Hands-on Machine Learning with TensorFlow
•The Course Overview
•Installing TensorFlow Environment
•TensorFlow Basic Syntax
•TensorFlow Graphs
•Variables and Placeholders
•What is Machine Learning?
•Regression from Scratch for 1 Million Data Points – Part 1
•Regression from Scratch for 1 Million Data Points – Part 2
•Housing Price Prediction Model with Estimator API
•Performing Classification Techniques on Pima Indians Diabetes Dataset – Part 1
•Performing Classification Techniques on Pima Indians Diabetes Dataset – Part 2
•Performing Classification Techniques on Pima Indians Diabetes Dataset – Part 3
•Predicting Class of Income on Census Data – Part 1
•Predicting Class of Income on Census Data – Part 2
•Predicting Class of Income on Census Data – Part 3
•Introduction to K-Means Clustering
•Apply K-Means Clustering on the Blob Dataset Part – 1
•Apply K-Means Clustering on the Blob Dataset Part – 2
•What is Deep Learning?
•Part 1 – Data Preprocessing
•Part 2 – Let’s Create the ANN
•Part 3 – Making Predictions and Evaluating Models
•What Is a Convolutional Neural Network?
•Part 1 – Import MNIST Data from TensorFlow
•Part 2 – Create Placeholders and Layers
•Part 3 – Optimize and Run Sessions
•Test your knowledge
•Real-World Machine Learning Projects Using TensorFlow
•Installing and Preparing the Environment
•Installing TensorFlow
•Warming Up Examples
•Model Representation and Gradient Descent
•Problem Statement and Solution
•Model Representation
•Problem Statement
•Problem Solution
•What Is Anomaly Detection?
•Server Computer’s Behavior
•Introduction to Traffic Sign Classifier
•Implementing Traffic Sign Classifier
•Getting Started with TensorFlow for Deep Learning
•Why Is Deep Learning Useful?
•Activation Functions
•Training Neural Networks
•Creating the Training Dataset
•Creating the Models
•Training the Model
•Visualization and Evaluation
•Loading the Dataset
•Defining the Model and Estimator
•Training
•Evaluation and Visualization
•Introduction to CNNs
•Constructing the Classifier – Part One
•Constructing the Classifier – Part Two
•Testing and Results
•Testing the Pre-Trained Model With Object Detection API
•Creating a Cats and Dogs Dataset
•Training Your New Model
•Deploying Your New Model
•Serverless Deep Learning with TensorFlow and AWS Lambda
•What Is Serverless?
•Why Serverless Deep Learning
•Where Serverless Deep Learning Works and Where It Doesn’t Work
•Example Projects That We Will Build During the Course
•Introduction to AWS Lambda Functions
•Creating an AWS Account and Getting Familiar with the Basics
•Creating a “Hello World” AWS Lambda Function
•Introduction to Serverless Framework
•Installation of Serverless Framework
•Deploying AWS Lambda Functions Using Serverless Framework
•General Overview of TensorFlow
•Simple TensorFlow Example
•Repositories for Pretrained TensorFlow Models
•Image Captioning Example
•Architecture of Deploying TensorFlow with AWS Lambda
•General Issues with Deploying Python Libraries on AWS Lambda
•Deploying TensorFlow on AWS Lambda Using Pre-existing Pack
•Deploying TensorFlow Using Serverless Framework
•Introduction to API Gateway Service
•Creating API Gateway Connection to AWS Lambda Using AWS Console
•Creating API Gateway Connection to AWS Lambda Using Serverless Framework
•Example Project – Deep Learning API
•Introduction to AWS Simple Query Service
•Creating AWS SQS Connection to AWS Lambda Using AWS Console
•Creating AWS SQS Connection to AWS Lambda Using Serverless Framework
•Example Project – Deep Learning Pipeline
•Introduction to AWS Step Functions Service
•Creating AWS Step Functions Connection to AWS Lambda Using AWS Console
•Creating AWS Step Functions to AWS Lambda Using Serverless Framework
•Example Project – Deep Learning Workflow
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