Pycon2016_ML

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list of Pycon2016 session related with ML

Pycon2016_ML 최신버젼 다운로드

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# Pycon2016_ML(DL)
## list of Pycon2016 session related with ML
![PYCON2016](https://d3nmt5vlzunoa1.cloudfront.net/pycharm/files/2016/05/pyconlogo.png)

: Pycon2016 awesome !! and It shows how much ML(DL) is hot these day, 
 Let's check below and train our real neural network(brain)!.

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- [Irene Chen - A Beginner's Guide to Deep Learning - PyCon 2016](https://www.youtube.com/watch?v=kVud83kqv30)   - [[slide](https://speakerdeck.com/pycon2016/irene-chen-a-beginners-guide-to-deep-learning)]
 
 
 : What is deep learning? It has recently exploded in popularity as a complex and incredibly powerful tool. This talk will present the basic concepts underlying deep learning in understandable pieces for complete beginners to machine learning. We will review the math, code up a simple neural network, and provide contextual background on how deep learning is used in production now.
 
 
- [Julia Ferraioli, Amy Unruh, Eli Bixby - Diving into Machine Learning through TensorFlow - PyCon 2016](https://www.youtube.com/watch?v=GZBIPwdGtkk)   - [[slide](https://storage.googleapis.com/amy-jo/talks/tf-workshop.pdf)], [[github](https://github.com/amygdala/tensorflow-workshop)]
 
 
 : What is deep learning? It has recently exploded in popularity as a complex and incredibly powerful tool. This talk will present the basic concepts underlying deep learning in understandable pieces for complete beginners to machine learning. We will review the math, code up a simple neural network, and provide contextual background on how deep learning is used in production now. 
 
 
- [Kashif Rasul - What's new in Deep Learning?](https://www.youtube.com/watch?v=mw-NfRO1jv0)   - [[slide](https://www.dropbox.com/s/b6lgvq6ijlutii4/new-deep-learning.pdf?dl=0)]
 
 
 : @karpathy's recent tweet "BatchNorm, STN, DCGAN, DRAW, soft/hard attention, char-rnn, DeepDream, NeuralStyle, TensorFlow, ResNet, AlphaGo.. a lot happened over 1 year" sums up the many new aspects of Deep Learning research. In this talk I will review some of the highlights of deep learning in the context of Python deep learning frameworks. @karpathy's recent tweet "BatchNorm, STN, DCGAN, DRAW, soft/hard attention, char-rnn, DeepDream, NeuralStyle, TensorFlow, ResNet, AlphaGo.. a lot happened over 1 year" sums up the many new aspects of Deep Learning research. In this talk I will review some of the highlights of deep learning in the context of Python deep learning frameworks.
 
 
- [Kevin Markham - Machine Learning with Text in scikit-learn - PyCon 2016](https://www.youtube.com/watch?v=znfy3T9OiAQ)], [[github](https://github.com/justmarkham/pycon-2016-tutorial)]
 
 
 : Although numeric data is easy to work with in Python, most knowledge created by humans is actually raw, unstructured text. By learning how to transform text into data that is usable by machine learning models, you drastically increase the amount of data that your models can learn from. In this tutorial, we'll build and evaluate predictive models from real-world text using scikit-learn.

- [Visual Diagnostics for More Informed Machine Learning: Within and Beyond Scikit-Learn - PyCon 2016](https://www.youtube.com/watch?v=Nt5HaoVKcvY)   - [slide]
 
 
 : Visualization has a critical role to play throughout the analytic process. Where static outputs and tabular data may render patterns opaque, human visual analysis can uncover volumes and lead to more robust programming and better data products. For Python programmers who dabble in machine learning, visual diagnostics are a must-have for effective feature analysis, model selection, and evaluation.

- [Making an Impact with Python Natural Language Processing Tools - PyCon 2016](https://www.youtube.com/watch?v=oSSnDeOXTZQ)   - [slide]
 
 
 : Do your tweets get lost in the shuffle? Would you like to predict a tweet's impact before you hit send? Python now has all the tools to make this possible. Several Python packages for machine learning and natural language processing have reached ""critical mass"" and can now be combined to perform these and other powerful natural language processing tasks. This tutorial will teach you how.

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If you know the slide link that doesn't here, please let me know thanks

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