Download Deep Learning Recommendation Algorithms with Python

What you would learn in Deep Learning Recommendation Algorithms with Python course?

We'll go over tried and tested recommendations based on collaborative filtering based on the neighborhood and move towards more advanced techniques such as matrix factorization and deep learning using artificial neural networks. Through the course, you'll benefit from our vast industry experience to better understand the challenges you'll face when using these algorithms on a large scale and using real-world data.

There are automated recommendations all over Netflix's homepage and on YouTube as well as on Amazon because these machine-learning algorithms analyze your individual preferences and suggest the best content or products for you as a person. These techniques are now integral to the biggest and most well-known tech companies available, and by knowing how they function and work, you'll be a great asset to them.

We'll look at tried and trusted recommendations based on community-based collaborative filtering. We'll also progress to more sophisticated techniques such as matrix factorization and deep learning using artificial neural networks.

Recommender systems can be complicated; don't enroll in this class expecting to be able to code in a learn-to-code kind or format. There's no recipe to follow to build a recommender system. You must know the various algorithms and decide the best one for a specific scenario. It is assumed that you already can code.

But, this course is highly hands-on as you'll build your framework to evaluate and combine a variety of recommendation algorithms and build the neural network of your choice with Tensorflowto create recommendations from real-world movie reviews from real-world users.

This extensive course will take you through the beginning of collaborative filtering, through cutting-edge applications using deep neural networks and advanced machine learning techniques for recommending the top products for each user.

The programming exercises used in this course utilize this Python programming language. The course includes an introduction to Python for those who are new to the language, but you'll need some previous programming experience for this course effectively. We also provide a brief introduction to deep learning if you're brand new to artificial intelligence. However, you'll need to be able to comprehend the latest algorithms for computers.

Content of the Course

• Create a framework for testing recommendations algorithms and testing them using Python
• Learn solutions to common problems by utilizing large-scale recommendation systems.
• Develop recommendations with deep learning on a massive scale
• Use the appropriate measurements to determine the success of a recommendation system

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