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TensorFlow Machine Learning Projects: Build 13 real-world projects with advanced numerical computations using the Python ecosystem Kindle Edition
Implement TensorFlow's offerings such as TensorBoard, TensorFlow.js, TensorFlow Probability, and TensorFlow Lite to build smart automation projects
Key Features
- Use machine learning and deep learning principles to build real-world projects
- Get to grips with TensorFlow's impressive range of module offerings
- Implement projects on GANs, reinforcement learning, and capsule network
Book Description
TensorFlow has transformed the way machine learning is perceived. TensorFlow Machine Learning Projects teaches you how to exploit the benefits—simplicity, efficiency, and flexibility—of using TensorFlow in various real-world projects. With the help of this book, you’ll not only learn how to build advanced projects using different datasets but also be able to tackle common challenges using a range of libraries from the TensorFlow ecosystem.
To start with, you’ll get to grips with using TensorFlow for machine learning projects; you’ll explore a wide range of projects using TensorForest and TensorBoard for detecting exoplanets, TensorFlow.js for sentiment analysis, and TensorFlow Lite for digit classification.
As you make your way through the book, you’ll build projects in various real-world domains, incorporating natural language processing (NLP), the Gaussian process, autoencoders, recommender systems, and Bayesian neural networks, along with trending areas such as Generative Adversarial Networks (GANs), capsule networks, and reinforcement learning. You’ll learn how to use the TensorFlow on Spark API and GPU-accelerated computing with TensorFlow to detect objects, followed by how to train and develop a recurrent neural network (RNN) model to generate book scripts.
By the end of this book, you’ll have gained the required expertise to build full-fledged machine learning projects at work.
What you will learn
- Understand the TensorFlow ecosystem using various datasets and techniques
- Create recommendation systems for quality product recommendations
- Build projects using CNNs, NLP, and Bayesian neural networks
- Play Pac-Man using deep reinforcement learning
- Deploy scalable TensorFlow-based machine learning systems
- Generate your own book script using RNNs
Who this book is for
TensorFlow Machine Learning Projects is for you if you are a data analyst, data scientist, machine learning professional, or deep learning enthusiast with basic knowledge of TensorFlow. This book is also for you if you want to build end-to-end projects in the machine learning domain using supervised, unsupervised, and reinforcement learning techniques
Table of Contents
- Overview of Tensorflow and Machine Learning
- Using Machine Learning to detect exoplanets in outer space
- Sentiment Analysis in your browser using Tensorflow.js
- Digit Classification using Tensorflow Lite
- Speech to text and topic extraction using NLP
- Predicting Stock Prices using Gaussian Process Regression
- Credit Card Fraud Detection using Autoencoders
- Generating Uncertainty in Traffic Signs Classifier using Bayesian Neural Networks
- Generating Matching Shoe Bags from Shoe Images Using DiscoGANs
- Classifying Clothing Images using Capsule Networks
- Making Quality Product Recommendations Using TensorFlow
- Object detection at a large scale with Tensorflow
- Generating Book Scripts Using LSTMs
- Playing Pacman using Deep Reinforcement Learning
- What is next?
- LanguageEnglish
- PublisherPackt Publishing
- Publication dateNovember 30, 2018
- File size16064 KB
Editorial Reviews
Review
About the Author
Ankit Jain currently works as a senior research scientist at Uber AI Labs, the machine learning research arm of Uber. His work primarily involves the application of deep learning methods to a variety of Uber's problems, ranging from forecasting and food delivery to self-driving cars. Previously, he has worked in a variety of data science roles at the Bank of America, Facebook, and other start-ups. He has been a featured speaker at many of the top AI conferences and universities, including UC Berkeley, O'Reilly AI conference, and others. He has a keen interest in teaching and has mentored over 500 students in AI through various start-ups and bootcamps. He completed his MS at UC Berkeley and his BS at IIT Bombay (India).
Armando Fandango creates AI empowered products by leveraging his expertise in deep learning, machine learning, distributed computing, and computational methods and has provided thought leadership roles as Chief Data Scientist and Director at startups and large enterprises. He has been advising high-tech AI-based startups. Armando has authored books titled Python Data Analysis - Second Edition and Mastering TensorFlow. He has also published research in international journals and conferences.
