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

Design and Tailor this course

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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  • 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
  • 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
  • Introduction
  • Why Cloud AI
  • Google Cloud AI Framework
  • Google Cloud AI Layers
  • Introduction
  • Why Cloud AI
  • Google Cloud AI Framework
  • Google Cloud AI Layers
  • Introduction to Machine Learning APIs
  • Key ML Use Cases
  • Vision API
  • Natural Language API
  • Translate API
  • Speech API
  • What is AutoML
  • Why AutoML
  • AutoML using Vision API 
  • AutoML using  Natural Language Processing (NLP)
  • AutoML Translation
  • Hands-on Exercise(s)
  • 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)
  • 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 Graphs
  • Variables and Placeholders
  • Activation Functions
  • Building Models 
  • Deploying Models on Google Cloud
  • Monitoring Model through Tensorboard
  • Dropout 
  • Regularization
  • Hands-on Exercise(s)
  • What is Keras?
  • Why Keras?
  • Keras Basics
  • Working with Keras
  • Hands-on Exercise(s)
  • What is transfer learning
  • Why transfer learning
  • Neural Network Architecture with Transfer Learning
  • Hands-on Exercise(s)
  • CNN History
  • Understanding CNNs 
  • Various Layers like Pooling, Convolution, Relu etc.
  • CNN Applications
  • Hands-on Exercise(s)
  • 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)
  • Why Cloud ML?
  • Running TensorFlow model in Local mode
  • Porting TensorFlow models to GCP
  • Deploying Models in Production
  • Model Predictions 
  • Hands-on exercise(s)
  • Intro to ChatBots
  • Key options available
  • Building ChatBot
  • 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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