One of the popular open-source libraries in machine learning, TensorFlow provides a suitable abode with essential tools for ML researchers and developers in order to perform SOTA machine learning applications. According to a survey, this library is one of the most loved deep learning frameworks.

In this article, we list down 10 free resources to learn TensorFlow in 2020.

Note: The list is in alphabetical order

1| Advanced ML with TensorFlow on Google Cloud Platform Specialization 

About: Advanced Machine Learning (ML) with TensorFlow on Google Cloud Platform Specialization is a course in Coursera offered by Google Cloud. This course is a little advanced for beginners and is meant for those who already entered the machine learning arena. In this course, one can learn the hands-on experience in optimising, deploying, and scaling production ML models of various types. One will also learn how to build scalable, accurate, and production-ready models for structured data, image data, time-series, and natural language text.

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2| Deep Learning With TensorFlow

About: This free course is provided by the tech giant IBM where one can learn the basic concepts of TensorFlow like starting with a simple “Hello World” example, its main functions, operations, and the execution pipeline. One can also learn how to explain foundational TensorFlow concepts such as the main functions, operations and execution pipelines. It also helps in understanding how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. Additionally, it helps in understanding how to apply TensorFlow for backpropagation to tune the weights and biases while the neural networks are being trained and other such.

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3| Deep Learning with TensorFlow 2 and Keras – Notebooks

About: The course Deep Learning with TensorFlow 2 and Keras mainly focuses on hands-on exercises. It contains the exercises and their solutions in the form of Jupyter notebooks. To learn this course one needs to have enough knowledge in Python and its libraries such as NumPy, Matplotlib, Jupyter, and TensorFlow. Also, this course requires Python 3.5 or Python 3.6.

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4| Introduction to TensorFlow For AI, ML and Deep Learning

About: This course in Coursera is offered by deeplearning.ai where one can learn the best practices for using TensorFlow. One will also get to learn to build a basic neural network in TensorFlow, training of a neural network for a computer vision application and understanding how to use convolutions in order to improve the neural network.

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5| Intro to TensorFlow for Deep Learning by TensorFlow

About: Intro to TensorFlow for Deep Learning by TensorFlow is a free course in Udacity where one can learn how to build deep learning applications with TensorFlow. This course is basically a practical approach to deep learning for software developers. Here, one can learn hands-on experience building the state-of-the-art image classifiers and other deep learning models.

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6| Introduction to TensorFlow Lite by TensorFlow Lite

About: Introduction to TensorFlow Lite by TensorFlow Lite is a free course in Udacity offered by the Tensorflow Lite team. This course is basically a practical approach to model deployment for software developers. In this course, one can learn hands-on experience with the TensorFlow Lite framework, along with deploying deep learning models on Android, iOS, and even an embedded Linux platform.  

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7| Learning TensorFlow

About: Learning TensorFlow is an e-book by Tom Hope, Yehezkel S. Resheff & Itay Lieder. In this book, one can learn how to run TensorFlow, how to use it to build deep learning models, and how to train deep learning models for computer vision, and natural language processing (NLP). It will also help the students to learn how to scale TensorFlow and use clusters to distribute model training and much more.

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8| Machine Learning with TensorFlow on Google Cloud Platform Specialization

About: This course “Machine Learning with TensorFlow on Google Cloud Platform Specialization” is offered by Google Cloud in Coursera. In this course, one can learn the basics of machine learning and the problems it can solve. One will also learn how to write distributed machine learning models that scale in Tensorflow. It also helps the student in scaling out the training of those models by offer high-performance predictions and other such.

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9| TensorFlow Tutorial By Stanford

About: This free tutorial is provided by Stanford University in Github. In this course, one can learn the fundamentals and contemporary usage of the Tensorflow library for deep learning research. It also helps in understanding the graphical computational model of TensorFlow and helps in exploring the functions it has to offer. It also teaches how to build and structure models best suited for a deep learning project and much more.

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10| TensorFlow Tutorials

About: This tutorial is provided by the TensorFlow team on their official website. The tutorials are written as Jupyter notebooks and run directly in Google Colab. It is beneficial for both the beginners and advanced practitioners as one can start learning from the very basic to advanced customisation, as well as distributed training.

Click here to learn.