High-Performance Computing with Python 3.x, published by Packt
This is the code repository for High-Performance Computing with Python 3.x [Video], published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.
Python is a versatile programming language. Many industries are now using Python for high-performance computing projects.
This course will teach you how to use Python on parallel architectures. You'll learn to use the power of NumPy, SciPy, and Cython to speed up computation. Then you will get to grips with optimizing critical parts of the kernel using various tools. You will also learn how to optimize your programmer using Numba. You'll learn how to perform large-scale computations using Dask and implement distributed applications in Python; finally, you'll construct robust and responsive apps using Reactive programming.
By the end, you will have gained a solid knowledge of the most common tools to get you started on HPC with Python.
- Learn backpropagation
- Install and configure Keras
- Understand callbacks and for customizing the process
- Study deep convolutional neural networks
- Recognize CIFAR-10 images with deep learning
To fully benefit from the coverage included in this course, you will need:
This course will help Python Programmers, Data Analysts and aspiring Data Science professionals familiar with basic Python programming to extend their skillset so as to scale their code and improve their code performance.
This course has the following software requirements:
● Python 3.5+ version
● Jupyter Notebook
● Any web browser for running jupyter notebook
● Python packages: NumPy, SciPy, Cython, Numbda, Dask, RxPy, AsyncIO