JuliaCon 2018 Workshop on Machine Learning with Julia

JuliaCon 2018. Machine Learning with Julia: Elegance, Speed and Ease. Mike Innes, Deniz Yuret, Pontus Stenetorp.
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Bir günlük derin öğrenme kursu

• Giriş sunumu
• IJulia defterleri
• Julia ne kadar hızlı?
• Julia öğrenelim
• MNIST el yazısı tanıma problemi
• Doğrusal modeller, türev ve optimizasyon
• Çok katmanlı modeller
• Konvolüsyonel modeller
• Özyinelemeli modeller
  • sentiment analizi
  • dil modelleri
  • otomatik tercüme

Sunumlar:

• 28.06.2018: Yapı Kredi Teknoloji Semineri, Koç Üniversitesi
• 30.06.2018: 25. İstatistiksel Fizik Günleri, İzmir Yüksek Teknoloji Enstitüsü
• 02.07.2018: Boğaz'da Yapay Öğrenme İsmail Arı Yaz Okulu, Boğaziçi Üniversitesi

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Derin Öğrenmeye Giriş, BYOYO 2018

BYOYO 2018 Boğaz'da Yapay Öğrenme İsmail Arı Yaz Okulu. Derin Öğrenmeye Giriş. Deniz Yuret.
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Erenay Dayanık, M.S. 2018

Current position: PhD student at University of Stuttgart, Germany (Linkedin)
M.S. Thesis: Morphological Tagging and Lemmatization with Neural Components. Koç University, Department of Computer Engineering. June, 2018. (PDF, Presentation, Code, Data)
Publications: bibtex.php

Abstract

I describe and evaluate MorphNet, a language-independent, end-to-end model that is designed to combine morphological analysis and disambiguation. Tradition- ally, analysis of morphologically complex languages has been performed in two stages: (i) A morphological analyzer based on finite-state transducers produces all possible morphological analyses of a word, (ii) A statistical disambiguation model picks the correct analysis based on the context for each word. MorphNet uses a sequence- to-sequence recurrent neural network to combine analysis and disambiguation. The model consists of three LSTM encoders to create embeddings of various input fea- tures and a two layer LSTM decoder to predict the correct morphological analysis. When MorphNet is trained with text labeled with correct morphological analyses, the model is able to achieve state-of-the art or comparable results in twenty-six different languages.

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Comp541 Deep Learning lecture videos on YouTube

The whole playlist.
Guest lecture by Alan Edelman: Why Julia?
Introduction to Neural Networks
Introduction to Julia
Backpropagation
Softmax Classification
Multi Layer Perceptrons
Convolutional Networks I
Convolutional Networks II
Convolutional Networks III
Recurrent Networks I
Recurrent Networks II
Methodology
Visualization
Optimization
Generalization
Generative Models
Reinforcement Learning: Intro
RL: Markov Decision Processes
RL: Dynamic Programming
RL: Model-free Methods
RL: Value Function Approximation and Policy Gradient Methods


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