はじめに — 機械学習帳import torch x = torch.tensor([1., -1.]) w = torch.tensor([1.0, 0.5], requires_grad=True) loss = -torch.dot(x, w).sigmoid().log() loss.backward() print(loss.item()) print(w.grad)
GMOペパボ、エンジニアの研修資料を無料公開 合計14時間の機械学習研修ほか | Ledge.ai
画像は公式ブログ（該当ページ）より GMOペパボ株式会社は9月21日、公式ブログにおいて、株式会社ミクシィやサイボウズ株式会社、株式会社リクルートなどに続き、エンジニア研修2021の研修資料を無料公開した。研修資料は「モバイルアプリ研修」「機械学習研修」などで構成される。目次は以下のとおり。 Rails Tutorialコンテナ研修Web セキュリティ研修フロントエンド研修モバイルアプリ研修機械学習研修「モバイルアプリ研修」は、宣言的UIプログラミングの利点を学びつつ、Flutterを用いてモバイルアプリケーションを開発できることに目的としている。 「機械学習研修」は、機械学習の初学者が手法のコンセプトを学び、手法を用いる際の設計や手順が研修前と比較して具体化できることをゴールに設定した。機械学習の前提としてのデータの重要性と、その扱い方を習得することにもフォーカスしている。日程は5日間・
Applications of Deep Neural Networks with Keras
Deep learning is a group of exciting new technologies for neural networks. Through a combination of advanced training techniques and neural network architectural components, it is now possible to create neural networks that can handle tabular data, images, text, and audio as both input and output. Deep learning allows a neural network to learn hierarchies of information in a way that is like the f
Python Data Science Handbook | Python Data Science Handbook
This website contains the full text of the Python Data Science Handbook by Jake VanderPlas; the content is available on GitHub in the form of Jupyter notebooks. The text is released under the CC-BY-NC-ND license, and code is released under the MIT license. If you find this content useful, please consider supporting the work by buying the book!
GitHub - rlabbe/Kalman-and-Bayesian-Filters-in-Python: Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters,
Kalman and Bayesian Filters in Python Introductory text for Kalman and Bayesian filters. All code is written in Python, and the book itself is written using Jupyter Notebook so that you can run and modify the code in your browser. What better way to learn? "Kalman and Bayesian Filters in Python" looks amazing! ... your book is just what I needed - Allen Downey, Professor and O'Reilly author. Thank
NLP From Scratch: Classifying Names with a Character-Level RNN — PyTorch Tutorials 2.5.0+cu124 documentation
Learn Get Started Run PyTorch locally or get started quickly with one of the supported cloud platforms Tutorials Whats new in PyTorch tutorials Learn the Basics Familiarize yourself with PyTorch concepts and modules PyTorch Recipes Bite-size, ready-to-deploy PyTorch code examples Intro to PyTorch - YouTube Series Master PyTorch basics with our engaging YouTube tutorial series
Contents
These notes form a concise introductory course on deep generative models. They are based on Stanford CS236, taught by Stefano Ermon and Aditya Grover, and have been written by Aditya Grover, with the help of many students and course staff. ⊕The notes are still under construction! Since these notes are brand new, you will find several typos. If you do, please let us know, or submit a pull request w
CSE 599
CSE 599, Autumn 2020 Generative Models Lecture: Monday, Wednesday 1:30-2:50 Instructor: John Thickstun Contact: thickstn@cs.washington.edu Office hours: Friday 3:00-4:00 (the same Zoom room as class) TA: Sami Davies Course Overview This course explores a variety of modern techniques for learning to sample from an unknown probability distribution given examples. Generative models are an active area
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GitHub - pytorch/tutorials: PyTorch tutorials.
Course Materials | Introduction to Computational Thinking with Julia, with Applications to Modeling the COVID-19 Pandemic | Mathematics | MIT OpenCourseWare
GitHub - ashishpatel26/500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code: 500 AI Machine learning Deep learning Computer vision NLP Projects with code