Recommender Systems: An Applied Approach using Deep Learning

Recommender Systems: Build a Customized Recommendation System or Recommendation engine using Deep Learning

What you will learn

• Learn the about deep learning of recommender systems

• Learn the about benefits and challenges of deep learning in recommender systems

• Learn about the mechanism of generic deep learning-based approaches for recommender system

• Learn the basic neural network models for recommendations

• Learn the theoretical aspects of neural collaborative filtering and variational auto encoders for collaborative filtering

• Learn the hands-on practice for the implementation of deep learning-based recommender system

• Learn about the implementation of two-tower model and its implementation for development of recommender systems

• Learn the implementation of TensorFlow recommenders for the development of recommender systems

• And much more…


Comprehensive Course Description:

Have you ever thought how YouTube adjust your feed as per your favorite content?

Ever wondered! Why is your Netflix recommending you your favorite TV shows?

Have you ever wanted to build a customized deep learning-based recommender system for yourself?

If Yes! Then this is the course you are looking for.

You might have searched for many relevant courses, but this course is different!

This course is a complete package for the beginners to learn the basics of recommender systems, its applications and building it from scratch by using deep learning with python. Every module has engaging content covering necessary theoretical concepts with a complete practical approach is used in along with brief theoretical concepts. At the end of every module, we assign you a quiz, the solution to the quizzes is also available in the next video.

We will be starting with the theoretical concepts of deep learning recommender systems. Moreover, a very detailed hands-on recommender system by using TensorFlow recommenders is included which will enable you to build recommender systems of your choice.

This complete package will enable you to learn the basic to advance mechanism of developing recommender system by using deep learning with python with the help of TensorFlow recommenders. We’ll be using Python as a programming language in this course, which is the hottest language nowadays if we talk about deep leaning.

This comprehensive course will be your guide to learning how to use the power of Python to evaluate your deep learning-based recommender systems datasets based on user ratings and user choices. Moreover, a practical approach will be adopted to build deep learning-based recommender system by adopting retrieval-based approach based on two tower model.

We’ll learn all the basic and necessary concepts for the applied recommender systems models along with the deep learning models. Moreover, one detailed project has been included in this course to develop a very useful experience for yourselves.

This course is designed for both beginners with some programming experience or even those who know nothing about Data Analysis, Deep Learning or TensorFlow.

This comprehensive course is comparable to other Deep Learning based Recommender Systems using TensorFlow courses that usually cost thousands of dollars, but now you can learn all that information at a fraction of the cost in only one course! With over 2 hours of HD video lectures that are divided into many videos and detailed code notebooks for every address this is one of the most comprehensive courses for Recommender Systems using Deep Learning on Udemy!

Why Should You Enroll in This Course?

The course is crafted to help you understand not only the role and impact of deep learning-based recommender systems in real world applications but it provides a very unique hands on experience on developing complete recommender systems engines for your customized dataset by using a real time project. This straightforward learning by doing course will help you in mastering the concepts and methodology with regards to Python.

This course is:

· Easy to understand.

· Expressive and self-explanatory

· To the point

· Practical with live coding

· A complete package with a project on Amazon product recommendation system using deep learning

· Thorough, covering the most advanced and recently discovered deep learning models by renowned data scientists and AI practitioners

Teaching Is Our Passion:

We focus on creating online tutorials that encourage learning by doing. We aim to provide you with more than a superficial look at practical approach towards building recommender systems using deep learning from the perspective of TenorFlow by implementing two tower approach. For instance, this course has one projects in the final module which will help you to see for yourself via experimentation the practical implementation of deep learning with data analysis on the real-world datasets of Amazon Product. We have worked extra hard to ensure you understand the concepts clearly. We want you to have a sound understanding of the basics before you move onward to the more complex concepts. The course materials that make certain you accomplish all this include high-quality video content, course notes, meaningful course materials, handouts, and evaluation exercises. You can also get in touch with our friendly team in case of any queries.

Course Content:

We’ll teach you how to program with Python, how to use deep learning concepts to develop recommender systems! Here are just a few of the topics that we will be learning:

1. Course Overview

2. Deep Learning for Recommender Systems

▪ Overview of Deep Learning in Recommendation Systems

▪ Benefits and Challenges of Deep Learning in Recommender Systems

▪ Deep Learning for Recommendation Inference

▪ A Generic Deep Learning based Recommendation Approach

▪ Neutral Collaborative Filtering

3. Project: Amazon Product Recommendation System

▪ Packages Installation

▪ Data Analysis for Products Recommendation

▪ Data Preparation

▪ Model Development using Two-tower Approach

▪ Implementing TensorFlow Recommenders

▪ Fitting and Evaluation or Recommender System

▪ Validation of Recommender System

▪ Testing a Recommender Model

▪ Making Predictions using Recommender Systems

Enroll in the course and become a recommender systems expert today!

After completing this course successfully, you will be able to:

· Relate the concepts and theories for recommender systems in various domains

· Understand and implement deep learning models for building real world recommendation systems

· Understand evaluate the deep learning based recommender systems

Who this course is for:

· People who want to advance their skills in applied deep learning

· People who want to master relation of data analysis with deep learning

· People who want to build customized recommender systems for their applications

· People who want to implement deep learning algorithms for recommender systems

· Individuals who are passionate about recommender systems specially with the help of TensorFlow Recommenders

· Deep Learning Practitioners

· Research Scholars

· Data Scientists




Course Outline
Your Honest Review
Links for the Course’s Materials and Codes

Deep Learning Foundation for Recommender Systems

Links for the Course’s Materials and Codes
Module Introduction
Deep Learning in Recommendation Systems
Inference After Training
Inference Mechanism
Embeddings and User Context
Neutral Collaborative Filtering
VAE Collaborative Filtering
Strengths and Weaknesses of DL Models
Deep Learning Quiz
Deep Learning Quiz Solution

Project Amazon Product Recommendation System

Links for the Course’s Materials and Codes
Module Overview
TensorFlow Recommenders
Two Tower Model
Project Overview
Download Libraries
Data Visualization with WordCloud
Make Tensors from DataFrame
Rating Our Data
Random Train-Test Split
Making the Model and Query Tower
Candidate Tower and Retrieval System
Compute Loss
Train and Validation
Accuracy vs Recommendations
Making Recommendations

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