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Tensorflow books

Best Books To Master Tensorflow

TensorFlow is one of Google’s most epic inventions, which has reduced the daunting process of implementing machine learning models for testing, training, and dishing out predictions for analysis. Learning about this open-source library will take you to the next level of your programming journey. So without further ado, here are the ten books in no particular order to satisfy your cravings for TensorFlow.

Key Highlights

  • Includes guidance for natural human language and usage of convents in image classifiers
  • Hands-on approach with detailed examples and key points about TensorFlow.
  • Provides a down-to-earth narrative and thorough details about TensorFlow
  • Covers the fundamental topics of TensorFlow ANN, autoencoder, and linear regression
  • Provides a fun and practical approach to learning TinyML with TensorFlow Lite
  • Provides a brilliant take on TensorFlow with concise, precise, and crystal-clear documentation of reading material

10 Must-Read Tensorflow Books

10 Must-Read Tensorflow Books

Sr.no Books Author Published

Rating

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1. Deep Learning with TensorFlow 2 and Keras: Regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API, 2nd Edition Antonio Gulli, Amita Kapoor, Sujit Pal 2019 Amazon: 4.5

Goodreads: 4.5

2. Learn TensorFlow 2.0: Implement Machine Learning and Deep Learning Models with Python  Pramod Singh, Avinash Manure 2019 Amazon:3.3

Goodreads:2.6

3. TensorFlow in 1 Day: Make your own Neural Network  Krishna Rungta 2018 Amazon: 3.5

Goodreads: 3.8

4. Machine Learning with TensorFlow  Nishant Shukla 2018 Amazon:4.4

Goodreads: 3.4

5. TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers Pete Warden, Daniel Situnayake 2020 Amazon: 4.4

Goodreads: 3.95

6. TensorFlow Machine Learning Projects: Build 13 real-world projects with advanced numerical computations using the Python ecosystem Ankit Jain, Armando Fandango, Amita Kapoor  

2018

Amazon 3.9

Goodreads- 3.0

7. Pro Deep Learning with TensorFlow: A Mathematical Approach to Advanced Artificial Intelligence in Python Santanu Pattanayak 2017 Amazon 4.1 Goodreads -3.8
8. Practical Deep Learning for Cloud, Mobile, and Edge: Real-World AI & Computer-Vision Projects Using Python, Keras & TensorFlow Anirudh Koul , Siddha Ganju , Meher Kasam 2019 Amazon 4.6

Goodreads- 4.6

9. Deep Learning: A Practitioner’s Approach Josh Patterson, Adam Gibson 2017 Amazon:4.2

Goodreads: 3.7

10. Building Machine Learning Pipelines: Automating Model Life Cycles with TensorFlow Hannes Hapke, Catherine Nelson 2020 Amazon: 4.4

Goodreads: 3.4

 

Let us look at the Tensorflow Books and see which one best suits your needs:-

1. Deep Learning with TensorFlow 2 and Keras

Author: Antonio Gulli, Amita Kapoor, Sujit Pal

Deep Learning with TensorFlow 2 and Keras

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Book Review

Enter the world of deep learning with the aid of TensorFlow and Keras with this up-and-coming technical cum introductory guide. This version is easier to follow and implementable with much-improvised context.

Key Takeaways from that Book

  • It is ideal for Python developers and machine learning experts as it requires prior knowledge.
  • Further your learnings in neural network foundations with TensorFlow 2.0, word embeddings, and reinforcement learning.
  • Get the most out of automated Google tools, use convents in image classifiers, get deep learning guidance for natural human language, etc.

2. Learn TensorFlow 2.0: Implement Machine Learning and Deep Learning Models with Python

Author: Pramod Singh, Avinash Manure

Learn TensorFlow 2.0

Get this book here

Book Review

Making the exhaustive list of complex APIs in the ever-changing field of TensorFlow is a tough nut to crack, but this book makes it plausible by doing it just right. Proper implementation of code blocks revolved around developers while giving a brief overview of keras.

Key Takeaways from that Book

  • A practical, straightforward, and summarized book for learning new features of TensorFlow 2.0
  • Online documentation of this book’s code scripts is available on GitHub
  • Revisit the TensorFlow API to find NLP models in production, their use in Computer vision, etc.

3. Tensorflow in 1 Day

Author: Krishna Rungta

TensorFlow in 1 Day

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Book Review

Get ready to learn through a hands-on approach, detailed illustrious examples, and critical points about TensorFlow. Unlike the title says, it is near impossible to learn TensorFlow in one day, but the book clears the basic concepts about the topics of AI/ML in a concise and gets to know the topics quickly.

