Deep Learning using Tensorflow and Cloud AI

This course is designed and developed for providing exposure to participants in Deep Learning, Tensorflow, Keras and Cloud AI using Google Cloud

Duration

4 Days

Level

Advanced Level

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As per your team needs

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This course is the third course in “Machine Learning and AI” learning path. It has been designed and developed for providing exposure to participants in Deep Learning, Tensorflow, Keras and Cloud AI using Google Cloud. Below points provide high-level overview about the course –

Understand the role of Deep Learning, Tensorflow and Cloud AI
Gain hands-on experience with Google Auto ML
Provides hands-on experience in Tensorflow, Keras and Google Cloud AI
Implement Convolutional Neural Networks
Learn how to design, develop, optimise, deploy and monitor Neural Networks
Build Chatbot using Amazon as well as Google Cloud Dialogflow

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This program is designed for those who aspire for Data/ML/AI roles:

Data Engineers
Data Scientists
Machine Learning Engineers
Data Integration Engineers
Data Architects

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Holistic view

Tensorflow Introduction
Spark vs Tensorflow
Spark and Tensorflow
Introduction to Serverless Architecture
Current Challenges with On-Premise Architectures
How Google enables higher productivity?
How Key Google Products fit in Enterprise Architecture?
How to design modern Data Analytics Pipeline on GCP?
Hands-on exercise: Getting familiar with Google Cloud Platform

GCP Introduction

Why Google Cloud Platform (GCP)?
How Innovations at Google driving Data Engineering and Science globally?
Key Google Products related to Data and Machine Learning
The relationship among Data Science and Machine Learning
Come on same page w.r.t. terms and concepts

Interactive Data exploration using DataLab

Introduction
Why Cloud AI
Google Cloud AI Framework
Google Cloud AI Layers

Cloud AI

Machine Learning APIs

Introduction to Machine Learning APIs
Key ML Use Cases
Vision API
Natural Language API
Translate API
Speech API

AutoML

What is AutoML
Why AutoML
AutoML using Vision API
AutoML using Natural Language Processing (NLP)
AutoML Translation
Hands-on Exercise(s)

Artificial Neural Network

Introduction to Neural Networks
Introduction to Perceptron
Neural Network Activation Functions
Basic Neural Nets
Single Hidden Layer Model
Single Hidden Layer Explained
Multiple Hidden Layer Model
Multiple Hidden Layer Results
Hands-on Exercise(s)

TensorFlow Introduction

What is TensorFlow?
Why Tensorflow?
Tensorflow vs other Frameworks
Installing TensorFlow
History of TensorFlow
TensorFlow Architecture
Where can Tensorflow run?
Introduction to Components of TensorFlow
Why is TensorFlow popular?
List of Prominent Algorithms supported by TensorFlow
Simple TensorFlow Example
Options to Load Data into TensorFlow
Create Tensorflow pipeline
Hands-on Exercise(s)

TensorFlow API

TensorFlow Graphs
Variables and Placeholders
Activation Functions
Building Models
Deploying Models on Google Cloud
Monitoring Model through Tensorboard
Dropout
Regularization
Hands-on Exercise(s)

Keras API

What is Keras?
Why Keras?
Keras Basics
Working with Keras
Hands-on Exercise(s)

Transfer learning

What is transfer learning
Why transfer learning
Neural Network Architecture with Transfer Learning
Hands-on Exercise(s)

Convolutional Neural Networks (CNN)

CNN History
Understanding CNNs
Various Layers like Pooling, Convolution, Relu etc.
CNN Applications
Hands-on Exercise(s)

Recurrent Neural Networks

What are Recurrent Neural Networks?
Different types of RNNs
Language model and sequence generation
Sampling novel sequences
Vanishing gradients with RNNs
Gated Recurrent Unit (GRU)
Long Short Term Memory (LSTM)
Bidirectional RNN
Deep RNNs
Hands-on Exercise(s)

CloudML: Scalable Models on GCP

Why Cloud ML?
Running TensorFlow model in Local mode
Porting TensorFlow models to GCP
Deploying Models in Production
Model Predictions
Hands-on exercise(s)

Conversational AI

Intro to ChatBots
Key options available
Building ChatBot
Hands-on Exercise(s)

Hands-on Exercise(s):

GCP
Machine Learning API
AutoML
- Preparing and formatting training data for AutoML Translation
- Preparing and formatting training data for Natural Language Processing Translation
Tensorflow
- MNIST dataset intro
- ML Introduction
- Basic Operations
- Convolutional Network
- Neural Network Raw
- Convolutional Network Raw
- Tensorboard Basic
- Save Restore model
- Tensorboard advanced
- Prevent overfitting with dropout and regularization.
Keras
- NN
- Image classification Demo
- Convolutional net
- Handwritten Digit Recognition
ChatBot
- Amazon Lex Bot
- DialogFlow Chat Bot
Deploying Tensorflow Model on CloudML

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Participants should have knowledge level equivalent to what is specified in “Data and Machine Learning Fundamentals” course (Beginner level course in “Machine Learning and Artificial Intelligence” learning path).

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