Recommender Systems and Deep Learning in Python is a specialized course to learn about recommender systems in the field of deep learning, machine learning, data science, and AI techniques provided by Udemy . Today, almost all online businesses use the recommender system in different ways. For example, the three biggest websites in the world, namely Google, YouTube, and Facebook, use the recommender system to provide services to users and attract more audiences, and because of this, they have reached their current position.
This course contains very valuable information that shows you how to use the recommender system on different platforms. In this course, the well-known news feed algorithms of Reddit, Hacker News, and Google PageRank are reviewed, and proposed Bayesian techniques used by major media companies are also shown. If you also have products that you sell online or if you write articles on your website, this course will help you to use the recommender system technique on your website and expand your business.
Items taught in this course:
- Understanding and accurately implementing the recommendation system for users using simple algorithms
- Big data matrix factorization on Spark with AWS EC2 cluster
- Matrix factorization in the Keras library
- Deep Neural Networks, Residual Networks, and Autoencoding in Keras
- Finite Boltzmann Machine in Tensorflow
Specifications of the Recommender Systems and Deep Learning in Python course:
- English language
- Duration: 12 hours and 32 minutes
- Number of courses: 92
- Instructor: Lazy Programmer Inc
- File format: mp4
Deep Learning in Python Course topics:
Prerequisites of the Recommender Systems and Deep Learning in Python course:
- For earlier sections, just know some basic arithmetic
- For advanced sections, know calculus, linear algebra, and probability for a deeper understanding
- Be proficient in Python and the Numpy stack (see my free course)
- For the deep learning section, know the basics of using Keras
Deep Learning in Python Pictures
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The version of 2022/10 compared to 2018/12 has increased the number of 9 lessons and the duration of 1 hour and 12 minutes.