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.
Yoshua Bengio’nun NIPS 2015’de yaptığı derin öğrenme sunum videosu ve dosyasına aşağıdan erişebilirsiniz.
bengio-lecun-20151207-deep-learning-tutorial-nips.pdf
Kaynak: http://research.microsoft.com/apps/video/default.aspx?id=259574&r=1
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 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.
| 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 |
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| 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:
Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.