Categorizing images with deep learning into Elasticsearch
Search and analytics, data ingestion, and visualization – all at your fingertips.
Deep Learning | Udacity
Dive into deep learning with this practical course on TensorFlow and the Keras API. Gain an intuitive understanding of neural networks without the dense jargon. Learn to build, train, and optimize your own networks using TensorFlow. The course also introduces transfer learning, leveraging pre-trained models for enhanced performance. Designed for swift proficiency, this course prioritizes hands-on
Stanford University CS231n: Deep Learning for Computer Vision
Course Logistics Lectures: Tuesday/Thursday 12:00-1:20PM Pacific Time at NVIDIA Auditorium. Lecture Videos: Will be posted on Canvas shortly after each lecture. These are unfortunately only accessible to enrolled Stanford students. Office Hours: We will be holding a mix of in-person and Zoom office hours. You can find a full list of times and locations on the calendar. Contact: Announcements and a
RecSys 2014 Keynote by Jeff Dean: Large Scale Machine Learning for Predictive Tasks, Pt. 1
Because of the Youtube Live Streaming platform outage on Wednesday, this speaker was interrupted during the streaming session. The missing portion appears in this video. https://www.youtube.com/watch?v=Zuwf6WXgffQ (approx. 32 min. of additional content) The ACM Recommender System conference (RecSys) is the premier international forum for the presentation of new research results, systems and te
Engineering
Engineering Building the platform where people around the world come to search, save and shop Our Engineering team is at the core of bringing our platform to life for Pinners worldwide. Working collaboratively and cross-functionally with teams across the company, our engineers tackle growth-driving challenges to build an inspired and inclusive platform for all. Natasha Magliui Engineering Manager,
Large Scale Distributed Deep Networks
Recent work in unsupervised feature learning and deep learning has shown that being able to train large models can dramatically improve performance. In this paper, we consider the problem of training a deep network with billions of parameters using tens of thousands of CPU cores. We have developed a software framework called DistBelief that can utilize computing clusters with thousands of machines
ニューラルネットの逆襲 - Preferred Networks Research & Development
岡野原です。Deep Learningが各分野のコンペティションで優勝し話題になっています。Deep Learningは7、8段と深いニューラルネットを使う学習手法です。すでに、画像認識、音声認識、最も最近では化合物の活性予測で優勝したり、既存データ・セットでの最高精度を達成しています。以下に幾つか例をあげます。 画像認識 LSVRC 2012 [html] 優勝チームスライド [pdf], まとめスライド[pdf] Googleによる巨大なNeuralNetを利用した画像認識（猫認識として有名）[paper][slide][日本語解説] また、各分野のトップカンファレンスでDeep Learningのチュートリアルが行われ、サーベイ論文もいくつか出ました。おそらく来年以降こうした話が増えてくることが考えられます。 ICML 2012 [pdf] ACL 2012 [pdf] CVPR
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Unsupervised Computer Vision: The Current State of the Art
Simon Osindero, A.I. Architect, Flickr - RE.WORK Deep Learning Summit 2015
http://nlp.stanford.edu/courses/cs224n/2015/reports/24.pdf
Deep Style: Using Variational Auto-encoders for Image Generation
Andrew Ng, Chief Scientist at Baidu
Real-time Recommendations using Spark Comcast Labs
Andrew Ng: Deep Learning, Self-Taught Learning and Unsupervised Feature Learning
