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TensorFlow Deep Learning Projects: 10 real-world projects on computer vision, machine translation, chatbots, and reinforcement learning by [Alexey Grigorev, Rajalingappaa Shanmugamani, Alberto Boschetti, Luca Massaron, Abhishek Thakur]

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Luca Massaron

Alberto Boschetti

TensorFlow Deep Learning Projects: 10 real-world projects on computer vision, machine translation, chatbots, and reinforcement learning Kindle Edition

by Alexey Grigorev (Author), Rajalingappaa Shanmugamani (Author), Alberto Boschetti (Author), Format: Kindle Edition

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Leverage the power of Tensorflow to design deep learning systems for a variety of real-world scenarios

Key Features

Build efficient deep learning pipelines using the popular Tensorflow framework
Train neural networks such as ConvNets, generative models, and LSTMs
Includes projects related to Computer Vision, stock prediction, chatbots and more

Book Description

TensorFlow is one of the most popular frameworks used for machine learning and, more recently, deep learning. It provides a fast and efficient framework for training different kinds of deep learning models, with very high accuracy. This book is your guide to master deep learning with TensorFlow with the help of 10 real-world projects.

TensorFlow Deep Learning Projects starts with setting up the right TensorFlow environment for deep learning. Learn to train different types of deep learning models using TensorFlow, including Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, and Generative Adversarial Networks. While doing so, you will build end-to-end deep learning solutions to tackle different real-world problems in image processing, recommendation systems, stock prediction, and building chatbots, to name a few. You will also develop systems that perform machine translation, and use reinforcement learning techniques to play games.

By the end of this book, you will have mastered all the concepts of deep learning and their implementation with TensorFlow, and will be able to build and train your own deep learning models with TensorFlow confidently.

What you will learn

Set up the TensorFlow environment for deep learning
Construct your own ConvNets for effective image processing
Use LSTMs for image caption generation
Forecast stock prediction accurately with an LSTM architecture
Learn what semantic matching is by detecting duplicate Quora questions
Set up an AWS instance with TensorFlow to train GANs
Train and set up a chatbot to understand and interpret human input
Build an AI capable of playing a video game by itself –and win it!

Who this book is for

This book is for data scientists, machine learning developers as well as deep learning practitioners, who want to build interesting deep learning projects that leverage the power of Tensorflow. Some understanding of machine learning and deep learning, and familiarity with the TensorFlow framework is all you need to get started with this book.

Recognizing traffic signs using Convnets
Annotating Images with Object Detection API
Caption generation for images
Building GANs for Conditional Image Creation
Stock Price Prediction with LSTM
Create & Train Machine Translation Systems
Train and set up a Chatbot, able to discuss like a human
Detecting Duplicate Quora Questions
Building a TensorFlow Recommender Systems
Video Games by Reinforcement learning

Editorial Reviews

About the Author

Luca Massaron is a data scientist and marketing research director specialized in multivariate statistical analysis, machine learning, and customer insight, with 10+ years experience of solving real-world problems and generating value for stakeholders using reasoning, statistics, data mining, and algorithms. Passionate about everything on data analysis and demonstrating the potentiality of data-driven knowledge discovery to both experts and non-experts, he believes that a lot can be achieved by understanding in simple terms and practicing the essentials of any discipline.

Alberto Boschetti is a data scientist with strong expertise in signal processing and statistics. He holds a PhD in telecommunication engineering and lives and works in London. In his work, he faces daily challenges spanning natural language processing, machine learning, and distributed processing. He is very passionate about his job and always tries to stay up to date on the latest development in data science technologies, attending meetups, conferences, and other events.

Alexey Grigorev is a skilled data scientist, machine learning engineer, and software developer with more than 8 years of professional experience. He started his career as a Java developer working at a number of large and small companies, but after a while he switched to data science. Right now, Alexey works as a data scientist at Simplaex, where, in his day-to-day job, he actively uses Java and Python for data cleaning, data analysis, and modeling. His areas of expertise are machine learning and text mining.

Abhishek Thakur is a data scientist. His focus is mainly on applied machine learning and deep learning, rather than theoretical aspects. He completed his master's in computer science at the University of Bonn in early 2014. Since then, he has worked in various industries, with a research focus on automatic machine learning. He likes taking part in machine learning competitions and has attained a third place in the worldwide rankings on the popular website Kaggle.

