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Deep Learning Kitabı Yakında Türkçe olarak Yayınlanacak

Yayınevinin talebi üzerine zorunlu düzeltme

Talep Tarihi: 31.10.2018

Talep İçeriği: Aşağıda üzeri çizili ifadenin tam olarak doğru olmadığı, kitap çevirisinin ve yayımının Buzdağı Yayınevi tarafından yapıldığı belirtilmiştir.

Open Zeka ve Buzdağı Yayınevi Ian Goodfellow, Yoshua Bengio ve Aaron Courville tarafından yazılan Deep Learning kitabını Türkçe olarak yakında yayınlayacak.

Makale: Challenges in Representation Learning: A Report on Three Machine Learning Contests

The ICML 2013 Workshop on Challenges in Representation Learning 3 focused on three challenges: the black box learning challenge, the facial expression recognition challenge, and the multimodal learning challenge. We describe the datasets created for these challenges and summarize the results of the competitions. We provide suggestions for organizers of future challenges and some comments on what kind of knowledge can be gained from machine learning competitions.

Derin Öğrenme Yaz Okulu 2015

Derin Öğrenme Yaz Okulu Montreal/Kanada’da Ağustos 2015 ayında icra edildi. 10 günlük faaliyette derin öğrenmenin kullanım alanlarına yönelik konusunda uzman kişilerin katıldığı sunumlar ve otonom sistem demoları yapıldı. Aşağıda  günlük programlar halinde sunulan sunumları indirip inceleyebilirsiniz.

Gelecek yaz döneminde benzer bir faaliyeti ülkemizde yapma konusunda şimdiden  hazırlıklara başladık. Değerli katkılarınızı bekliyoruz.


1’inci Gün – 03 Ağustos 2015
Pascal Vincent: Intro to ML
Yoshua Bengio: Theoretical motivations for Representation Learning & Deep Learning
Leon Bottou: Intro to multi-layer nets

2’nci Gün – 04 Ağustos 2015
Hugo Larochelle: Neural nets and backprop
Leon Bottou: Numerical optimization and SGD, Structured problems & reasoning
Hugo Larochelle: Directed Graphical Models and NADE
Intro to Theano

3’üncü Gün – 05 Ağustos 2015
Aaron Courville: Intro to undirected graphical models
Honglak Lee: Stacks of RBMs
Pascal Vincent: Denoising and contractive auto-encoders, manifold view

4’üncü Gün – 06 Ağustos 2015
Roland Memisevic: Visual features
Honglak Lee: Convolutional networks
Graham Taylor: Learning similarit

5’inci Gün – 07 Ağustos 2015
Chris Manning: NLP 101
Graham Taylor: Modeling human motion, pose estimation and tracking
Chris Manning: NLP / Deep Learning

6’ncı Gün – 08 Ağustos 2015
Ruslan Salakhutdinov: Deep Boltzmann Machines
Adam Coates: Speech recognition with deep learning
Ruslan Salakhutdinov: Multi-modal models

7’nci Gün – 09 Ağustos 2015
Ian Goodfellow: Structure of optimization problems
Adam Coates: Systems issues and distributed training
Ian Goodfellow: Adversarial examples

8’inci Gün – 10 Ağustos 2015
Phil Blunsom: From language modeling to machine translation
Richard Socher: Recurrent neural networks
Phil Blunsom: Memory, Reading, and Comprehension

9’uncu Gün – 11 Ağustos 2015
Richard Socher: DMN for NLP
Mark Schmidt: Smooth, Finite, and Convex Optimization
Roland Memisevic: Visual Features II

10’uncu Gün – 12 Ağustos 2015
Mark Schmidt: Non-Smooth, Non-Finite, and Non-Convex Optimization
Aaron Courville: VAEs and deep generative models for vision
Yoshua Bengio: Generative models from auto-encoder

Tüm sunumları indirmek için tıklayınız.

Kaynaklar:

https://stanfordsailors.wordpress.com

https://sites.google.com/site/deeplearningsummerschool

Makale: EmoNets: Multimodal Deep Learning Approaches for Emotion Recognition in Video

The task of the emotion recognition in the wild (EmotiW) Challenge is to assign one of seven emotions to short video clips extracted from Hollywood style movies. The videos depict acted-out emotions under realistic conditions with a large degree of variation in attributes such as pose and illumination, making it worthwhile to explore approaches which consider combinations of features from multiple modalities for label assignment. In this paper we present our approach to learning several specialist models using deep learning techniques, each focusing on one modality. Among these are a convolutional neural network, focusing on capturing visual information in detected faces, a deep belief net focusing on the representation of the audio stream, a K-Means based “bag-of-mouths” model, which extracts visual features around the mouth region and a relational autoencoder, which addresses spatio-temporal aspects of videos. We explore multiple methods for the combination of cues from these modalities into one common classifier. This achieves a considerably greater accuracy than predictions from our strongest single-modality classifier. Our method was the winning submission in the 2013 EmotiW challenge and achieved a test set accuracy of 47.67% on the 2014 dataset.