Code repository for TensorFlow Machine Learning Cookbook by Packt.
PacktPublishing/TensorFlow-Machine-Learning-Cookbook is ranked 16,939 with 564 stars.
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Key parameters and specifications
Key parameters and specifications| Parameter | Value |
|---|
| Language | Python |
|---|
| Category | Tutorials |
|---|
| Subcategory | Tool-specific tutorials |
|---|
| Stars | 564 |
|---|
| Forks | 391 |
|---|
| Contributors | 6 |
|---|
| Created date | 2017-02-14 |
|---|
| Updated date | 2026-09-21 |
|---|
| Stars in 1 day | 0 |
|---|
| Stars in 7 days | 0 |
|---|
| Rank | 16,939 |
|---|
| Previous rank | 16,727 |
|---|
| Rank change | -212 |
|---|
| 7-day stars rank | 15,814 |
|---|
| Previous 7-day stars rank | 15,822 |
|---|
| 7-day stars rank change | 8 |
|---|
| Total stars rank | 17,627 |
|---|
| Previous total stars rank | 17,598 |
|---|
| Total stars rank change | -29 |
|---|
| Country | UK |
|---|
README summary
TensorFlow Machine Learning Cookbook This is the code repository for TensorFlow Machine Learning Cookbook, published by Packt. It contains all the supporting project files necessary to work through the book from start to finish. About the book TensorFlow is an open source software library for Machine Intelligence. The independent recipes in this book will teach you how to use TensorFlow for complex data computations and will let you dig deeper and gain more insights into your data than ever before
. You’ll work through recipes on training models, model evaluation, sentiment analysis, regression analysis, clustering analysis, artificial neural networks, and deep learning – each using Google’s machine learning library TensorFlow. This guide starts with the fundamentals of the TensorFlow library which includes variables, matrices, and various data sources. Moving ahead, you will get hands-on experience with Linear Regression techniques with TensorFlow. The next chapters cover important high-level concepts such as neural networks, CNN, RNN, and NLP. Once you
are familiar and comfortable with the TensorFlow ecosystem, the last chapter will show you how to take it to production. Instructions and Navigations All of the code is organized into folders. Each folder starts with a number followed by the application name. The code will look like the following: Software requirements: Python 3, with the following installed Python libraries: TensorFlow, Numpy, Scikit-Learn, Requests, and Jupyter. It is compatible in all three major operating systems, Mac, Windows, and Linux. It
requires no special hardware to run the scripts. Related Products: Getting Started with TensorFlow [Deep Learning with TensorFlow [Video]]( [Building Machine Learning Systems with TensorFlow [Video]]( Suggestions and Feedback Click here if you have any feedback or suggestions. Download a free PDF If you have already purchased a print or Kindle version of this book, you can get a DRM-free PDF version at no cost. Simply click on the link to claim your free PDF.
Frequently asked questions
How many stars and forks are recorded?
PacktPublishing/TensorFlow-Machine-Learning-Cookbook has 564 stars and 391 forks.
What is the repository rank?
PacktPublishing/TensorFlow-Machine-Learning-Cookbook is ranked 16,939.
What language and category are recorded?
PacktPublishing/TensorFlow-Machine-Learning-Cookbook has 564 stars, uses Python and belongs to Tutorials.
Which repository is closest in rank?
PacktPublishing/TensorFlow-Machine-Learning-Cookbook is compared with audeering/w2v2-how-to, ranked 16,943 with 564 stars.
TensorFlow Machine Learning Cookbook
This is the code repository for TensorFlow Machine Learning Cookbook, published by Packt. It contains all the supporting project files necessary to work through the book from start to finish.
About the book
TensorFlow is an open source software library for Machine Intelligence. The independent recipes in this book will teach you how to use TensorFlow for complex data computations and will let you dig deeper and gain more insights into your data than ever before. You’ll work through recipes on training models, model evaluation, sentiment analysis, regression analysis, clustering analysis, artificial neural networks, and deep learning – each using Google’s machine learning library TensorFlow.
This guide starts with the fundamentals of the TensorFlow library which includes variables, matrices, and various data sources. Moving ahead, you will get hands-on experience with Linear Regression techniques with TensorFlow. The next chapters cover important high-level concepts such as neural networks, CNN, RNN, and NLP.
Once you are familiar and comfortable with the TensorFlow ecosystem, the last chapter will show you how to take it to production.
Instructions and Navigations
All of the code is organized into folders. Each folder starts with a number followed by the application name. For example, Chapter 03.
The code will look like the following:
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from sklearn import datasets
from tensorflow.python.framework import ops
ops.reset_default_graph()
Software requirements:
Python 3, with the following installed Python libraries: TensorFlow, Numpy,
Scikit-Learn, Requests, and Jupyter. It is compatible in all three major operating
systems, Mac, Windows, and Linux. It requires no special hardware to run the
scripts.
Related Products:
Suggestions and Feedback
Click here if you have any feedback or suggestions.
Download a free PDF
If you have already purchased a print or Kindle version of this book, you can get a DRM-free PDF version at no cost.
Simply click on the link to claim your free PDF.
https://packt.link/free-ebook/9781786462169