Artificial Neural Networks with TensorFlow 2: ANN Architecture Machine Learning Projects

Author:   Poornachandra Sarang
Publisher:   APress
Edition:   1st ed.
ISBN:  

9781484261491


Pages:   726
Publication Date:   21 November 2020
Format:   Paperback
Availability:   Manufactured on demand   Availability explained
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Artificial Neural Networks with TensorFlow 2: ANN Architecture Machine Learning Projects


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Overview

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 developing machine learning models through applicable projects. This book covers a wide variety of ANN architectures—starting from working with a simple sequential network to advanced CNN, RNN, LSTM, DCGAN, and so on. A full chapter is devoted to each kind of network and each chapter consists of a full project describing the network architecture used, the theory behind that architecture, what data set is used, the pre-processing of data, model training, testing and performance optimizations, and analysis.  This practical approach can either be used from the beginning through to the end or, if you're already familiar with basic ML models, you can dive right into the application that interests you. Line-by-line explanations on major code segments help to fill in the details as you work and the entire project source is available to you online for learning and further experimentation. With Artificial Neural Networks with TensorFlow 2 you'll see just how wide the range of TensorFlow's capabilities are.  What You'll Learn Develop Machine Learning Applications Translate languages using neural networks Compose images with style transfer Who This Book Is For Beginners, practitioners, and hard-cored developers who want to master machine and deep learning with TensorFlow 2. The reader should have working concepts of ML basics and terminologies.

Full Product Details

Author:   Poornachandra Sarang
Publisher:   APress
Imprint:   APress
Edition:   1st ed.
Weight:   1.139kg
ISBN:  

9781484261491


ISBN 10:   1484261496
Pages:   726
Publication Date:   21 November 2020
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

Table of Contents

Chapter 1: TensorFlowIntroduction  What's new in TensorFlow 2 Chapter 2: A Quick Start on TensorFlow  Hello World for TensorFlow using Google Colab     Chapter 3: TensorFlow Keras Integration  tf.kerasImage Classification     Chapter 4: TensorFlow Hub  Transfer Learning TensorFlow Hub and Keras     Chapter 5: Regression  Predicting Continuous Value Output     Chapter 6: Estimators  Solving Classification Problems Using Estimators     Chapter 7: Distributed Training  Describing tf.distribute.Strategy     Chapter 8: Text   Text ClassificationGeneration with RNN     Chapter 9: Language Translation  The seq2seq model for language translation     Chapter 10: Language Understanding  Using Transformer Model     Chapter 11: Image Captioning  Attention-based model for captioning images     Chapter 12: Time Series Forecasting  Using RNNsForecasting a univariate/multivariate time series     Chapter 13: Style Transfer  Composing an image in the style of another image     Chapter 14: Image Generation using GAN  Introduction to GANGenerating images using a DCGAN     Chapter 15: Image Translation  Colorizing B&W images  

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Author Information

Poornachandra Sarang has 30+ years of IT experience and is an experienced author. His work has always focused on state-of-the-art and emerging technologies. He has provided consulting services to—Sun Microsystems, Microsoft, Oracle, and Hewlett-Packard.  He has been a Ph.D. advisor for Computer Science and is currently on a Thesis Advisory Committee for students pursuing Ph.D. in Computer Engineering—setting the course curriculum for both under-graduate and post-graduate courses in Computer Science/Engineering. He has delivered seminars, written articles, and provided consulting recently on Machine Learning and Deep Learning. He maintains a machine learning blog at education.abcom.com.

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