Amita Kapoor is an Associate Professor at the Department of Electronics, SRCASW, University of Delhi. She has been teaching neural networks for twenty years. During her PhD, she was awarded the prestigious DAAD fellowship, which enabled her to pursue part of her research work at the Karlsruhe Institute of Technology, Germany. She was awarded the Best Presentation Award at the International Conference on Photonics 2008. Being a member of the ACM, IEEE, INNS, and ISBS, she has published more than 40 papers in international journals and conferences. Her research areas include machine learning, AI, neural networks, robotics, and Buddhism and ethics in AI. She has co-authored the book, Tensorflow 1.x Deep Learning Cookbook, by Packt Publishing.
Product details
- ASIN : B07GDHJBDZ
- Publisher : Packt Publishing; 1st edition (November 30, 2018)
- Publication date : November 30, 2018
- Language : English
- File size : 16064 KB
- Text-to-Speech : Enabled
- Screen Reader : Supported
- Enhanced typesetting : Enabled
- X-Ray : Not Enabled
- Word Wise : Not Enabled
- Sticky notes : On Kindle Scribe
- Print length : 324 pages
- Page numbers source ISBN : 1789132215
- Best Sellers Rank: #2,139,355 in Kindle Store (See Top 100 in Kindle Store)
- #467 in Natural Language Processing (Kindle Store)
- #623 in Neural Networks
- #1,088 in Natural Language Processing (Books)
- Customer Reviews:
About the authors
Amita Kapoor, is Associate Professor in the Department of Electronics, SRCASW, University of Delhi and has been actively teaching neural networks and artificial intelligence for the last 20 years. She completed her masters in Electronics in 1996 and PhD in 2011, during PhD she was awarded prestigious DAAD fellowship to pursue a part of her research work in Karlsruhe Institute of Technology, Karlsruhe, Germany. She was awarded best Presentation Award at the Photonics 2008 international conference. She is an active member of ACM, AAAI, IEEE, and INNS. She has co-authored two books. She has more than 40 publications in international journals and conferences. Her present research areas include ML, AI, Deep Reinforcement Learning and Robotics.
Ankit currently works as a Senior Research Scientist at Uber AI Labs, the machine learning research arm of Uber. His work primarily involves the application of Deep Learning methods to a variety of Uber’s problems ranging from forecasting, food delivery to self driving cars. Previously, he has worked in variety of data science roles at Bank of America, Facebook and other startups.
Additionally, he has been a featured speaker in many of the top AI conferences and universities across US including UC Berkeley, OReilly AI conference etc. He completed his MS from UC Berkeley and BS from IIT Bombay (India).
Fun Fact: In his previous life, he used to work on Oil Rigs in drilling operations in middle of oceans before starting in AI/ML.
Dr. Armando creates AI-empowered products by leveraging reinforcement learning, deep learning, and distributed computing. Armando has provided thought leadership in diverse roles at small and large enterprises including Accenture, Nike, Sonobi, and IBM, along with advising high-tech AI-based startups. Armando has authored several books including Mastering TensorFlow, TensorFlow Machine Learning Projects, and Python Data Analysis, and published research in international journals and conferences.
Dr. Armando’s current research and product development interests lie in the areas of reinforcement learning, deep learning, edge-AI, and AI in virtual, augmented, and simulated environments (VR/AR/XR).
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Another thing that stood out to me was that this book discusses Industry relevant problems such as recommender systems, traffic sign classifier which is actively being researched in many companies. So, I can easily take these learnings and use them on my job. Through these examples, introduces tools that will help deploy these model in production.
Another thing I liked is that this book doesn't get into all the mathematical details which is great if your motive is to quickly get going with using Tensorflow in applied settings.
It seems like a great starting point for someone who has heard of Deep Learning or taken a few online courses and would like to get into solving a variety of real-world problems. The book walks through actual code examples and explains many practical aspects of programming.
Reviewed in the United States on March 30, 2019