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Computer Science and Programming

Computer Science and Programming

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Channel specialized for advanced topics of: * Artificial intelligence, * Machine Learning, * Deep Learning, * Computer Vision, * Data Science * Python Admin: @otchebuch Memes: @memes_programming Ads: @Source_Ads, https://telega.io/c/computer_science

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Computer Science and Programming (@computer_science_and_programming) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 138 788 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 818-o'rinni va Italiya mintaqasida 89-o'rinni egallagan.

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невідомо sanasidan buyon loyiha tez o‘sib, 138 788 obunachiga ega bo‘ldi.

03 Noyabr, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni -962 ga, so‘nggi 24 soatda esa -16 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.

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“Channel specialized for advanced topics of: * Artificial intelligence, * Machine Learning, * Deep Learning, * Computer Vision, * Data Science * Python Admin: @otchebuch Memes: @memes_programming Ads: @Source_Ads, https://telega.io/c/computer_sc...”

Yuqori yangilanish chastotasi (oxirgi ma’lumot 04 Noyabr, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Texnologiyalar & Aralashmalar toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.

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Postlar arxiv
Pytorch internals from Edward Z.Yang. PyTorch NYC meetup on May 2019. Concepts: * Tensors, * Storage, * Strides, * Layouts, * Device, * Dtype, * Autograd ...

Just another advance of #GAN generations. From Face to pix2pix

computer age statistical inference.pdf8.11 MB

Computer Age Statistical Inference - Algorithms, Evidence, & Data Science Table of Content: Part I Classic Statistical Infere
Computer Age Statistical Inference - Algorithms, Evidence, & Data Science Table of Content: Part I Classic Statistical Inference 1 Algorithms and Inference 2 Frequentist Inference 3 Bayesian Inference 4 Fisherian Inference and Maximum Likelihood Estimation 5 Parametric Models and Exponential Families Part II Early Computer-Age Methods 6 Empirical Bayes 7 James–Stein Estimation and Ridge Regression 8 Generalized Linear Models and Regression Trees 9 Survival Analysis and the EM Algorithm 10 The Jackknife and the Bootstrap 11 Bootstrap Confidence Intervals 12 Cross-Validation and Cp Estimates of Prediction Error 13 Objective Bayes Inference and MCMC 14 Postwar Statistical Inference and Methodology Part III Twenty-First-Century Topics 15 Large-Scale Hypothesis Testing and FDRs 16 Sparse Modeling and the Lasso 17 Random Forests and Boosting 18 Neural Networks and Deep Learning 19 Support-Vector Machines and Kernel Methods 20 Inference After Model Selection 21 Empirical Bayes Estimation Strategies

Thrilled to be teaching a new course on Deep Unsupervised Learning with Peterxichen (ImprovedGAN, InfoGAN, PixelCNN++, VLAE, PixelSNAIL, Flow++), Hojonathanho (Flow++, GAIL), Aravind(Flow++): * Lectures * Homeworks

Google IO 2019 keynote. Learn about the latest product and platform innovations at Google in a Keynote Several improvements are introduced this year in AI + Software + Hardware integration. 👇

Great Tensorflow tutorial Series from Hvass Laboratories, which one of the most dominating Deep Learning Framework with practical examples

SafeML ICLR 2019 Workshop accepted papers list. Read and explore new horizons of Machine Learning

I highly recommend the Cornell University's "Machine Learning for Intelligent Systems (CS4780/ CS5780)" course taught by Associate Professor Kilian Q. Weinberger.

"One Model to Rule Them All" Christoph Molnar. Some experienced toughts how to work effectively with your Machine Learning model(project)

Deep Learning lecture

Deep Learning lecture The full deck of (600+) slides, by Professor Gilles Louppe. PDF file available here: