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Tuesday, October 12, 2021

Review - Is Coursera's TensorFlow: Advanced Techniques Specialization Worth it?

The market size of the artificial intelligence industry is expected to reach more than $299 billion in the year 2026. Companies are trying to involve this technology in their systems, like the recommendation system on amazon that will recommend products related to your history purchases, which will generate more revenue or self-driving cars that will take the wheel for you, and the examples are endless.

The demand for artificial intelligence engineers is growing rapidly. It outstrips the supply, meaning having these skills in your belt will guarantee a good position with an average salary of around $118k a year,  according to, and maybe even create your own online business that uses this science to solve people's problems like artificial intelligent cameras for detecting guns and fires.

People think they need years in the university to experience this field. Still, actually, it only requires you to understand the Python programming language and some online courses in artificial intelligence. If you are ready to start this career, I recommend enrolling in this TensorFlow: Advanced Techniques Specialization program.

Is Coursera's TensorFlow: Advanced Techniques Specialization really  Worth it?

Now, where do these TensorFlow courses and specializations stand on our review?

1. The Instructors Review

This course was created by the company, one of the leading companies in the AI field, and their instructors have profound knowledge of this industry. The specialization has two instructors named Laurence Moroney, who wrote dozens of programming books and AI. The second one is Eddy Shyu, one of the co-instructors in audacity.

2. Course Content

Now, let's take a look at the content structure and content quality of this Coursera program. In this part, you'll learn what is covered and which online courses are part of this best TensorFlow specialization in Coursera. 

2.1. Custom Models, Layers, and Loss Functions with TensorFlow

First of all, this specialization requires you to know the python language and some experience in TensorFlow and deep learning. 

You will start by comparing the functional API to the Sequential API and how this first one gives you more flexibility in designing your model and learning to build a customer loos function that measures how well your model is and help neural networks learn from the data. 

Later you will start making your custom layer and coding your own custom dense layer and a custom model. Finally, extend the TensorFlow model class to build a ResNet model.

2.2. Custom and Distributed Training with TensorFlow

Starting the course by understanding the fundamentals of building blocks in TensorFlow and learning what tensors work and the mat operations behind these tensors. Next, you will build your own custom training loops in TensorFlow, giving you more flexibility in your model. 

Also, you will learn about graph mode, the benefits of generating code that runs on the graph model, and how to generate them efficiently using TensorFlow tools, so you don’t have to write them yourself.

Finally, you will understand distributed training and use it to process more data and large models faster and use various distributed training.

2.3. Advanced Computer Vision with TensorFlow

This section is all about using TensorFlow in the computer vision industry. You start first understanding the classification & object detection, how they work, and what transfer learning is. Next, you will have an overview of the popular object detection models such as ResNet-50. 

You will use pre-trained models to train your data using them, build your own model for object detection, and use transfer learning to detect localized rubber duckies inside an image.

Later, you will learn about image segmentation that labeling image pixels, and performing much more detailed identification of objects than other object detection techniques. Finally, learn about the model interpretability, which lets you understand how your model arrives at its decisions.

2.4. Generative Deep Learning with TensorFlow

You will understand what style transfer is and use it to extract the content of an image and other kinds of features in images. Next, you will learn about the autoencoders and build them using TensorFlow, see the differences in the results of the DNN and CNN autoencoder models, and build a CNN autoencoder to output a clean image from a noisy one.

Later you will see about the variationally autoencoders to generate entirely new data, and you will generate anime faces using autoencoders. Finally, learn about Gans and who invented them, and build your own Gans to generate faces.


This advanced course about Tensorflow will definitely enhance your skills in this framework and the deep learning field. You can enroll to build more complex models and optimize them by creating custom models and functions.

     Other Coursera Resource Articles you may like

Thanks for reading this article. If you like this review of Coursera's TensorFlow: Advanced Techniques Specialization, then please share them with your friends and colleagues. If you have any questions or feedback, then please drop a note.

P. S. - If you are looking for the best Udemy online courses to learn TensorFlow, you can also check out TensorFlow Developer Certificate in 2023: Zero to Mastery courson Udemy. It's one of the best TensorFlow certification courses on Udemy and is trusted by more than 14K learners.  


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