Table of Contents

Today weâre looking at the best TensorFlow books for 2021.

**What is TensorFlow?**

TensorFlow is Googleâs free and open-source machine learning library. Itâs used for large-scale machine learning.

In addition, it simplifies computations by visualizing them as graphs.

It has plenty of real-world uses such as seeking out new planets.

Youâll find that TensorFlow aides in:

- classification
- discovering
- prediction
- creation

And beyond.

So with our list of TensorFlow books, you can start learning **today**.

Weâve also included a couple TensorFlow courses you might be interested in.

*This post contains affiliate links. I may receive compensation if you buy something. Read myÂ disclosureÂ for more details.*

**TLDR: Best TensorFlow Books for 2021****đ„ Best Overall đ„****Deep Learning with TensorFlow 2 and Keras****đ„ Best for Newbies đ„****Machine Learning with TensorFlow****đž Best Value đž****Machine Learning Using TensorFlow Cookbook**

**TensorFlow Books**

**1. Practical Deep Learning for Cloud, Mobile, and Edge**

đš **Ideal for:** intermediate Python developers

đ„ **Major topics:** deploy computer vision models, build scalable applications, develop artificial intelligence

**Practical Deep Learning for Cloud, Mobile and Edge** by Anirudh Koul, Siddha Ganju and Meher Kasam is featured on the official Keras website.

đĄ Keras is a deep learning API written in Python that runs on top of TensorFlow.

In this step-by-step guide, youâll learn how to build deep learning applications for:

- the cloud
- mobile
- browsers
- edge devices

Using a hands-on approach, youâll learn how to build scalable, creative and useful applications.

For example, youâll learn how to train, tune and deploy computer vision models using TensorFlow, Keras and beyond.

Youâll also learn how to develop artificial intelligence (AI) for devices such as:

- Raspberry Pi
- Jetson Nano
- Google Coral

With over 40 industry case studies, youâll work on fun projects such as simulating an autonomous car in a video game environment.

**Practical Deep Learning with Cloud, Mobile, and Edge** also dispenses over 50 practical tips to maximize debugging and scaling operations.

*Want to build an industry-level machine learning project? Sign up for the course Applied Machine Learning: Industry Case Study with TensorFlow on Educative.io.*

**2. Deep Learning with JavaScript: Neural networks in TensorFlow.js**

đš **Ideal for:** intermediate JavaScript developers

đ„ **Major topics:** text analysis, speech processing, image recognition

**Deep Learning with JavaScript by Shanqing Cai, et al. is one of the best TensorFlow books. **

**Instead of using Python or R programming, youâll use JavaScript to build deep learning apps.**

Because JavaScript is the language of the web, this is a good book for developers who want to explore TensorFlow.

**Itâs written by the main authors of the TensorFlow library.**

Youâll find plenty of case studies and in-depth instructions for deep learning apps using JavaScript or Node.

And youâll learn how use TensorFlow to build deep learning models that run directly in the browser.

In this fast-paced book, youâll learn about:

- text analysis
- speech processing
- image recognition

In addition, youâll build a self-learning game AI.

After mastering the basics of deep learning, youâll explore more advanced concepts such as image generation.

You should have a strong foundation in JavaScript before reading **Deep Learning with JavaScript**.

**3. Learning TensorFlow.js: Powerful Machine Learning in JavaScript**

đš **Ideal for:** intermediate JavaScript developers

đ„ **Major topics:** neural network architectures, DataFrames, train machine learning data

**Learning TensorFlow.js by Gant Laborde is another one of those TensorFlow books with a strong focus on deep learning using JavaScript.**

Youâll start by exploring the most fundamental structures of machine learning: tensors. Then youâll use real-world examples to convert data into tensors.

This includes going into:

- neural network architectures
- DataFrames
- TensorFlow Hub

And learning more about model conversion and transfer learning.

Youâll also learn how to use TensorFlow.js to combine the web with artificial intelligence.

Then youâll explore resources used to train and manage machine learning data.

By the end of **Learning TensorFlow.js**, youâll be able to start building your own training models.

**4. TensorFlow 2.0 Computer Vision Cookbook**

đš **Ideal for:** intermediate Python developers

đ„ **Major topics:** Keras and dataset APIs, image classification, object detection

**TensorFlow 2.0 Computer Vision Cookbook** (Packt) by Jesus Martinez is intended to help boost the performance of computer vision models.

This is done using machine learning techniques and deep learning algorithms using TensorFlow 2.x.

Youâll learn about recipes to overcome challenges while building computer vision models.

In addition, it will help machines gain a human level of understanding to be able to recognize and analyze images and videos.

đĄ *Computer vision models take uploaded images or videos and predict pre-learned concepts.*

But first, youâll learn about the key features of TensorFlow 2.x. This includes Keras and dataset APIs.