Key Takeaways from that Book

  • Lays the fundamental topics of TensorFlow ANN, autoencoder with TensorFlow, and linear regression case study in a brief yet understandable way.
  • Learn TensorFlow basics, graph visualization, and jupyter notebook tutorial simply and effectively to stay fresh in your memory.

4 Machine Learning with TensorFlow

Author: Nishant Shukla

Machine Learning

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Book Review

The narrative is very down to earth, easy to follow, and complete details about TensorFlow. Ready yourself with the technical know-how to practice the technical code blocks of TensorFlow. The reader wants pre-requisite Python and basic algebra knowledge for an immersive experience.

Key Takeaways from that Book

  • Be blown away by the auto-focus for machine learning described in this book, coupled with topics such as autoencoders, recurrent neural networks, and clustering data.
  • The highlights include seq2seq models, a broad-length introductory lesson on Ml, and utility landscapes, making the book excellent for beginners and budding developers.

5 TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers

Author: Pete Warden , Daniel Situnayake

TinyML

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Book Review

Have a fun and practical approach to learning this new technology of TinyML with TensorFlow lite with the help of very low-cost equipment such as Arduino, Sparkfun, etc. This book can deliver intriguing examples, from learning simple regression models to creating a sine wave function and audio detection to a fully-fledged image classification model.

Key Takeaways from that Book

  • Achieve the working of cool and exciting embedded systems with the help of tinyML.
  • Optimize TensorFlow lite in designing a model architecture, importing dependencies, machine learning technologies, and many more.

6 TensorFlow Machine Learning Projects

Author: Ankit Jain, Armando Fandango, Amita Kapoor

TensorFlow Machine Learning Projects

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Book Review

A comprehensive guide to all ML enthusiasts looking to get their hands dirty with application-oriented programming in TensorFlow. Considered a lively book by many, the reader won’t regret using this book to get in-depth learnings on logistic regression, capsule networks, and real-life problems.

Key Takeaways from that Book

  • Find learning guides on object detection at a large scale with TensorFlow, TensorFlowOnSpark, understanding Bayesian deep learning, etc.
  • Dive deep into content, such as generating book scripts using LSTMs, playing Pacman with deep reinforcement learning, and sentiment analysis in your browser using TensorFlow.js.

7 Pro Deep Learning with TensorFlow

Authors: Santanu Pattanayak

 

Pro Deep Learning

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Book Review

The author brings the book, an immersive guide for budding deep-learning enthusiasts. It includes a profound focus on math and the complex types of different learnings, which proves helpful in learning about the complicated maths behind the backpropagation used in DL models.

Key Takeaways from that Book

  • Provides an excellent section on gradient descent, MLE, recurrent neural networks, etc.
  • Introduces RNN, generative adversarial network, backpropagation on neural networks, and models like LSTM and GRU.

8 Practical Deep Learning for Cloud, Mobile, and Edge

Author: Anirudh Koul , Siddha Ganju , Meher Kasam

Practical Deep Learning for Cloud, Mobile, and Edge

Get this book here

Book Review

Get to know about the inside magic of creating deep learning models that have the power to create the next viral AI app. The book is infused with careful learning about fun and challenging projects to test your skill set and keep you occupied for the joyride it is.

Key Takeaways from that Book

  • Use 50+ practical tools for enhancing model accuracy, which scales to millions of users.
  • Develop computer vision models with Keras, core ML, and TensorFlow Lite and produce results on raspberry pi, jetson nano, etc.

9 Deep Learning: A Practitioner’s Approach

Author: Josh Patterson, Adam Gibson

Deep Learning: A Practitioner's Approach

Get this book here

Book Review

A comprehensive guide for prolific absorption of advanced machine learning topics, mainly ANN, CNN, and various other algorithms. Has a lot of potential in D4LJ APIs meant for practitioners’ use in their work routine.

Key Takeaways from that Book

  • Helpful in getting to know about neural network fundamentals and mapping specific deep networks to the exact problem.
  • Walk through the fundamentals with data types like DataVec, and DL4J in Spark and Hadoop.

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

Author: Hannes Hapke, Catherine Nelson

Building Machine Learning Pipelines

Get this book here

Book Review

A brilliant take on TensorFlow with equal proportions of conciseness, preciseness, and crystal clear documentation of reading material perfectly blended with the practical know-how of pipelines with the help of TFX. Contains the material for both the development and automation of ML pipelines.

Key Takeaways from that Book

Orchestrate the varied TensorFlow models created using tensorflow serving or develop a pipeline using tensorflow extended.

Cover the concepts of data versioning, data preprocessing, and model serving in a robust and scalable methodology.

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