PyTorchでDeep Learningを学べる書籍のPDFが無料公開中。522ページ全15章の大作で、理論面だけでなく現実世界での実例やデプロイ・運用周りも幅広く扱っている📘 https://t.co/5XxtgRq2WN
u++ on Twitter: "PyTorchでDeep Learningを学べる書籍のPDFが無料公開中。522ページ全15章の大作で、理論面だけでなく現実世界での実例やデプロイ・運用周りも幅広く扱っている📘 https://t.co/5XxtgRq2WN"
PyTorchでDeep Learningを学べる書籍のPDFが無料公開中。522ページ全15章の大作で、理論面だけでなく現実世界での実例やデプロイ・運用周りも幅広く扱っている📘 https://t.co/5XxtgRq2WN
NVIDIA Makes 3D Deep Learning Research Easy with Kaolin PyTorch Library | NVIDIA Technical Blog
NVIDIA Makes 3D Deep Learning Research Easy with Kaolin PyTorch Library Research efforts in 3D computer vision and AI have been rising side-by-side like two skyscrapers. But the trip between these formidable towers has involved clambering up and down dozens of stairwells. To bridge that divide, NVIDIA recently released Kaolin, which in a few steps moves 3D models into the realm of neural networks.
Making Deep Learning Go Brrrr From First Principles So, you want to improve the performance of your deep learning model. How might you approach such a task? Often, folk fall back to a grab-bag of tricks that might've worked before or saw on a tweet. "Use in-place operations! Set gradients to None! Install PyTorch 1.10.0 but not 1.10.1!" It's understandable why users often take such an ad-hoc appro
Course lectures for MIT Introduction to Deep Learning. http://introtodeeplearning.com
GitHub - anhaidgroup/deepmatcher: Python package for performing Entity and Text Matching using Deep Learning.
DeepMatcher is a Python package for performing entity and text matching using deep learning. It provides built-in neural networks and utilities that enable you to train and apply state-of-the-art deep learning models for entity matching in less than 10 lines of code. The models are also easily customizable - the modular design allows any subcomponent to be altered or swapped out for a custom imple
GitHub - NVIDIA-Merlin/NVTabular: NVTabular is a feature engineering and preprocessing library for tabular data designed to quickly and easily manipulate terabyte scale datasets used to train deep learning based recommender systems.
NVTabular is a feature engineering and preprocessing library for tabular data that is designed to easily manipulate terabyte scale datasets and train deep learning (DL) based recommender systems. It provides high-level abstraction to simplify code and accelerates computation on the GPU using the RAPIDS Dask-cuDF library. NVTabular is a component of NVIDIA Merlin, an open source framework for build
We describe the new field of mathematical analysis of deep learning. This field emerged around a list of research questions that were not answered within the classical framework of learning theory. These questions concern: the outstanding generalization power of overparametrized neural networks, the role of depth in deep architectures, the apparent absence of the curse of dimensionality, the surpr
Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers
Andrew Ng, Adjunct Professor & Kian Katanforoosh, Lecturer - Stanford University http://onlinehub.stanford.edu/ Andrew Ng Adjunct Professor, Computer Science Kian Katanforoosh Lecturer, Computer Science To follow along with the course schedule and syllabus, visit: http://cs230.stanford.edu/ To get the latest news on Stanford’s upcoming professional programs in Artificial Intelligence, visi
えるエル on Twitter: "2020年発売予定の深層学習フレームワークPyTorchの入門書籍『Deep Learning With Pytorch』がフリー公開 PyTorchで深層学習を始める際に必要な知識、特に入門者がイメージしにくいテンソル操作や、コ… https://t.co/CZ9FhDBQgY"
2020年発売予定の深層学習フレームワークPyTorchの入門書籍『Deep Learning With Pytorch』がフリー公開 PyTorchで深層学習を始める際に必要な知識、特に入門者がイメージしにくいテンソル操作や、コ… https://t.co/CZ9FhDBQgY
Neural networks have a reputation for being better at solving statistical or approximate problems than at performing calculations or working with symbolic data. In this paper, we show that they can be surprisingly good at more elaborated tasks in mathematics, such as symbolic integration and solving differential equations. We propose a syntax for representing mathematical problems, and methods for
