Announcement Introducing ML News 📈 ML News (MLN for short) is a community for sharing and discussing all things related to machine learning, deep learning, AI, data science, and the like.
Computer Vision Channel Attention and Squeeze-and-Excitation Networks (SENet) In this article we will cover one of the most influential attention mechanisms proposed in computer vision: channel attention, as seen in Squeeze-and-Excitation Networks (SENet).
Machine Learning Working with Different Genetic Algorithm Representations in Python Depending on the nature of the problem being optimized, the genetic algorithm (GA) supports two different gene representations: binary, and decimal. The binary GA has only two values for its
New: Better secrets management arrives in Paperspace Gradient We're delighted to announce the arrival of new secrets management in Gradient! Secrets are now available in Gradient Projects!Secrets are a way to scope keys and credentials locally at
Computer Vision Paper Review: GhostNet (CVPR 2020) in PyTorch and TensorFlow In this post we'll take an in-depth look at feature maps in convolutional neural networks, do a thorough review of GhostNet, and break down the code in PyTorch and TensorFlow.
Customer Story An Evolution in Gaming Interview with Wessel Stoop, the creator of The Sapling: a game about evolution for Windows and Linux.
Paperspace Joins NVIDIA Cloud Service Provider Program, Accelerates Cloud GPU Adoption & Remote Work New York City, September 10, 2020 - Paperspace today announced it has joined the Cloud Service Provider Program within the NVIDIA Partner Network (NPN) to bring GPU acceleration to the
Computer Vision Image Super-Resolution: A Comprehensive Review Image Super Resolution refers to the task of enhancing the resolution of an image from low-resolution (LR) to high (HR). It is popularly used in the following applications: Surveillance: to
New Fast.ai course for 2020 We first partnered with Jeremy Howard and the Fast.ai team around three years ago for the launch of the original course. If you aren't already familiar, Fast.ai is
Reinforcement Learning Play Super Mario Bros with a Double Deep Q-Network As cool as neural networks are, the first time that I felt like I was building true AI was not when working on image classification or regression problems, but when
Keras A Guide to TensorFlow Callbacks TensorFlow callbacks are an essential part of training deep learning models, providing a high degree of control over many aspects of your model training.
Gradient Paperspace CEO Dillon Erb joins the TWIML AI Podcast Few people have as much expertise in the machine learning space as Sam Charrington from TWIML (This Week in Machine Learning). For years now he's been inspiring us with his
Deepfakes Getting Real with Deepfake Artist Jesse Richards Interview with Tiktok and Instagram deepfaker, Jesse Richards (@Iamadeepfaker).
Keras How to Build a Variational Autoencoder in Keras This tutorial gives an introduction to the variational autoencoder (VAE) neural network, how it differs from typical autoencoders, and its benefits. We'll then build a VAE in Keras that can
Introducing Paperspace Terraform Provider At Paperspace we're building the world's most advanced GPU and machine learning cloud. ⚡ As part of this mission, we're making tools available along the way to help you get the
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.
Neural Network Neural Architecture Search Part 4: REINFORCE Gradient and Evaluation So far we've built two major components of our neural architecture search (NAS) pipeline. In the second part of this series we created a model generator which takes encoded sequences
NLP Generating Text Summaries Using GPT-2 on PyTorch with Minimal Training In this article I will discuss an efficient abstractive text summarization approach using GPT-2 on PyTorch with the CNN/Daily Mail dataset.
Updated Terraform Installer, New CLI, and Introducing Managed Private Gradient Clusters We recently launched the Terraform-based Gradient MLOps Installer to let users turn any cloud into a super-powerful machine learning platform. Out of the box the Gradient Installer lets you run
NumPy Nuts and Bolts of NumPy Optimization Part 2: Speed Up K-Means Clustering by 70x In this part we'll see how to speed up an implementation of the k-means clustering algorithm by 70x using NumPy. We cover how to use cProfile to find bottlenecks in the code, and how to address them using vectorization.
Customer Story Building Virtual Worlds with CyberLayerVR Q&A with CyberLayerVR's Founder and CEO Bo Layer, a subject expert in VR, gaming, and machine learning.
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.
Machine Learning Building a Game-Playing Agent for CoinTex Using the Genetic Algorithm Games can have complex environments, where many different factors contribute to making a decision. An entire area of artificial intelligence is devoted to building game-playing agents that can make the
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
Neural Network Capsule Networks: A Quick Primer Whether you're new to deep learning or a serious researcher, you've surely encountered the term Convolutional Neural Networks. They are one of the most researched and top-performing architectures in the
