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Deep Learning CNN: Convolutional Neural Networks with Python

Deep Learning CNN: Convolutional Neural Networks with Python

Deep Learning CNN: Convolutional Neural Networks with Python Use CNN for Image Recognition, Computer vision using TensorFlow & VGGFace2! For Data Science, Machine Learning, and AI


What you'll learn

  • The importance of Convolutional Neural Networks (CNNs) in Data Science.
  • The reasons to shift from hand engineering (classical computer vision) to CNNs.
  • The essential concepts from the absolute beginning with comprehensive unfolding with examples in Python.
  • Practical explanation and live coding with Python.
  • An overview of concepts of Deep Learning theory.
  • Evolutions of CNNs from LeNet (1990s) to MobileNets (2020s).
  • Deep details of CNNs with examples of training CNNs from scratch.
  • TensorFlow (Deep learning framework by Google).
  • The use and applications of state-of-the-art CNNs (with implementations in state-of-the-art framework TensorFlow) that are much more recent and advanced in terms of accuracy and efficiency.
  • The use and applications of state-of-the-art pre-trained CNNs (with implementations in state-of-the-art framework TensorFlow) for transfer learning on your own dataset.
  • Building your own applications for human Face Verification and Neural Style Transfer.


Comprehensive Course Description:

Convolutional Neural Networks (CNNs) are considered as game-changers in the field of computer vision, particularly after AlexNet in 2012. And the good news is CNNs are not restricted to images only. They are everywhere now, ranging from audio processing to more advanced reinforcement learning (i.e., Resnets in AlphaZero). So, the understanding of CNNs becomes almost inevitable in all the fields of Data Science. Even most of the Recurrent Neural Networks rely on CNNs these days. So, keeping all these concerns in parallel, with this course, you can take your career to the next level with an expert grip on the concepts and implementations of CNNs in Data Science.

The course ‘Mastering Convolutional Neural Networks, Theory and Practice in Python, TensorFlow 2.0’ is crafted to reflect the in-demand skills in the marketplace that will help you in mastering the concepts and methodology with regards to Python. The course is:

  • Easy to understand.
  • Exhaustive.
  • Expressive.
  • Practical with live coding.
  • Rich with state-of-the-art and recently discovered CNN models by the champions in this field.

How is this course different?

This course has been designed for beginners. However, we will go far deep gradually.

Also, this course is a quick compilation of all the basics, and it encourages you to press forward and experience more than what you have learned. By the end of every module, you will work on the assigned Homework/tasks/activities, which will evaluate / (further build) your learning based on the previous concepts and methods. Several of these activities will be coding-based to get you up and running with implementations.

Data Science is certainly a rewarding career that not only allows you to solve some of the most interesting problems, but also offers you a handsome salary package. With a core understanding of CNNs, you can back up your business and ensure emerging career growth.

Unlike other courses, this comprehensive course is relatively inexpensive – in fact, you can learn the concepts and methodologies of CNNs with Data Science at a fraction of the cost. Our tutorials are divided into 75+ short HD videos along with detailed code notebooks.

So, get started with the course and embrace yourself with the knowledge that waits for you.

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