This article is reproduced from Machine Heart.This overview paper lists important research achievements in deep learning in recent years, covering methods, architectures, as well as regularization and optimization techniques. Machine Heart believes that this overview is a good reference for beginners in deep learning, helping to form a basic academic landscape and guiding literature searches. … Read more
Notes from Andrew Ng’s DeepLearning.ai Course [Andrew Ng’s DeepLearning.ai Notes 1] Intuitive Explanation of Logistic Regression [Andrew Ng’s DeepLearning.ai Notes 2] Simple Explanation of Neural Networks (Part 1) [Andrew Ng’s DeepLearning.ai Notes 2] Simple Explanation of Neural Networks (Part 2) Is deep learning not working well? Andrew Ng will help you optimize neural networks (1) … Read more
Reproduced from: Machine Heart Master these 17 methods to accelerate your PyTorch deep learning training in the most effortless way. Recently, a post on Reddit gained massive popularity. The topic is about how to speed up PyTorch training. The original author is LORENZ KUHN, a master’s student in computer science at ETH Zurich. The article … Read more
Author｜Jack Chih-Hsu Lin Source｜AI Park Compiled by｜ronghuaiyang Introduction 18 PyTorch tips you should know. Debugging deep learning pipelines is like finding the right combination of gears Why Should You Read This Article? The training/inference process of deep learning models involves many steps. With limited time and resources, the faster each iteration is, the quicker the … Read more
Excerpt from fast.ai Authors: Sylvain Gugger, Jeremy Howard Translated by: Machine Heart Contributors: Siyuan, Wang Shuting, Zhang Qian Optimization methods have always been a crucial part of machine learning and are the core algorithms of the learning process. Since its introduction in 2014, Adam has garnered widespread attention, with over 10,047 citations for the original … Read more
If you find this article helpful, please give it a thumbs up and follow us ~~~ Send a private message on WeChat to receive free AI, C++, and other related materials, continuously collected and updated! • Hello everyone, I am Student Xiao Zhang, sharing AI knowledge and practical cases daily • Welcome to like + … Read more
Source: Internet Introduction For almost all machine learning algorithms, whether supervised learning, unsupervised learning, or reinforcement learning, the ultimate goal generally boils down to solving optimization problems. Therefore, optimization methods play a central role in the derivation and implementation of machine learning algorithms. In this article, the author will provide a comprehensive summary of the … Read more
Originally from Data Department THU Source CloudB Computational Thinking and Aesthetics "How can 'humans' do what they are good at, while leaving the rest to machines. [ Introduction ] Since the 1980s, machine learning has led the development trend of artificial intelligence, shifting the contribution of artificial intelligence from human-given intelligence to machine-learned intelligence. Undoubtedly, … Read more
Introduction Want to know how those large-scale neural networks are trained? OpenAI summarizes in an article: Besides needing many GPUs, algorithms are also crucial! Many advancements in AI today are attributed to large neural networks, especially the pre-trained models provided by large companies and research institutions, which have significantly propelled the progress of downstream tasks. … Read more
Source: Machine Learning Grocery Store This article is approximately 1800 words long and suggests a reading time of 10 minutes. One of the most challenging parts of the ML workflow is finding the best hyperparameters for the model. The performance of ML models is directly related to hyperparameters. Introduction Wikipedia states, “Hyperparameter optimization or tuning … Read more