40+ Best Resources to Learn Tensorflow (YouTube, Courses, Books, etc)- 2024

Best Resources to Learn Tensorflow

Do you want to learn Tensorflow and looking for the Best Resources to Learn Tensorflow?… If yes, you are in the right place. In this article, I have listed all the best resources to learn Tensorflow including Online Courses, Tutorials, Books, and YouTube Videos.

So, give your few minutes and find out the best resources to learn Tensorflow. You can bookmark this article so that you can refer to this article later.

Now without further ado, let’s get started-

Best Resources to Learn Tensorflow

For your convenience, I have created separate tables for each resource. So let’s start with online courses-

Note- If you are reading this article on Mobile, Please slide left for Full Table.

Best Online Courses For Tensorflow

S/NCourse NameRatingTime to Complete
1. Introduction to TensorFlow Lite– Udacity FREE CourseNA2 months
2.Intro to TensorFlow for Deep Learning– Udacity FREE CourseNA2 months
3. TensorFlow in Practice Specialization deeplearning.ai4.7/54 months (5 hours/week)
4.TensorFlow: Data and Deployment Specialization deeplearning.ai4.5/54 months (4 hours/week)
5.Machine Learning with TensorFlow on Google Cloud Platform Specialization–  Google Cloud4.5/5 3 months (6 hours/week)
6.Tensorflow 2.0 | Recurrent Neural Networks, LSTMs, GRUs– Udemy FREE Course4.3/5 1hr 1min
7.Intro to Machine Learning with TensorFlow– Udacity4.7/53 months (10 hrs/week)
8.Advanced Machine Learning with TensorFlow on Google Cloud Platform Specialization-Google Cloud4.5/5 3 months (5 hours/week)
9.Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning– Coursera FREE to Audit Course4.7/5 31 hours
10. Getting started with TensorFlow 2– Coursera FREE to Audit Course4/9/526 hours
11.End-to-End Machine Learning with TensorFlow on GCP– Coursera FREE to Audit Course4.5/5 13 hours
12.Deep Learning with Tensorflow– edX FREE CourseNA5 weeks
13.Tensorflow 2.0: Deep Learning and Artificial Intelligence-Udemy4.6/521 hours

Best Books to learn Tensorflow

S/NBook NameAuthorKey FeaturesBook LInk
1. Hands-On Neural Networks with TensorFlow 2.0Paolo Galeone1. Understand the basics of machine learning and discover the power of neural networks and deep learning
2. Explore the structure of the TensorFlow framework and understand how to transition to TF 2.0
3. Solve any deep learning problem by developing neural network-based solutions using TF 2.0
Buy on Amazon
2.Machine Learning Using TensorFlow CookbookAlexia Audevart, Konrad Banachewicz, Luca Massaron1. Deep Learning solutions from Kaggle Masters and Google Developer Experts
2. Get to grips with the fundamentals including variables, matrices, and data sources
3. Learn advanced techniques to make your algorithms faster and more accurate
Buy on Amazon
3.TensorFlow 2 Reinforcement Learning CookbookPraveen Palanisamy1. Develop and deploy deep reinforcement learning-based solutions to production pipelines, products, and services
2. Explore popular reinforcement learning algorithms such as Q-learning, SARSA, and the actor-critic method
3. Customize and build RL-based applications for performing real-world tasks
Buy on Amazon
4.Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlowAurélien Géron 1. Explore the machine learning landscape, particularly neural nets
2. Use Scikit-Learn to track an example machine-learning project end-to-end
3. Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods
4. Use the Tensor Flow library to build and train neural nets
5. Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning
6. Learn techniques for training and scaling deep neural nets.
Buy on Amazon
5.TensorFlow 2 Pocket ReferenceKC Tung1. Understand best practices in TensorFlow model patterns and ML workflows
2. Use code snippets as templates in building TensorFlow models and workflows
3. Save development time by integrating prebuilt models in TensorFlow Hub
4. Make informed design choices about data ingestion, training paradigms, model saving, and inferencing
5. Address common scenarios such as model design style, data ingestion workflow, model training, and tuning
Buy on Amazon
6.Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2Sebastian Raschka, Vahid Mirjalili1. Third edition of the bestselling, widely acclaimed Python machine learning book
2. Clear and intuitive explanations take you deep into the theory and practice of Python machine learning
3. Fully updated and expanded to cover TensorFlow 2, Generative Adversarial Network models, reinforcement learning, and best practices
Buy on Amazon
7.Learning Deep Learning: Theory and Practice of Neural Networks, Computer Vision, Natural Language Processing, and Transformers Using TensorFlowMagnus Ekman1. Explore and master core concepts: perceptrons, gradient-based learning, sigmoid neurons, and back propagation
2. See how DL frameworks make it easier to develop more complicated and useful neural networks
3. Discover how convolutional neural networks (CNNs) revolutionize image classification and analysis
4. Apply recurrent neural networks (RNNs) and long short-term memory (LSTM) to text and other variable-length sequences
5. Master NLP with sequence-to-sequence networks and the Transformer architecture
6. Build applications for natural language translation and image captioning
Buy on Amazon
8.TinyML: Machine Learning with TensorFlow Lite Pete Warden1 Build a speech recognizer, a camera that detects people, and a magic wand that responds to gestures
2. Work with Arduino and ultra-low-power microcontrollers
3. Learn the essentials of ML and how to train your own models
4. Train models to understand audio, image, and accelerometer data
Buy on Amazon
9.TensorFlow 2.0 Computer Vision CookbookJesús Martíne1. Develop, train, and use deep learning algorithms for computer vision tasks using TensorFlow 2. x
2. Discover practical recipes to overcome various challenges faced while building computer vision models
3. Enable machines to gain a human-level understanding to recognize and analyze digital images and videos
Buy on Amazon
10.TensorFlow for Deep Learning: From Linear Regression to Reinforcement LearningBharath Ramsundar, Reza Bosagh Zadeh1. Learn TensorFlow fundamentals, including how to perform basic computation
2. Build simple learning systems to understand their mathematical foundations
3. Dive into fully connected deep networks used in thousands of applications
4. Turn prototypes into high-quality models with hyperparameter optimization
5. Process images with convolutional neural networks
Buy on Amazon

