is TensorFlow?
TensorFlow code-basics
Graph Visualization
Constants, Placeholders, Variables
Creating a Model
Step by Step – Use-Case Implementation
Introduction to Keras
Understand Neural Networks in Detail
Illustrate Multi-Layer Perceptron
Backpropagation – Learning Algorithm
Understand Backpropagation – Using Neural Network Example
MLP Digit-Classifier using TensorFlow

Deep Neural Networks

Why Deep Networks
Why Deep Networks give better accuracy?
Understand How Deep Network Works?
How Backpropagation Works?
Illustrate Forward pass, Backward pass
Different variants of Gradient Descent
Types of Deep Networks
Batch Normalization
Activation and Loss functions
Hyper parameter tuning
Training challenges and techniques
Optimizers, learning rate, momentum, etc.

Convolutional Neural Networks

Introduction to CNNs
CNNs Application
Architecture of a CNN
Forward propagation & Backpropagation for CNNs
Convolution, Pooling, Padding & its mechanisms
Understanding and Visualizing a CNN
An overview of pre-trained models (AlexNet, VGGNet, InceptionNet & ResNet) and Transfer Learning
Image classification using CNN

Advanced Computer Vision