Python - Paperspace Blog

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

Are you working on a regression problem and looking for an efficient algorithm to solve your problem? If yes, you must explore gradient boosting regression (or GBR). In this article

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

Whether you're working locally or on the cloud, many machine learning engineers don't have experience actually deploying their models so that they can be used on a global scale. In

Python

NumPy Array Processing With Cython: 1250x Faster

This tutorial will show you how to speed up the processing of NumPy arrays using Cython. By explicitly specifying the data types of variables in Python, Cython can give drastic

Generative Models

Implementing CycleGAN for Age Conversion

If you've been on any sort of social media this year, you've probably seen people uploading a recent picture of themselves right next to another picture of what they'll look

Python

Boosting Python Scripts With Cython

Python might be one of today's most popular programming languages, but it's definitely not the most efficient. In the machine learning world in particular, practitioners sacrifice efficiency for the ease-of-use

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. Part 4 is divided into two sections. In

Series: Gradient Descent with Python

Implementing Gradient Descent in Python, Part 3: Adding a Hidden Layer

Hello again in the series of tutorials for implementing a generic gradient descent (GD) algorithm in Python for optimizing parameters of artificial neural network (ANN) in the backpropagation phase. The

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.