What Is Programming? And How To Get Started
January 28, 2025
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Instructor: Packt - Course Instructors
Included with
Recommended experience
Intermediate level
A basic understanding of machine learning, deep learning, Python, TensorFlow, recommender systems, data handling, and visualization is essential.
Recommended experience
Intermediate level
A basic understanding of machine learning, deep learning, Python, TensorFlow, recommender systems, data handling, and visualization is essential.
Learn about deep learning and recommender systems
Explore the mechanisms of deep learning-based approaches
Learn to implement a two-tower model and TensorFlow for recommender system
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Recommender systems are used in various areas with commonly recognized examples, including playlist generators for video and music services, product recommenders for online stores and social media platforms, and open web content recommenders. Recommender systems have also been developed to explore research articles and experts, collaborators, and financial services.
The course begins with an introduction to deep learning concepts to develop recommender systems and a course overview. The course advances to topics covered, including deep learning for recommender systems, understanding the pros and cons of deep learning, recommendation inference, and deep learning-based recommendation approach. You will then explore neural collaborative filtering and learn how to build a project based on the Amazon Product Recommendation System. You will learn to install the required packages, analyze data for product recommendations, prepare data, and model development using a two-tower approach. You will learn to implement a TensorFlow recommender and test a recommender model. You will make predictions using the built recommender system. Upon completion, you can relate the concepts and theories for recommender systems in various domains and implement deep learning models for building real-world recommendation systems. This course is designed for individuals looking to advance their skills in applied deep learning, understand relationships of data analysis with deep learning, build customized recommender systems for their applications, and implement deep learning algorithms for recommender systems. The prerequisites include a basic to intermediate knowledge of Python and Pandas library.
In this module, we will introduce you to the instructor, providing a brief overview of their background and teaching style. You will also get a comprehensive outline of the course, including the main topics and concepts that will be covered, setting the stage for your learning journey ahead.
2 videos1 reading
In this module, we will delve into the foundational aspects of deep learning as it pertains to recommender systems. You will gain insights into transitioning from machine learning to deep learning, deploying models for inference, and understanding the intricacies of neural and variational autoencoder collaborative filtering. Additionally, you will explore the pros and cons of deep learning models and assess their effectiveness in recommender systems.
11 videos
In this module, we will guide you through creating a project that develops an Amazon product recommendation system. You will learn to use TensorFlow Recommenders, implement the two-tower model, and visualize data with WordCloud. The lessons cover downloading necessary libraries, preparing and rating data, performing train-test splits, and building the model. Finally, we will evaluate model accuracy and generate product recommendations.
15 videos1 assignment
Packt helps tech professionals put software to work by distilling and sharing the working knowledge of their peers. Packt is an established global technical learning content provider, founded in Birmingham, UK, with over twenty years of experience delivering premium, rich content from groundbreaking authors on a wide range of emerging and popular technologies.
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University of Minnesota
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Sungkyunkwan University
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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.
If you complete the course successfully, your electronic Course Certificate will be added to your Accomplishments page - from there, you can print your Course Certificate or add it to your LinkedIn profile.
This course is one of a few offered on Coursera that are currently available only to learners who have paid or received financial aid, when available.
You will be eligible for a full refund until two weeks after your payment date, or (for courses that have just launched) until two weeks after the first session of the course begins, whichever is later. You cannot receive a refund once you’ve earned a Course Certificate, even if you complete the course within the two-week refund period. See our full refund policy.
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
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