Design Patterns have proven to be useful over several decades and knowledge about them is still very important to design robust, decoupled systems. However, in recent decades a lot of misconceptions have piled up, many based on misunderstandings about software design in general and Design Patterns in particular. This purpose of this talk is to help to separate facts from misconceptions. It explains what software design is, how Design Patterns fit in, and what an idiom is. Also, it addresses the following misconceptions about Design Patterns: – Design Patterns are outdated and have become irrelevant;
– The GoF Design Patterns are nothing but idioms;
– The GoF Design Patterns are limited to object-oriented programming;
– ‘std::make_unique’ is a Design Pattern and helps to adhere to SRP; After this talk, attendees will have a much deeper understanding of the art of software design and about the purpose of Design Patterns. PUBLICATION PERMISSIONS:
CppCon Organizer provided Coding Tech with the permission to republish CppCon tech talks. CREDITS:
CppCon YouTube channel: https://www.youtube.com/user/CppCon https://www.youtube.com/watch?v=edYk82YAaaM

At Bloomberg, we maintain a system that coherently builds and integrates more than 10,000 C++ packages that are maintained independently by thousands of software engineers on hundreds of teams across our Engineering department. In this talk, we will go over the lessons we have learned about maintaining these packages, as well as how package management should interact with third-party libraries, third-party tools, build systems, IDEs, static analysis tools, and refactoring automation. We hope this will start a conversation around the potential requirements for a more complete package management solution in the C++ ecosystem. PUBLICATION PERMISSIONS:
CppCon Organizer provided Coding Tech with the permission to republish CppCon tech talks. CREDITS:
CppCon YouTube channel: https://www.youtube.com/user/CppCon/videos https://www.youtube.com/watch?v=9YYbFWU9tjs

Data Scientists often have large datasets and powerful hardware at their disposal. However, the excitement of fast computation in Python slows against a steep learning curve. This talk will build your confidence and intuition around high performance computing with Python. We step through a complete example while also covering the core concepts so you can generalize to your own work. Description
An example data science pipeline with numpy and pandas
Common heuristics for when to accelerate your code
Quick survey of common approaches
An example data processing pipeline with numpy
How to accelerate on a single machine with Numba
Brief introduction to Numba
Quick comparison to cython
Accelerating our example pipeline with numba
How to distribute on a cluster with Numba and Dask
Brief introduction to Dask
Quick comparison to PySpark, Ray
Accelerating our example pipeline with numba and dask
How to accelerate and distribute with Numba, Dask, and Rapids
Brief introduction to Rapids & GPUs
Quick comparison to other GPU computing methods
Accelerating our example pipeline with numba, dask, and rapids
Conclusion
Review of performance gains
Summary of when to apply each to your project
Where to find hardware and example costs for various pipelines and data volumes PUBLICATION PERMISSIONS:
PyData provided Coding Tech with the permission to republish PyData tech talks. CREDITS:
PyData YouTube channel: https://www.youtube.com/c/PyDataTV/videos https://www.youtube.com/watch?v=HhV6yzKXqFU