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TensorFlow Deep Learning Projects: 10 real-world projects on computer vision, machine translation, chatbots, and reinforcement learning Kindle Edition
Leverage the power of Tensorflow to design deep learning systems for a variety of real-world scenarios
- 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
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.
Table of Contents
- 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
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.
- 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)
- Customer Reviews:
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(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.