Best Tensorflow Tutorials

S/NTutorial NameProvider
1. Tensorflow TutorialsTensorflow Org
2. TensorFlow TutorialTutorialsPoint
3. TensorFlow Tutorial For BeginnersDatacamp
4.TensorFlow TutorialjavaTpoint
5.TensorFlow Tutorial for BeginnersGuru99
6.TensorFlow 2 TutorialMachine Learning Mastery
7.TensorFlow Tutorial for BeginnersSimplilearn
8.Tensorflow TutorialsIBM
9.TensorFlow.js Tutorialw3schools

Best Tensorflow Youtube Tutorials

S/NTutorial NameChannel Name
1.TensorFlow 2.0 Complete Course freeCodeCamp.org
2.TensorFlow 2.0 Tutorial For BeginnersSimplilearn
3.TensorFlow 2.0 Tutorial For BeginnersAladdin Persson
4.TensorFlow for Computer Vision CoursefreeCodeCamp.org
5.Deep Learning With Tensorflow 2.0, Keras, and Pythoncodebasics
6.Learn TensorFlow and Deep Learning fundamentals with PythonDaniel Bourke
7.TensorFlow Full CourseEdureka
8.Keras with TensorFlow CoursefreeCodeCamp.org
9.Tensorflow Tutorial for BeginnersIntellipaat
10.TensorFlow 2.0 Tutorial – Full CourseGreat Learning

And here the list ends. I hope these resources will help you to learn and master Tensorflow. I would suggest you bookmark this article for future referrals.

Now, let’s have a look at Tensorflow features-

Why TensorFlow?

I will explain with the help of TensorFlow features. So, these are some important features of Tensorflow-

  • With the help of TensorFlow, we can visualize each and every part of the graph which is not an option while using Numpy or SciKit.
  • Tensorflow is easily trainable on CPU as well as GPU for distributed computing.
  • Tensorflow offers Parallel Neural Network Training. That means you can train multiple neural networks and multiple GPUs. This feature makes the models very efficient on large-scale systems.
  • Tensorflow has a huge team because it is developed by Google.
  • The best part about Tensorflow is that it is open source so anyone can use it as long as they have internet connectivity.
  • Availability of Statistical Distributions such as Bernoulli, Beta, Chi2, Uniform, and Gamma.

These are very few features of Tensorflow that I discussed. Tensorflow has lots of other features too. I hope now you understood the importance of Tensorflow.

Now it’s time to wrap up.


In this article, I tried to cover all the best resources to learn Tensorflow from online courses to YouTube videos. If you have any doubts or questions, feel free to ask me in the comment section.

All the Best!

Enjoy Learning!

Thank YOU!

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– Henry Ford

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