Top 10 Books on TensorFlow to Learn TensorFlow
TensorFlow is an essential tool for machine learning and its implementation. This platform requires immense knowledge for its usage in developing systems. There are various books available that teach TensorFlow from scratch to the expertise stage. The following are some of the top books that can be considered for learning TensorFlow.
1. Python for Data Analysis written by Leon Miller
This book helps to learn machine learning along with libraries such as Keras, SciKit, and TensorFlow. It teaches the basics of Python through real-time project building. It’s a high recommendation for those who aspire to learn data analysis along with machine learning.
2. TensorFlow tutorial for experts by Randy Moore
It focuses mainly on TensorFlow and highlights its importance. The book illustrates the functioning of the low-level toolkit. It tailors the method of playing around the graphs to train a model. The book also deals with the CIFAR10 model, along with its implementation and launching. It contains the interview questions from the basic to advance level for checking the grasp on the subject.
3. Learn TensorFlow 2.0 by Pramod Singh, Avinash Manure
The book focuses on the implementation of deep learning and machine learning with the help of the TensorFlow framework. It also explains the updations in version 2.0 and how it works. The book sets an outstanding effect with the help of considerable examples and methods it uses.
4. Deep Learning Pipeline, written by Hisham El-Amir, Mahmoud Hamdy
The book aims at building a deep learning model with TensorFlow. It notes all the approaches that TensorFlow uses in modern times. It teaches the process of building TensorFlow projects and the concept of pipelining. This book seems perfect for those who want to enhance their existing knowledge.
5. Machine Learning with TensorFlow.js by Kai Sasaki
6. Deep Learning with Python by Daniel Geron
This book is the Crash Course for Beginners to Learn the Basics of Deep Learning with Python Using TensorFlow, Keras, and PyTorch. It helps beginners who aspire to develop their systems with guidance and examples. It answers every question of reasoning why Python is the appropriate language for the desired system.
7. Deep learning with TensorFlow 2 and Keras written by Antonio Gulli, Amita Kapoor, Sujit Pal
It centers on Regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API, 2nd Edition. This book introduces the different functions used to build machine learning and deep learning. It helps to learn the concepts and implement them in real-time.
8. Practical Deep Learning for Cloud and Mobile by Anirudh Koul, Siddha Ganju, Meher Kasam- Hands-On Computer Vision Projects Using Python, Keras & TensorFlow
It teaches the concepts with transfer learning and focuses on clearing the practical skills of building a model, debugging it, and checking its operability. The book aids learning for developing applications for different hardware and OS. It garnishes the practical knowledge in the field of deep learning.
9. Mastering Machine Learning with AWS written by Saket Mengle, Maximo Gurmendez
It highlights Advanced machine learning in Python using SageMaker, Apache Spark, and TensorFlow, through AWS. It uses artificial Intelligence workflows on cloud services to develop data-driven products and how to cluster data through EMS and integrate with SageMaker.
10. Hands-on Neural Networks by Leonardo De Marchi, Laura Mitchell
It’s based on learning how to build and train your first neural network model using Python. It trains to develop neural networks using artificial intelligence on OpenAI Gym, TensorFlow, Keras. It supervises transfer learning concepts using Keras and VGG, LSTM, and NLP.