Then youâll learn about common computer vision tasks:

- image classification
- transfer learning
- object detection

And more.

So this might not sound all that interesting, but youâll actually be doing some pretty cool things like enabling machines to recognize peopleâs emotions or predict their age based on pictures.

**You should have a basic understanding of Python programming and computer vision before reading TensorFlow 2.0 Computer Vision Cookbook.**

**5. Deep Learning with TensorFlow 2 and Keras**

đš **Ideal for:** intermediate Python developers

đ„ **Major topics:** regression, recurrent neural networks, natural language processing

**Deep Learning with TensorFlow 2 and Keras by Antonio Gulli, et al. is one of the best TensorFlow books for learning regression, CNNs, GANs, recurrent neural networks (RNNs) and natrual language processing (NLP). **

Unlike some other similar books, it introduces TensorFlow and Keras right out of the gate.

Youâll use them throughout the book while you learn key deep learning and machine learning techniques. Youâll also find extensive code examples.

Then youâll examine the most popular approach to machine learning which is regression analysis.

Youâll come across some fun challenges while applying deep learning to natural human language and producing responses.

Eventually, youâll learn how to write deep learning applications in this powerful, scalable machine learning stack.

In addition, youâll see how TensorFlow 2 provides full integration with Keras.

Finally, youâll learn how to train your models on the cloud and use TensorFlow in real environments.

*Complement this book with the course Build Deep Learning Models with TensorFlow on Codecademy Pro. *

**6. Python Machine Learning, 3rd Edition**

đš **Ideal for:** intermediate Python developers

đ„ **Major topics:** TensorFlow 2, GANs, reinforcement learning

**Python Machine Learning** by Sebastian Raschka and Vahid Mirjalili will teach you machine learning with a heavy focus on theory.

In this third edition, youâll delve deep into machine learning in Python while covering:

- TensorFlow 2
- GANs
- reinforcement learning

And best practices.

This comprehensive guide to deep learning and machine learning is jam-packed with examples, visualizations and explanations.

In addition to following instructions, youâll learn about the principles behind machine learning so you can build your own models.

In addition to TensorFlow 2, youâll learn about the Keras API as well as scikit-learn, a machine learning library.

Youâll also learn about reinforcement learning techniques, GANs and NLP.

By mastering these frameworks and techniques, youâll be able to teach machines to learn from data. In fact, youâll be able to apply machine learning to:

- image classification
- intelligent web applications
- sentiment analysis

And beyond.

**Python Machine Learning** is ideal for programmers with some previous experience in Python.

**7. Machine Learning Using TensorFlow Cookbook**

đš **Ideal for:** intermediate Python developers

đ„ **Major topics:** boosted trees, tabular data, reinforcement learning

### Created by Kaggle masters and Google developers, **Machine Learning using TensorFlow Cookbook is one of the best TensorFlow books. **

Youâll find over 60 recipes for machine learning while using deep learning solutions.

Youâll also master TensorFlow to create machine learning algorithms all while learning about:

- Keras
- boosted trees
- tabular data
- transformers
- reinforcement learning

And more.

đĄ *Tabular data is data thatâs arranged in rows and columns in a table. Theyâre used in research, communication and data analysis.*

Some of the recipes youâll work on cover training models, regression analysis and artificial neural networks.

Then youâll look at real-world implementations of Keras and TensorFlow.

And in the final chapter, youâll take a project to the production level.

By the end of **Machine Learning using TensorFlow Cookbook**, you should be proficient in TensorFlow 2.

**8. Machine Learning with TensorFlow**

đš **Ideal for:** intermediate Python developers

đ„ **Major topics:** classic prediction, deep learning, recurring neural networks

**Machine Learning with TensorFlow** by Nishant Shukla will teach you machine learning concepts as well as TensorFlow using Python programming.

Youâll start by learning the basics:

- classic prediction
- classification
- clustering algorithms

And more.

Then youâll move onto more challenging concepts like the exploration of deep learning. This includes things like looking at recurrent neural networks and reinforcement learning.

In addition to understanding and using neural networks, youâll learn how to visualize algorithms with TensorBoard.

Before reading **Machine Learning with TensorFlow**, you should be familiar with Python programming and algebra.

**đ„ Geenaâs Hot Take**

Thereâs an updated version of **Machine Learning with TensorFlow**. But I (and many others) think that the first edition is the best edition.

There are different authors, different materials covered. The second edition has additional chapters and concepts, but it just doesnât have the same panache as the first edition.

JustâŠ just trust me and stick with the first edition. Youâll get a better understanding of TensorFlow fundamentals.

Keep in mind that they both cover TensorFlow (not TensorFlow 2).

