Recommender Systems Complete Course Beginner to Advanced

Overview

Class Central Tips

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. Move beyond basics with modern RNN units like GRU and LSTM, mastering their application in complex signal prediction and overcoming long-distance dependency issues. Learn the intricacies of RNN architecture and prepare to tackle more challenging tasks such as image classification and stock return predictions. The course emphasizes practical coding exercises, ensuring you can confidently implement these techniques in real-world scenarios. Finally, explore natural language processing (NLP) applications, including embeddings, text preprocessing, and text classification using LSTMs. This course is structured to provide a thorough understanding of RNNs, empowering you to apply these deep learning models effectively in various domains. This course is perfect for developers, data scientists, and tech enthusiasts who want to learn how to build and implement recommender systems. Basic knowledge of Python and machine learning concepts is recommended but not required.

Syllabus

Introduction

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.

Recommender Systems with Machine Learning

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.

Deep Learning for Recommender Systems: An Applied Approach

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.