Cover Image

Artificial neural networks with TensorFlow 2 : ANN architecture machine learning projects / Poornachandra Sarang.

Show other versions (2)

Develop machine learning models across various domains. This book offers a single source that provides comprehensive coverage of the capabilities of TensorFlow 2 through the use of realistic, scenario-based projects. After learning what's new in TensorFlow 2, you'll dive right into develop...

Full description

Saved in:
Bibliographic Details
Online Access: Full Text (via O'Reilly/Safari)
Main Author: Sarang, P. G. (Poornachandra G.)
Format: eBook
Language:English
Published: [Place of publication not identified] : Apress, 2021.
Subjects:
TensorFlow.
Neural networks (Computer science)
Machine learning.
Machine learning
Table of Contents:
  • Intro
  • Table of Contents
  • About the Author
  • About the Technical Reviewer
  • Acknowledgments
  • Preface
  • Chapter 1: TensorFlow Jump Start
  • What Is TensorFlow 2.0?
  • TensorFlow 2.x Platform
  • Training
  • Data Preparation
  • Designing Model
  • Distribution Strategy
  • Analysis
  • Model Saving
  • Deployment
  • What TensorFlow 2.x Offers?
  • The tf.keras in TensorFlow
  • Eager Execution
  • Distribution
  • TensorBoard
  • Vision Kit
  • Voice Kit
  • Edge TPU
  • Pre-trained Models for AIY Kits
  • Data Pipelines
  • Installation
  • Installation
  • Docker Installation
  • No Installation
  • Testing
  • Summary
  • Chapter 2: A Closer Look at TensorFlow
  • A Trivial Machine Learning Application
  • Creating Colab Notebook
  • Imports
  • Importing TensorFlow 2.x
  • Importing numpy
  • Setting Up Data
  • Defining Neural Network
  • Compiling Model
  • Training Network
  • Examining Training Output
  • Predicting
  • Full Source Code
  • Binary Classification in TensorFlow
  • Setting Up Project
  • Imports
  • Mounting Google Drive
  • Loading Data
  • Shuffling Data
  • Examining Data
  • Data Preprocessing
  • Checking Nulls
  • Selecting Features and Labels
  • Encoding Categorical Columns
  • Scaling Numerical Values
  • Creating Training and Testing Datasets
  • Defining ANN
  • Compiling Model
  • Model Training
  • Performance Evaluation
  • Predicting on Test Data
  • Confusion Matrix
  • Predicting on Unseen Data
  • Full Source Code
  • Summary
  • Chapter 3: Deep Dive in tf.keras
  • Getting Started
  • Functional API for Model Building
  • Sequential Models
  • Model Subclassing
  • Predefined Layers
  • Custom Layers
  • Saving Models
  • Whole-Model Saving
  • Export to SavedModel Format
  • Saving Architecture
  • Saving Weights
  • Saving to JSON
  • Convolutional Neural Networks
  • Image Classification with CNN
  • Creating Project
  • Image Dataset
  • Loading Dataset
  • Creating Training/Testing Datasets
  • Preparing Data for Model Training
  • Creating Validation Dataset
  • Augmenting Data
  • Model Development
  • Train/Evaluate/Display Function
  • Predict Function
  • Defining Models
  • A Model with 2 Convolutional Layers
  • Model_2 with 4 Convolutional Layers
  • Third Model: 6 Convolutional layers with 32, 64 and 128 filters respectively
  • Fourth Model: Addition of dropout layer
  • Model 5
  • Saving Model
  • Predicting Unseen Images
  • Summary
  • Chapter 4: Transfer Learning
  • Knowledge Transfer
  • TensorFlow Hub
  • Pre-trained Modules
  • Using Modules
  • ImageNet Classifier
  • Setting Up Project
  • Classifier URL
  • Creating Model
  • Preparing Images
  • Loading Label Mappings
  • Displaying Prediction
  • Listing All Classes
  • Result Discussions
  • Dog Breed Classifier
  • Project Description
  • Creating Project
  • Loading Data
  • Setting Up Images and Labels
  • Preprocessing Images
  • Processing Image
  • Associating Labels to Images
  • Creating Data Batches
  • Display Function for Images
  • Selecting Pre-trained Model
  • Defining Model

Similar Items