NumPy Nuts and Bolts of NumPy Optimization Part 3: Understanding NumPy Internals, Strides, Reshape and Transpose We cover basic mistakes that can lead to unnecessary copying of data and memory allocation in NumPy. We further cover NumPy internals, strides, reshaping, and transpose in detail.

NumPy Nuts and Bolts of NumPy Optimization Part 1: Understanding Vectorization and Broadcasting In Part 1 of our series on writing efficient code with NumPy we cover why loops are slow in Python, and how to replace them with vectorized code. We also dig deep into how broadcasting works, along with a few practical examples.

Python Getting Started with PyPy The Python programming language is an interface that can be implemented in many ways. Some examples include CPython which uses the C language, Jython that is implemented using Java, and

Machine Learning 5 Genetic Algorithm Applications Using PyGAD This tutorial introduces PyGAD, an open-source Python library for implementing the genetic algorithm and training machine learning algorithms. PyGAD supports 19 parameters for customizing the genetic algorithm for various applications.

Machine Learning Implementing Gradient Boosting in Python In this article we'll start with an introduction to gradient boosting for regression problems, what makes it so advantageous, and its different parameters. Then we'll implement the GBR model in Python, use it for prediction, and evaluate it.

Python Cythonizing Genetic Algorithms: 18x Faster In this tutorial we use Cython to reduce the execution time of the genetic algorithm implemented in Python. We've brought down our computational time from 1.46 seconds to a mere 0.08 seconds, so that 1 million generations run in less than 10 seconds with Cython, compared to 180 seconds in Python.

Deep Learning Deploying Deep Learning Models Part 1: Preparing the Model In this tutorial we'll see how you can take your work and give it an audience by deploying your projects on the web

Python NumPy Array Processing With Cython: 1250x Faster Here we see how to speed up NumPy array processing using Cython. By explicitly declaring the "ndarray" data type, your array processing can be 1250x faster.

Generative Models Implementing CycleGAN for Age Conversion In this tutorial we'll train CycleGAN with Keras to generate images which age a subject's face, either forwards or backwards.

Python Boosting Python Scripts With Cython Python might be one of today's most popular languages, but it's definitely not the most efficient. See how Cython can easily boost your Python scripts.

Series: Gradient Descent with Python Implementing Gradient Descent in Python, Part 4: Applying to Any Number of Neurons In this tutorial, we extend our implementation of gradient descent to work with a single hidden layer with any number of neurons.

Series: Gradient Descent with Python Implementing Gradient Descent in Python, Part 3: Adding a Hidden Layer In the third part of this series, the implementation of Part 2 will be extended for allowing the GD algorithm to work with a single hidden layer with 2 neurons.

Gradient New Gradient Python SDK Build out complex end-to-end machine learning pipelines with the new Gradient Python SDK.

Getting Started A practical guide to Deep Learning in 6 months This post will give you a detailed roadmap to learn Deep Learning and will help you get Deep Learning internships and full-time jobs within 6 months.