**9. Hands-On Machine Learning with Scikit-Learn, Keras, and ****TensorFlow**

đš **Ideal for:** intermediate Python developers

đ„ **Major topics:** training models, neural networks, deep learning

**Hands-On Machine Learning with Scikit-Learn, Keras and TensorFlow by AurĂ©lien GĂ©ron is one of the best TensorFlow books for learning concepts, tools and techniques.**

Youâll learn all about using TensorFlow with Python with concrete examples.

Youâll learn about everything from linear regression and progressing to neural networks.

But first, youâll learn about the fundamentals of machine learning. Then youâll dive into neural networks and deep learning.

Each chapter contains a series of exercises with detailed solutions.

Throughout **Hands-On Machine Learning**, youâll explore multiple training models including:

- support vector machines
- decision trees
- random forests
- ensemble methods

And more.

Finally, youâll use TensorFlow to train and scale deep neural networks.

You should be familiar with Python programming before taking on **Hands-On Machine Learning**.

**10. Building Machine Learning Pipelines: Automating Model Life Cycles with TensorFlow**

đš **Ideal for:** intermediate Python developers

đ„ **Major topics:** machine learning pipeline, analyze models

**Building Machine Learning Pipelines by Hannes Hapke and Catherine Nelson is one of the best TensorFlow books we could find.**

For starters, it teaches you how to automate a machine learning pipeline using TensorFlow.

In addition, youâll learn how to reduce deployment time from days to minutes using various techniques.

Then youâll learn how to use tools like Apache Beam, Apache Airflow and Kubeflow.

After that, youâll learn how to analyze models.

Finally, youâll explore machine learning techniques that maintain privacy.

**11. Python Machine Learning for Beginners**

đš **Ideal for:** Python developers new to machine learning

đ„ **Major topics:** data visualization, machine learning, statistical models

**Python Machine Learning for Beginners by AI Publishing is one of the best TensorFlow books for beginners in Python machine learning.**

In addition to learning TensorFlow, youâll also learn about useful data visualization tools like:

But first youâll start with a Python crash course.

After that, youâll learn about data analysis and data visualization.

In the second half of the book, youâll spend time on machine learning and statistical models for data science.

Each chapter presents the theoretical frameworks for different data science and machine learning techniques.

And from there, youâll find practical examples and illustrations.

Youâll also gain instant access to online references, PDFs and exercises on the publisherâs website.

**Best TensorFlow Books: Conclusion**

**Today we looked at the best TensorFlow books of this year:**

**đ„ Best Overall đ„****Deep Learning with TensorFlow 2 and Keras**

**đ„ Best for Newbies đ„****Machine Learning with TensorFlow**

**đž Best Value đž****Machine Learning Using TensorFlow Cookbook**

So whether youâre looking for quality, value, or newbie-friendliness, we think there are TensorFlow books for every developer.

**Up Next:**

- Best Machine Learning Courses for Beginners in 2021 [Bonus: Intermediate and Advanced Machine Learning Courses]
- 6 Best Machine Learning Courses and Specializations [Includes Andrew Ng Stanford Course!]
- Is Grokking the Machine Learning Interview by Educative Worth It? [Machine Learning Interview Preparation]
- 9 Best Data Science Courses for Beginners [+4 Data Science Learning Paths]
- 18 Best Python Courses for Beginners [Including Python Learning Paths]

**What are the best TensorFlow books?**We picked the best TensorFlow books based on the following criteria. For best overall, we think Deep Learning with TensorFlow 2 and Keras is the way to go. For newbies, we think Machine Learning with TensorFlow is the best book. And for value, we think Machine Learning Using TensorFlow Cookbook takes the win.

**What is TensorFlow?**TensorFlow is Google's free and open-source machine learning library. It's used for large-scale machine learning. In addition, it simplifies computations by visualizing them as graphs. It has plenty of real-world uses such as seeking out new planets. You'll find that TensorFlow aides in classification, discovering, prediction, creation and beyond.

**Is the book Deep Learning with TensorFlow 2 and Keras worth it?**Deep Learning with TensorFlow 2 and Keras by Antonio Gulli, et al. is one of the best TensorFlow books for learning regression, CNNs, GANs, recurrent neural networks (RNNs) and natrual language processing (NLP). Unlike some other similar books, it introduces TensorFlow and Keras right out of the gate, and you'll use them throughout the book while you learn key deep learning and machine learning techniques. You'll also find extensive code examples to reinforce concepts. Then you'll examine the most popular approach to machine learning which is regression analysis. Plus you'll have some fun challenges while applying deep learning to natural human language and producing responses. Eventually, you'll learn how to write deep learning applications in this powerful, scalable machine learning stack. In addition, you'll see how TensorFlow 2 provides full integration with Keras.

Finally, you'll learn how to train your models on the cloud and use TensorFlow in real environments.