Dive into the world of Recurrent Neural Networks (RNNs) with this in-depth course designed to equip you with essential knowledge and hands-on skills using TensorFlow. Start with an introduction to the core concepts of sequence data and time series forecasting, then progress to understanding and implementing autoregressive linear models. Discover how to apply simple RNNs to solve many-to-one and many-to-many problems, with practical coding sessions in TensorFlow 2.
Recommender Systems Complete Course Beginner to Advanced
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Recommender Systems Complete Course Beginner to Advanced
Instructor: Packt - Course Instructors
Included with 
Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
9 hours to complete
3 weeks at 3 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Identify the fundamental concepts of sequence data and time series forecasting.
Explain the workings of autoregressive linear models and simple RNNs.
Implement GRU and LSTM units for various prediction tasks using TensorFlow.
Differentiate between simple RNNs, GRU, and LSTM units.
Details to know
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September 2024
Assessments
1 assignment
Taught in English
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There are 3 modules in this course
In this module, we will introduce the instructor and provide an overview of the course. You'll learn about the course structure, the key concepts covered, and the differences between machine learning and deep learning recommender systems.
What's included
5 videos1 reading
In this module, we will explore the fundamentals of recommender systems, including their motivations, processes, and goals. You'll learn about different generations of recommender systems, their real-world applications, and the challenges they face. Additionally, this section covers various filtering techniques and their evaluation methods.
What's included
63 videos
In this module, we will delve into the application of deep learning techniques in recommender systems. You'll learn about foundational concepts, inference mechanisms, and different deep learning models, such as neural collaborative filtering and variational autoencoders. This module also includes a project on building an Amazon product recommendation system using TensorFlow.
What's included
26 videos1 assignment
Instructor
Offered by
Recommended if you're interested in Machine Learning
University of Minnesota
Imperial College London
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Frequently asked questions
Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.
If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.
Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.