Rajalingappaa Shanmugamani is currently a deep learning lead at SAP, Singapore. Previously, he worked and consulted at various startups, developing computer vision products. He has a master's from IIT Madras, his thesis having been based on the applications of computer vision in manufacturing. He has published articles in peer-reviewed journals, and spoken at conferences, and applied for a few patents in machine learning. In his spare time, he coaches programming and machine learning to school students and engineers.

--This text refers to the paperback edition.

Product details

ASIN ‏ : ‎ B077PW98JB
Publisher ‏ : ‎ Packt Publishing; 1st edition (March 28, 2018)
Publication date ‏ : ‎ March 28, 2018
Language ‏ : ‎ English
File size ‏ : ‎ 19054 KB
Text-to-Speech ‏ : ‎ Enabled
Screen Reader ‏ : ‎ Supported
Enhanced typesetting ‏ : ‎ Enabled
X-Ray ‏ : ‎ Not Enabled
Word Wise ‏ : ‎ Not Enabled
Print length ‏ : ‎ 322 pages
Lending ‏ : ‎ Not Enabled

Best Sellers Rank: #2,867,506 in Kindle Store (See Top 100 in Kindle Store)
- #321 in Natural Language Processing (Kindle Store)
- #518 in Neural Networks
- #802 in Natural Language Processing (Books)

Customer Reviews:
8 ratings

About the authors

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Luca Massaron
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Luca Massaron is a data scientist and a research director specialized in multivariate statistical analysis, machine learning and customer insight with over a decade of experience in solving real world problems and in generating value for stakeholders by applying reasoning, statistics, data mining and algorithms. From being a pioneer of Web audience analysis in Italy to achieving the rank of top ten data scientist at competitions held by kaggle.com, he has always been passionate about everything regarding data and analysis and about demonstrating the potentiality of data-driven knowledge discovery to both experts and non-experts. Favouring simplicity over unnecessary sophistication, he believes that a lot can be achieved in data science just by doing the essential.
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Rajalingappaa Shanmugamani
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Rajalingappaa Shanmugamani is currently working as a Engineering Manager for a Deep learning team at Kairos. Previously he worked as a Senior Machine Learning Developer at SAP, Singapore and worked at various startups for developing machine learning products. He has a Masters from Indian Institute of Technology – Madras. He has published articles in peer-reviewed journals and conferences and applied for few patents in the area of machine learning. In his spare time, he coaches programming and machine learning to school students and engineers.
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Alberto Boschetti
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Alberto Boschetti is a data scientist, with strong expertise in signal processing and statistics. He holds a Ph.D. in Telecommunication Engineering and currently lives and works in London. In his work projects he daily faces challenges spanning among natural language processing (NLP), machine learning and probabilistic graph models. He is very passionate about his job and he always tries to be updated on the latest development of data science technologies, attending meetups, conferences and other events.
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Abhishek Thakur
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Abhishek Thakur is a data scientist and world's first 4x grandmaster on Kaggle. His passion lies in solving difficult world problems through data science. Abhishek did his Bachelors in Electronics Engineering from India and moved to Germany for pursuing MSc from University of Bonn, Germany with a focus on image processing and computer vision. He dropped out of PhD in 2015 and since then has been working in industries.
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N31LD

Not written for me, and poor Kindle presentation of code

Reviewed in the United States on April 18, 2018

Verified Purchase

Three problems with this book.
(1) The Kindle version does not render the code well. The code wraps and indentation does not hold up as the page width varies, so it's impossible to tell whether a line of code belongs to one level of indentation or another. Sure, I can download the code from the repo and look at it in Jupyter, but what's the use of the book about code if you can't read the code?
(2) The text between the code chunks is frequently trivial, and in many cases should have just been comments in the code. e.g. "And finally, here's the code for training the model with minibatches:"
(3) The book assumes a moderately high level of understanding of TensorFlow. Do not buy this book unless you already have a strong understanding of terms like "dropout", "flattenizer", "softmax", and "leaky ReLU activation". Terms like these are used continuously without explanation. If you don't already understand them, this book will not help you understand TensorFlow.

Given (3), I have to wonder who the target audience is for this book - presumably if the reader already understands TensorFlow they will have access to code examples for different types of project. If the reader has data science skills but does not understand TensorFlow, it's not helpful. This book missed out on an important niche of helping data scientists learn TensorFlow through practical projects.