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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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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Knet-the-Julia-dope: An interactive book on deep learning.

Written by Manuel Antonio Morales (@moralesq). This repo is the Julia translation of the mxnet-the-straight-dope repo, a collection of notebooks designed to teach deep learning, MXNet, and the gluon interface. This project grew out of the MIT course 6.338 Modern Numerical Computing with Julia taught by professor Alan Edelman. Our main objectives are:

• Introduce the Julia language and its main packages in the context of deep learning
  
• Introduce Julia's package Knet: an alternative/complementary option to MXNet
  
• Leverage the strengths of Jupyter notebooks to present prose, graphics, equations, and code together in one place

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