Baselines for Uncertainty and Robustness in Deep Learning
Philosophy We strive to create an environment conducive to many different types of research across many different time scales and levels of risk. Learn more about our Philosophy Learn more
Contributors# Thank you to contributors for offering suggestions, identifying errors, and helping improve this book! Twitter handles, if available Contributed Chapter# Mehrad Ansari (@MehradAnsari) Sam Cox (@SamCox822) Heta Gandhi (@gandhi_heta) Josh Rackers (@JoshRackers) Contributed Content to Chapter# Geemi Wellawatte (@GWellawatte) Substantial Feedback on Content# Lily Wang (@lilyminium) Marc
Migrating a codebase from an archaic programming language such as COBOL to a modern alternative like Java or C++ is a difficult, resource-intensive task that requires expertise in both the source and target languages. COBOL, for example, is still widely used today in mainframe systems around the world, so companies, governments, and others often must choose whether to manually translate their code
もともとPython使いの自分には、ゼロから作るDeep LearningをPythonでやっても面白くない！ ということでJuliaでやってみた。 速度が犠牲にならないように多少は考慮したつもりだが、本家Python版の方が速いかも。 なんにも考えなくても速度がでるNumpy凄い... Juliaは数学的に自然な形でコードが書けて嬉しいが、行列演算をちょっと工夫して書かないとNumpyより遅くなる。 また、実装していて気がついたのだがどうやらdictionaryへのアクセスがJuliaは遅いらしい？ discourse.julialang.org dictionaryは便利だけど、できるだけstructを使って回すのが良さそうです。 最後までちゃんと読まなかったようで、Juliaの方が早いとの事。失礼しました。 #Julia言語 1秒(Python2,3)と0.18秒(Julia v0.
10 Best Laptops for Deep Learning & Machine Learning (2020)
Though there are various activities out there which requires a laptop with good specifications and you can get it at an affordable price but that’s not the case when you are looking for the best laptops for deep learning and machine learning. These need to have more than the good specification in a laptop. They need to be the best.. Many people try to build their own desktop PC for deep learning b
Do we really need deep learning models for time series forecasting?
3 main points ✔️ In the domain of time series prediction, deep learning models have recently shown rapid performance improvements. However, is classical machine learning models no longer necessary, which is why this large-scale survey and comparison experiment was conducted. ✔️ GBRT is used as a representative of classical learning models. The representation of inter-sequence dependencies realized
GitHub - facebookresearch/d2go: D2Go is a toolkit for efficient deep learning
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Authors: Nick Erickson, Jonas Mueller, Hang Zhang, Balaji Kamakoti Thanks to Aaron Markham, Mu Li, Matthias Seeger, Talia Chopra, and Sheng Zha for their early feedback and edits. Introducing AutoGluonAutoGluon is a new open source AutoML library that automates deep learning (DL) and machine learning (ML) for real world applications involving image, text and tabular datasets. Whether you are new t
How to apply machine learning and deep learning methods to audio analysis
Author: Niko Laskaris, Customer Facing Data Scientist, Comet.ml To view the code, training visualizations, and more information about the python example at the end of this post, visit the Comet project page. IntroductionWhile much of the writing and literature on deep learning concerns computer vision and natural language processing (NLP), audio analysis — a field that includes automatic speech re
require 'numo/narray' def identity_function(x) return x end def step_function(x) grad = Numo::Uint32.zeros(x.shape) grad[x>=0] = 1 grad end def sigmoid(x) 1 / (1 + Numo::DFloat::Math.exp(-x)) end def sigmoid_grad(x) (1.0 - sigmoid(x)) * sigmoid(x) end def relu(x) copy = x.copy copy[x < 0] = 0 copy end def relu_grad(x) grad = Numo::DFloat.zeros(x.shape) grad[x>=0] = 1 grad end def softmax(x) if x.n
