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Deep Learning using Tensorflow and Cloud AI

  /    /  Deep Learning using Tensorflow and Cloud AI

Deep Learning using Tensorflow and Cloud AI

Machine Learning

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

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
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
  • Spinning up Cloud DataLab
  • Experiencing Datalab notebooks
  • Exploring Google Cloud Storage
  • Leveraging relational data with Google Cloud SQL
  • Reading and writing streaming Data with Google BigTable
  • Querying Data from Google BigQuery
  • Making Google API Calls from notebooks
Cloud AI
  • Introduction
  • Why Cloud AI
  • Google Cloud AI Framework
  • Google Cloud AI Layers
Machine Learning APIs
  • 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)
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)

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).


Our course begins with the first step for generating great user experiences: understanding what people do, think, say, and feel. In this module, you’ll learn how to keep an open mind while learning.

Course Information


5 Days

Mode of Delivery

Instructor led/Virtual



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