Hands-On Convolutional Neural Networks with TensorFlow: Solve computer vision problems with modeling in TensorFlow and Python

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Deep Learning and Convolutional Neural Networks

efore we begin this chapter, we need to talk a bit about AI and machine learning (ML) and how those two components fit together. The term "artificial" refers to something that is not real or natural, whereas "intelligence" refers to something capable of understanding, learning, or able to solve problems (and, in extreme cases, being self-aware).

Officially, artificial intelligence research began at the Dartmouth Conference of 1956 where AI and its mission were defined. In the following years, everyone was optimistic as machines were able to solve algebra problems and learn English, and the first robot was constructed in 1972. However in the 1970s, due to overpromising but under delivering, there was a so-called AI winter where AI research was limited and underfunded. After this though AI was reborn through expert...

Key benefits

Learn the fundamentals of Convolutional Neural Networks

Harness Python and Tensorflow to train CNNs

Build scalable deep learning models that can process millions of items

Description

Convolutional Neural Networks (CNN) are one of the most popular architectures used in computer vision apps. This book is an introduction to CNNs through solving real-world problems in deep learning while teaching you their implementation in popular Python library - TensorFlow. By the end of the book, you will be training CNNs in no time! We start with an overview of popular machine learning and deep learning models, and then get you set up with a TensorFlow development environment. This environment is the basis for implementing and training deep learning models in later chapters. Then, you will use Convolutional Neural Networks to work on problems such as image classification, object detection, and semantic segmentation. After that, you will use transfer learning to see how these models can solve other deep learning problems. You will also get a taste of implementing generative models such as autoencoders and generative adversarial networks. Later on, you will see useful tips on machine learning best practices and troubleshooting. Finally, you will learn how to apply your models on large datasets of millions of images.

What you will learn

Train machine learning models with TensorFlow

Create systems that can evolve and scale during their life cycle

Use CNNs in image recognition and classification

Use TensorFlow for building deep learning models

Train popular deep learning models

Fine-tune a neural network to improve the quality of results with transfer learning

Build TensorFlow models that can scale to large datasets and systems

What do you get with Print?

Instant access to your digital copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Redeem a companion digital copy on all Print orders

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

Frequently bought together

Hands-On Deep Learning for Images with TensorFlow

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Recurrent Neural Networks with Python Quick Start Guide

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Total $ 70.97

Chris Novitsky Apr 19, 2021

good content, awful formating

Amazon Verified review

Mina Jan 13, 2019

Simple explanation for the concepts.Fast delivery.

M. Glass Apr 26, 2019

This book, although at times has some useful information, has numerous formatting issues that make pages hard to read. its as if no one ready through it completely prior to printing. Also, the chapters feel more like the rough draft, and often lack enough detail to make sense of the subject matter. The authors seemed to have rushed this to print before prematurely

Hands-On Convolutional Neural Networks with TensorFlow: Solve computer vision problems with modeling in TensorFlow and Python

What do you get with Print?

Hands-On Convolutional Neural Networks with TensorFlow

Deep Learning and Convolutional Neural Networks

AI and ML

Artificial neural networks

Convolutional neural networks

Building a CNN model in TensorFlow

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