Anomaly detection, a.k.a. outlier detection or novelty detection, has been a lasting yet active research area in various research communities for several decades. There are still some unique problem complexities and challenges that require advanced approaches. In recent years, deep learning enabled anomaly detection, i.e., deep anomaly detection, has emerged as a critical direction. This paper sur
Geometric Deep Learning is an attempt for geometric unification of a broad class of ML problems from the perspectives of symmetry and invariance. These principles not only underlie the breakthrough performance of convolutional neural networks and the recent success of graph neural networks but also provide a principled…
Facebook open-sources DLRM, a deep learning recommendation model
Time's almost up! There's only one week left to request an invite to The AI Impact Tour on June 5th. Don't miss out on this incredible opportunity to explore various methods for auditing AI models. Find out how you can attend here. Facebook today announced the open source release of Deep Learning Recommendation Model (DLRM), a state-of-the-art AI model for serving up personalized results in produc
Attention in transformers, visually explained | Chapter 6, Deep Learning
Demystifying attention, the key mechanism inside transformers and LLMs. Instead of sponsored ad reads, these lessons are funded directly by viewers: https://3b1b.co/support Special thanks to these supporters: https://www.3blue1brown.com/lessons/attention#thanks An equally valuable form of support is to simply share the videos. Demystifying self-attention, multiple heads, and cross-attention. Inst
GitHub - alibaba/MNN: MNN is a blazing fast, lightweight deep learning framework, battle-tested by business-critical use cases in Alibaba
MNN is a highly efficient and lightweight deep learning framework. It supports inference and training of deep learning models and has industry-leading performance for inference and training on-device. At present, MNN has been integrated into more than 30 apps of Alibaba Inc, such as Taobao, Tmall, Youku, DingTalk, Xianyu, etc., covering more than 70 usage scenarios such as live broadcast, short vi
Buy from Amazon. Buy from Cambridge University Press. Download a draft from the arXiv. Reload website: deeplearningtheory.com The Principles of Deep Learning Theory An Effective Theory Approach to Understanding Neural Networks Daniel A. Roberts, Sho Yaida, Boris Hanin A Cambridge University Press Book This book develops an effective theory approach to understanding deep neural networks of practica
Adam — latest trends in deep learning optimization.
Adam [1] is an adaptive learning rate optimization algorithm that’s been designed specifically for training deep neural networks. First published in 2014, Adam was presented at a very prestigious conference for deep learning practitioners — ICLR 2015. The paper contained some very promising diagrams, showing huge performance gains in terms of speed of training. However, after a while people starte
Training deep learning models for NLP tasks typically requires many hours or days to complete on a single GPU. In this post, we leverage Determined’s distributed training capability to reduce BERT for SQuAD model training from hours to minutes, without sacrificing model accuracy. In this 2-part blog series, we outline tips and tricks to accelerate NLP deep learning model training across multiple G
Deep learning: How OpenCV's blobFromImage works - PyImageSearch
Today’s blog post is inspired by a number of PyImageSearch readers who have commented on previous deep learning tutorials wanting to understand what exactly OpenCV’s blobFromImage function is doing under the hood. You see, to obtain (correct) predictions from deep neural networks you first need to preprocess your data. In the context of deep learning and image classification, these preprocessing t
Deep learning rethink overcomes major obstacle in AI industry
