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TensorFlow-Machine-Learning-Projects

This is the code repository for TensorFlow Machine Learning Projects, published by Packt.

Checkout the code with the following command:

git clone --recursive [email protected]:PacktPublishing/TensorFlow-Machine-Learning-Projects.git

Build 13 real-world projects with advanced numerical computations using the Python ecosystem

What is this book about?

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.

This book covers the following exciting features:

• 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

If you feel this book is for you, get your copy today!

Instructions and Navigations

All of the code is organized into folders. For example, Chapter02.

The code will look like the following:

import pandas as pd
train = pd.read_csv(os.path.join(dsroot,'exoTrain.csv'))
test = pd.read_csv(os.path.join(dsroot,'exoTest.csv'))
print('Training data\n',train.head())
print('Test data\n',test.head())

Following is what you need for this book: 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

With the following software and hardware list you can run all code files present in the book (Chapter 1-15).

Software and Hardware List

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.

Related products

• Tensorflow Machine Learning Cookbook - Second Edition [Packt] [Amazon]

• Hands-on Artificial Intelligence with TensorFlow [Packt] [Amazon]

Get to Know the Authors

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 deep learning, machine learning, distributed computing, and computational methods and has provided thought leadership 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.

Other books by the authors

Mastering Apache Storm

TensorFlow Machine Learning Projects

TensorFlow 1.x Deep Learning Cookbook

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