Deep Learning

Machine Learning & Analytics

Course Description

Deep Learning dives deeper into Machine Learning and can be thought of as a subset of Machine Learning. Neural networks allow computers to mimic the human brain. Just like our brain has the innate capability to recognize patterns that allow categorizing and classifying information, neural networks achieve the same for computers.

Deep Learning does incorporate deep neural networks due to the numerous layers of nested hierarchy of decision trees as millions of data points. Deep Learning leverages Natural Language Processing (NLP) and deep neural networks to establish insights facilitating effective decision-making.

32 Hours

  • In this module, you’ll get an introduction to Deep Learning and understand how Deep Learning solves problems which Machine Learning cannot.
  • Understand fundamentals of Machine Learning and relevant topics of Linear Algebra and Statistics.
  • Deep Learning has a wide horizon for IT professionals, electrical and electronics engineers, designers, and solution architects. It can also be a boon for the existing and budding entrepreneurs who are interested in building solutions for their customers.
  • Professionals working in other sectors like pharmaceuticals, real estate, sales, finance, designing, manufacturing, electrical, retail, healthcare, etc. can also benefit from Machine Learning, AI & Deep Learning solutions.
  • Graduates and newcomers can also kick-start their career with the Deep Learning
  • Concepts about Machine Learning
  • Statistics and Machine learning algorithms

Deep Learning Introduction

  • What is deep learning and how it is different from Machine Learning
  • Deep Learning – Usecases
  • Packages & Libraries available for implementing Deep learning
  • Where does Deep Leaning fit into Data Science Ecosystem
  • Quick Review of Machine Learning

Neural Networks with Tensor Flow

  • 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

Neural Networks with Tensor Flow – Advanced

  • 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
  • Summary

Python libraries for Data Science

  • Installation & setup Python IDE – Anaconda
  • NumPy
  • SciPy
  • Pandas,
  • Matplotlib
  • SciKit-Learn
  • NLTK

Convolutional 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 Network (RNN)

  • Intro to RNN Model
  • Application use cases of RNN
  • Modeling sequences
  • Training RNNs with Backpropagation
  • Long Short-Term memory (LSTM)
  • Recursive Neural Tensor Network Theory
  • Recurrent Neural Network Model

RBM and Autoencoders

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

Keras & TFlearn

  • 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
  • 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

Sample Capstone Project Cloud – Deep Learning with Amazon Web Service

  • Deep Learning AMIs available
  • Image Rekognition API
  • Common Practise to setup Deep Learning Project in cloud

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