Rice University computer scientists have overcome a major obstacle in the burgeoning artificial intelligence industry by showing it is possible to speed up deep learning technology without specialized acceleration hardware like graphics processing units (GPUs). Rice University's Anshumali Shrivastava led a team that demonstrated how to implement deep learning technology without specialized acceler
Coursera / Neural Networks and Deep Learning 受講メモ - たにしきんぐダム
Deep Learning を勉強しようと思い、Coursera の Deep Learning Specialization を受講し始めた。 ある手法がうまくいく/うまくいかないことのイメージを説明してくれたり、実装に際してのtips and tricksも教えてくれるのが良い。 解析や線形代数を知らない人にも門戸を開くために、コスト関数やactivation functionの微分の計算などは答えだけ提示している。(良いと思う) 穴埋め形式ではあるのものの、Jupyter Notebook 上で自分で Neural Network を実装する課題があって面白い。 www.coursera.org この専門講座は5つのコースから構成されていて、Neural Networks and Deep Learning はその1つ目のコース。内容としてはロジスティック回帰、単層ニューラルネット、
GitHub - keroro824/HashingDeepLearning: Codebase for "SLIDE : In Defense of Smart Algorithms over Hardware Acceleration for Large-Scale Deep Learning Systems"
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PyTorch Deep Learning Nanodegree: Generative Adversarial Networks
A fifth part of the Nanodegree: GAN Introduction Neural Networks Convolutional Neural Networks Recurrent Neural Networks Generative Adversarial Networks Deploying a Model The end of this journey GeneralIn this lesson we learn about various types of GANs and how to implement them. Also, we’ll work on a fourth project — generating faces. Generative Adversarial Networks The first lesson on GANs is le
Deep Learning Labの次回大規模イベント情報と、今後の方針・施策 ～2周年イベントより～
Deep Learning Labの次回大規模イベント情報と、今後の方針・施策 ～2周年イベントより～：気になるニュース＆ネット記事 DLLABの2周年イベントで発表された「DLLABの活動の現状と今後」について説明。「次回の大規模イベント開催」の速報や、今後の施策として計画されている「機械学習＋IoTのコンテスト企画」と「企業のための実験室としてのDLLAB活用」について概説する。
A New Lens on Understanding Generalization in Deep Learning
Philosophy We strive to create an environment conducive to many different types of research across many different time scales and levels of risk. Learn more about our Philosophy Learn more
By Alex Nodet — Artificial Intelligence Engineer King, like many other game companies, follows a free-to-play business model. This trend has increased within the gaming industry in recent years. Its efficiency is driven by frequent releases of new in-game content. As of October 2018, Candy Crush Saga offers more than 3,700 levels to its players, and 15 new ones are released every week. Offering hi
OCR with Keras, TensorFlow, and Deep Learning - PyImageSearch
PyImageSearch You can master Computer Vision, Deep Learning, and OpenCV - PyImageSearch In this tutorial, you will learn how to train an Optical Character Recognition (OCR) model using Keras, TensorFlow, and Deep Learning. This post is the first in a two-part series on OCR with Keras and TensorFlow: Part 1: Training an OCR model with Keras and TensorFlow (today’s post)Part 2: Basic handwriting rec
Deep Learning - Augmentor で画像のデータセットを水増し (オーグメンテーション) する。 - Pynote
概要 画像データをオーグメンテーションするライブラリ Augmentor の使い方について紹介する。 概要 関連記事 インストール 基本的な使い方 入出力の方式 入力画像の枚数と同じ枚数生成する。 n 枚生成する。 ディレクトリから読み込む代わりに配列を渡す。 Keras Generator を作成する。 operation の種類 probability 2値化する。(black_and_white) グレースケール化する。 ヒストグラムを平坦化する。 切り抜きする。 反転する。 ネガ反転する。 明るさを変更する。 彩度を変更する。 コントラストを変更する。 一部を歪ませる。 画像の一部を隠す。 リサイズする。 回転する。 せん断する。 拡大する。 歪ませる。 関連記事 pynote.hatenablog.com インストール pip でインストールできる。 pip install Au
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Making Deep Learning go Brrrr From First Principles
MIT 6.S191: Introduction to Deep Learning
The Modern Mathematics of Deep Learning
Deep Learning for Symbolic Mathematics
Overview — deep learning for molecules & materials
Deep learning to translate between programming languages
Juliaでゼロから作るDeep Learning - 元バイオ系
AutoGluon: Deep Learning AutoML
敢えてRubyで学ぶ「ゼロから作るDeep Learning」基本関数群 - Qiita
Pythonでサクッと作れる時系列の予測モデルNeuralProphet（≒FacebookのProphet × Deep Learning）
Deep Learning for Anomaly Detection: A Review
Geometric foundations of Deep Learning
The Principles of Deep Learning Theory
Faster NLP with Deep Learning: Distributed Training
Human-Like Playtesting with Deep Learning
Google はどうやって Deep Learning でメモリ使用量を 99% 削減したか。