# Chapter 2 - Compression Techniques “I have made this longer than usual because I have not had time to make it shorter.” Blaise Pascal In the last chapter, we discussed a few ideas to improve the deep deep learning efficiency. Now, we will elaborate on one of those ideas, the compression techniques. Compression techniques aim to reduce the model footprint (size, latency, memory etc.). We can reduce chapter, we introduce Quantization, a model compression technique that addresses both these issues. We'll start with a gentle introduction to the idea of compression. Details of quantization and its applications

# Advanced Compression Techniques “The problem is that we attempt to solve the simplest questions cleverly, thereby rendering them unusually complex. One should seek the simple solution.” — Anton Pavlovich Pavlovich Chekhov In this chapter, we will discuss two advanced compression techniques. By ‘advanced’ we mean that these techniques are slightly more involved than quantization (as discussed in the second quality of our models. Did we get you excited yet? Let's learn about these techniques together! ## Model Compression Using Sparsity Sparsity or Pruning refers to the technique of removing (pruning)

## Back to Basics: Debugging Techniques Bob Steagall CppCon 2021 KEWB COMPUTING ## The Cost of Software Failures ## • January 2018, Tricentis’ Software Fail Watch documents 606 software failures in defect occurred – the context • Steps necessary to reproduce the defect – the analogous context • Techniques and tools used to localize the defect • The defect's category • The underlying root cause

## +21 ## Compile-Time Compression and Resource Generation with C++20 ## ASHLEY ROLL 20 21 October 24-29 ## I ntroduction Explore how C++20's constexper features can: • Generate data from code Descriptors ## Along the way • Introduce some libraries I created for this code • Discuss some techniques I found building compile-time libraries ## constexpr in Brief • Specifies a variable or function 3 ## String Compression Lets make a compressed string table https://github.com/AshleyRoll/squeeze • map from enum Key to Compressed String • Huffman Coding for compression • Output struct:

# Chapter 3 - Learning Techniques "The more that you read, the more things you will know. The more that you learn, the more places you'll go." — Dr. Seuss Model quality is an important benchmark translation accuracy would garner better consumer support. In this chapter, our focus will be on the techniques that enable us to achieve our quality goals. High quality models have an additional benefit in first chapter, we briefly introduced learning techniques such as regularization, dropout, data augmentation, and distillation to improve quality. These techniques can boost metrics like accuracy, precision

thread: Techniques for moving work to background threads Anthony Williams Just Software Solutions Ltd https://www.justsoftwareresolutions.co.uk September 2020 ## Get off my thread: Techniques for moving

Background • What is data building? • Differences from Game Code • Assumptions and Concepts 2. Techniques • Keep Threads Busy • 3D Caching • Optimise Sorting • Avoid Blocking Threads 3. Questions Background What data building is Differences from normal game code Concepts used in presentation Techniques I've used to optimise the data building system Time for questions at end Numbers at bottom C++20 (GameDev is stuck here) Speaker notes Quick detour - assumptions about data build system Techniques for running on single computer - many threads - not about clusters Built for async processing

in deep learning models. We will also introduce core areas of efficiency techniques (compression techniques, learning techniques, automation, efficient models & layers, infrastructure). Our hope is tools at your disposal to achieve what you want. The subsequent chapters will delve deeper into techniques, infrastructure, and other helpful topics where you can get your hands dirty with practical projects possible classes. This helped with creating a testbed for researchers to experiment with. Along with techniques like Transfer Learning to adapt such models for the real world, and a rapid growth in data collected

# Chapter 6 - Advanced Learning Techniques “Tell me and I forget, teach me and I may remember, involve me and I learn.” – Benjamin Franklin This chapter is a continuation of Chapter 3, where we introduced introduced learning techniques. To recap, learning techniques can help us meet our model quality goals. Techniques like distillation and data augmentation improve the model quality, without increasing the impressive quality with a small number of labels. As we described in chapter 3's 'Learning Techniques and Efficiency' section, labeling of training data is an expensive undertaking. Factoring in

## When Lock-Free Still Isn’t Enough: An Introduction to Wait-Free Programming and Concurrency Techniques DANIEL ANDERSON