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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 140 431 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 811-o'rinni va Italiya mintaqasida 88-o'rinni egallagan.

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01 Sentabr, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni -772 ga, so‘nggi 24 soatda esa -64 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 02 Sentabr, 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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https://www.adhiraiyan.org/deeplearning/02.00-Linear-Algebra?fbclid=IwAR1roZFCx4aHYDQfsbS47-su7VCajlQ_M9Z7ZHEatSx6xJI3CRUUoScwp2w

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Linear Algebra topics you need to understand Deep Learning with practical examples in Tensorflow 2.0.

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Machine learning is the main trend in IT and technologies .The most actual information about big data , machine learning , neural networks on channel : @ai_machinelearning_big_data

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Detailed description and transcription of talk: http://blog.ezyang.com/

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

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

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computer age statistical inference.pdf8.11 MB

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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

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https://sites.google.com/view/berkeley-cs294-158-sp19/home

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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

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https://www.youtube.com/watch?v=lyRPyRKHO8M

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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. 👇

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https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ

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

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https://sites.google.com/view/safeml-iclr2019/accepted-papers?fbclid=IwAR18gdqKNSE6Vao23HIc6J0zZ_ViACoHjr-zJ5OkpK1iIqR2154wly3RxPQ

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

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Youtube Video Lectures: 👇 https://www.youtube.com/playlist?list=PLl8OlHZGYOQ7bkVbuRthEsaLr7bONzbXS Course Lecture Notes: 👇 http://www.cs.cornell.edu/courses/cs4780/2018fa/lectures/

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

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https://bentoml.com/posts/2019-04-19-one-model/

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