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scutan90 /DeepLearning-500-questions

JavaScript58K16K

TranslatedDeep Learning 500 Questions: in question-and-answer form, it covers hot topics in commonly used probability, linear algebra, machine learning, deep learning, computer vision and more, to help myself and readers who need it. The book is divided into 18 chapters and has more than 500,000 characters. Due to limited ability, readers are kindly asked to criticize and correct any improper parts of the book. To be continued............ If you are interested in cooperating, contact [email protected]. All rights reserved, infringement will be prosecuted. Tan 2018.06

Advanced-Frontend /Daily-Interview-Question

JavaScript27K3.2K

TranslatedI am Yiyang (木易杨, Muyiyang), author of the official account "高级前端进阶" (Advanced Frontend Progression). I tackle one frontend interview question from a big tech company every day. I wish everyone progress every day, and in a year you will see a different self.

haizlin /fe-interview

JavaScript26K3.2K

TranslatedDaily 3+1 for front-end interviews: driving learning through interview questions, promoting daily study and thinking, improving a little every day! Every morning at 5 a.m., interview questions are published purely by hand (pushing myself hard, delighting everyone). Over 6,000 front-end interview questions give full coverage of HTML/CSS/JavaScript/Vue/React/Nodejs/TypeScript/ECMAScritpt/Webpack/Jquery/mini programs/soft skills……

borisbanushev /stockpredictionai

JavaScript5.6K1.9K

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.