The 5 best resources to learn Tensorflow in 2021
Even though the war is still in progress, TensorFlow remains the dominant Deep Learning modeling framework. Indeed, the 2020 O’Reilly survey on AI adoption in the enterprise revealed that more than half of data scientists have TensorFlow as their primary tool in AI-related work. Moreover, according to Hacker News Who’s Hiring Trends, TensorFlow outperforms Pytorch in numbers of job postings by a factor of three.
However, for many years TensorFlow was considered a perfect example of what is a steep learning curve. It was enough to write a few lines of TensorFlow code to discourage many from learning it. As stated by Rachel Thomas from fast.ai: “Using TensorFlow makes me feel like I’m not smart enough to use TensorFlow”. Nevertheless, after 2019 everything changed. TensorFlow 2.0 was released with the inclusion of Keras as its high-level API. This decision flattened the learning curve and opened access for many to take advantage of TensorFlow and join the Deep Learning revolution.
Then, learning TensorFlow in 2021 is not only an excellent investment for your career (you can even obtain an official certification), but it’s also easier than ever!. So, in this post, I will show you the best resources to acquire one of the most wanted Machine Learning skills: mastering TensorFlow!
1. Tensorflow and Keras Oficial Tutorials
Getting Started tutorial, and work though all the examples, are the best way to learn from the official source. These tutorials have been written and documented by Google Engineers and experts. In particular, a big part of Keras documentation has been written by François Cholet, its creator.
2. Tensorflow Developer Prossional Certificate by Coursera
In this four-course Specialization, you’ll explore exciting opportunities for AI applications. Begin by developing an understanding of how to build and train neural networks. Improve a network’s performance using convolutions as you train it to identify real-world images. You’ll teach machines to understand, analyze, and respond to human speech with natural language processing systems. Learn to process text, represent sentences as vectors, and input data to a neural network. You’ll even train an AI to create original poetry!
With over 9000 reviews giving this specialization the note of 4.7, this is one of the most popular and best valuable courses in Coursera. Offered by deeplearning.ai from the famous Andrew Ng and Laurence Moroney from Google Brain. Moreover, this is the official resource to be prepared for the TensorFlow Certification.
Finally, you can continue deepen your knowledge with the TensorFlow: Advanced Techniques Specialization that teach you how to customize and build powerful real-world models for complex scenarios using advanced TensorFlow, and the TensorFlow: Data and Deployment Specialization that teach you how to deploy models inthe real world.
3. Video Tutorials on Youtube Channels
3.1. DeepLizard
As stated in the article Top 5 Free Resources to learn Deep Learning with PyTorch, DeepLizard is a criminally underated channel that has a lot to offer.
3.2. FreeCodeCamp.org
According to its website, Freecodecamp is “a nonprofit community that helps you learn to code by building projects”. During the last few months, its curriculum has incorporated many new courses. In particular, you can take the “Machine Learning with Python Certification” that uses TensorFlow 2.0, takes approximately 300 hours to complete and offers a free certificate.
Additionaly, in its YouTbe Channel, you will find several resources to sharpen your programming skills and improve your career prospects. For example, SQL Tutorial — Full Database Course for Beginners, Docker Tutorial for Beginners — A Full DevOps Course on How to Run Applications in Containers or even How to Find Freelance Jobs. Without a doubt, FreeCodeCamp makes the world a better place :).
4. Introduction to Deep Learning from MIT
The course consists of a series of lectures on the fundamentals of neural networks and their applications to sequence modeling, computer vision, generative models, and reinforcement learning.
According to its authors: “MIT’s official motto is “Mens et Manus” — Mind and Hand — so it’s no coincidence that we, too, are big believers in this philosophy. As the organizers and lecturers for MIT’s Introduction to Deep Learning, we wanted to develop a course that focused on both the conceptual foundation and the practical skills it takes to understand and implement deep learning algorithms.”
Fortunately for us, all the practical skills are developed using TensorFlow, including three interactives software labs that cover the basics of the framework, recurrent neural network models for music generation, computer vision, debiasing facial recognition systems, and deep reinforcement learning.
5. Book: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow.
A Best Seller and currently #1 on amazon for Data Processing with 4.7 stars. It’s an expanded version of the first Edition and incorporated TensorFlow 2.0. By reading this book, you will understand the theory and the practice of Machine Learning using TensorFlow.
https://www.amazon.com/dp/1492032646/
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