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🧑‍🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠

Translated"开源大模型食用指南" (Open-Source Large Model Usage Guide): a tutorial tailor-made for Chinese users on quickly fine-tuning (full-parameter/LoRA) and deploying domestic and international open-source large language models (LLM) and multimodal large models (MLLM) in a Linux environment

TranslatedThe place where 冴羽 (Yu Saeba, "Saeba Yu") writes his blog; he plans to write four series: an in-depth JavaScript series, a JavaScript topics series, an ES6 series, and a React series.

  • blog
  • javascript
  • advanced

TranslatedQiangu's illustrated front-end tutorials, an extremely detailed knowledge base from beginner to advanced front-end. Learn front-end from scratch and become a refined and elegant front-end engineer.

TranslatedA Python web scraping tutorial series, learning Python web scraping from 0 to 1, including browser packet capture and mobile APP packet capture with tools such as fiddler and mitmproxy, the use of the various modules involved in web scraping, such as requests, beautifulSoup, selenium, appium, scrapy, etc., as well as IP proxies, CAPTCHA recognition, using Mysql and MongoDB databases with Python, multi-threaded and multi-process scraping, reverse engineering and cracking of CSS scraping encryption, JS scraping reverse engineering, distributed scraping, practical scraping project examples, etc.

  • python3
  • python-script
  • python-spider

TranslatedA repository covering six columns: Spring Boot 2.X, Spring Cloud, Spring Cloud Alibaba, Dubbo, distributed message queues, and distributed transactions. I hope you will give it a quick tap and click Star in the upper right corner. Thank you, 1024

TranslatedAn easy-to-understand, witty and humorous Java learning guide, covering core knowledge points such as Java basics, Java concurrent programming, the Java Virtual Machine, Java enterprise development, and Java interviews. To learn Java, look no further than 二哥's (Erge, "Second Brother") Java advancement path 😄

Translatedflink learning blog. http://www.54tianzhisheng.cn/ Covers Flink introduction, concepts, principles, hands-on practice, performance tuning, source code analysis, and more. Includes learning examples involving Flink Connector, Metrics, Library, DataStream API, Table API & SQL, and also shares large-scale project cases of Flink in production (PV/UV, log storage, real-time deduplication of 10 billion records, monitoring and alerting). You are welcome to support my column 《大数据实时计算引擎 Flink 实战与性能优化》 ("Big Data Real-Time Computing Engine Flink: Hands-On Practice and Performance Optimization")

Translatedmall learning tutorial, with a comprehensive analysis of the architecture, business logic, and key technical points. The mall project (60k+ stars) is an e-commerce system built with currently mainstream technologies. It covers technologies such as SpringBoot, MyBatis, Elasticsearch, RabbitMQ, Redis, MongoDB, and MySQL, and uses Docker containerized deployment.

Translated:books: This repository is a summary of the technical learning journey of the author, Xiao Fuge (小傅哥), from many years of working in front-line internet Java development. It aims to give everyone a clear and detailed learning tutorial, with a focus on writing about core Java content. If this repository helps you, please show your support (follow, like, share)!

TranslatedA programmer who loves cooking and is determined to use animations to explain algorithms in plain, easy-to-understand terms. My interview website: www.chengxuchu.com

In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.