Deep Learning Model Compression Algorithms

Deep Learning Model Compression Algorithms, available at Free, has an average rating of 4.65, with 13 lectures, 6 quizzes, based on 46 reviews, and has 2267 subscribers.

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You will learn about Understand pruning algorithms Understand quantization algorithms Understand distillation algorithms Understand factorization algorithms This course is ideal for individuals who are deep learning model developers or model compression research beginners It is particularly useful for deep learning model developers or model compression research beginners.

Enroll now: Deep Learning Model Compression Algorithms

Summary

Title: Deep Learning Model Compression Algorithms

Price: Free

Average Rating: 4.65

Number of Lectures: 13

Number of Quizzes: 6

Number of Published Lectures: 11

Number of Published Quizzes: 6

Number of Curriculum Items: 19

Number of Published Curriculum Objects: 17

Original Price: Free

Quality Status: approved

Status: Live

What You Will Learn

Understand pruning algorithms

Understand quantization algorithms

Understand distillation algorithms

Understand factorization algorithms

Who Should Attend

deep learning model developers

model compression research beginners

Target Audiences

deep learning model developers

model compression research beginners

This course is intended to provide learners with an in-depth understanding of techniques used in compressing deep learning models. The techniques covered in the course include pruning, quantization, knowledge distillation, and factorization, all of which are essential for anyone working in the field of deep learning, particularly those focused on computer vision and natural language processing. These techniques should be generally applicable to all deep learning models.

One of the primary objectives of this course is to provide advanced content that is updated with the latest algorithms. This includes product quantization and its variants, tensor factorization, and other cutting-edge techniques that are rapidly evolving in the field of deep learning. To ensure learners are equipped with the knowledge they need to succeed in this field, the course will summarize these techniques based on academic papers, while avoiding an emphasis on experiment result details. It’s worth noting that leaderboard results are updated frequently, and new models may require compression. As a result, the course will focus on the technical aspects of these techniques, helping learners understand what happens behind the scenes.

Upon completion of the course, learners will feel confident in their ability to read news, blogs, and academic papers related to model compression. You will be encouraged to apply these techniques to your own work and share the knowledge with others.

Course Curriculum

Chapter 1: Introduction

Lecture 1: A brief introduction about deep learning

Lecture 2: Model compression overview

Lecture 3: Demo CNN quantization in Tensorflow

Chapter 2: Compression Algorithm: pruning

Lecture 1: pruning overview

Lecture 2: Hessian based pruning

Lecture 3: Group Hessian pruning

Lecture 4: Hessian Reuse 1

Lecture 5: Hessian Reuse 2

Chapter 3: Compression Algorithm: quantization

Lecture 1: fixed point quantization

Chapter 4: Compression Algorithm: distillation

Lecture 1: knowledge distillation cross entropy

Chapter 5: Compression Algorithm: factorization

Lecture 1: SVD factorization

Instructors

easy peasy
Machine learning engineer

Rating Distribution

1 stars: 0 votes

2 stars: 5 votes

3 stars: 7 votes

4 stars: 16 votes

5 stars: 18 votes

Frequently Asked Questions

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