Chapter 1: Profiling

• What are the ways to profile your code?

• What insights can I gain from a profile?

Chapter 2: Understanding Performant Programming

• How does a computer work and how does that affect my code performance?

Chapter 3: Lists and Tuples

• How do lists and tuples work?

• What are the performance implications of this?

Chapter 4: Dictionaries and Sets

• How do dictionaries and sets work?

• What are the performance implications of this?

Chapter 5: Iterators

• How do iterators work?

• How can I use them to reduce memory in my code?

• How can I use them to reduce the CPU operations needed in my code?

Chapter 6: Matrix and Vector Computation

• How does the CPU perform matrix/vector operations?

• What are the ways I can profile the efficiency of these operations?

• How can I speed up code given a profile?

Chapter 7: Compiling to C

• What are the automated ways to compile my python to C?

• What are the manual ways to compile my python to C?

• How can I use these technologies to speed up my code and not slow down development?

Chapter 8: Concurrency

• How does concurrency work and how does python support it?

• How can I speed up multiple IO operations?

• How can I hide IO wait?

Chapter 9: Multiprocessing

• What is multiprocessing?

• How can I speed up my code on a multiple CPU machine?

• What are the subtleties of muli-CPU code?

Chapter 10: Clusters and Job Queues

• How can I extend my code to a cluster?

• What are some of the pain-points with clusters?

• What is a queue and how is it useful?

Chapter 11: Using Less Ram

• How can I use algorithms to reduce the RAM usage of my code?

• What are tries and probabilistic data structures?

• https://subscription.packtpub.com/video/application-development/9781789956252/101415/101437/introduction-to-cython