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Sebastian Raschka
Works at Michigan State University
Attends Michigan State University
Lives in East Lansing
848 followers|3,004,707 views
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If you are interested in machine learning, I am happy to announce that my "Python Machine Learning" book is available for free today (for 4 1/2 more hours); just found out about it last night) :) : https://www.packtpub.com/packt/offers/free-learning

PS: And you can grab the code examples from this github repo: https://github.com/rasbt/python-machine-learning-book
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Sebastian Raschka

Supervised Learning  - 
 
Quite a productive weekend: "Model evaluation, model selection, and algorithm selection in machine learning - Part II"

http://sebastianraschka.com/blog/2016/model-evaluation-selection-part2.html

part III & IV are already (/almost) finished and will follow soon; was just a bit afraid that it was a tad too long ;) Looking forward to your feedback!


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+S. S. Singh oh wow, great to hear that I inspired you to blog occasionally as well. Looking forward to your articles!
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Sebastian Raschka

Supervised Learning  - 
 
Finally found some spare time for blogging again :). I am kind of rusty, and I'd love to get some feedback!
http://sebastianraschka.com/blog/2016/model-evaluation-selection-part1.html
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That's great, thanks for taking the time Marianna, I really appreciate it! And yes, I agree with all of your points above :)
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Sebastian Raschka

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In order to explain the differences between alternative approaches to estimating the parameters of a model, let's take a look at a concrete example: Ordinary Least Squares (OLS) Linear Regression. The illustration below shall serve as a quick reminder to recall the different components of a ...
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Sebastian Raschka

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#SciPy2016 initial list of talks and posters is announced! Check out the lineup of fantastic presentations. Early bird registration ends Sunday 5/22! http://ow.ly/KXAh300fxA0 #Python
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By Sebastian Raschka, Michigan State University. There are several different reasons why implementing algorithms from scratch can be useful: it can help us to understand the inner works of an algorithm; we could try to implement an algorithm more efficiently; we can add new features to an ...
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Sebastian Raschka

Supervised Learning  - 
 
In case you were following my model evaluation and selection series, I just uploaded Part III, yesterday. This time, it's about cross-validation and bias-variance trade-offs, and model selection.

http://sebastianraschka.com/blog/2016/model-evaluation-selection-part3.html
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Just got back from SciPy 2016! It was a blast! In case you couldn't attend, all videos of the talks and tutorials at SciPy 2016 are now online at https://www.youtube.com/playlist?list=PLYx7XA2nY5Gf37zYZMw6OqGFRPjB1jCy6

Btw. Andreas Mueller and I gave a "little" (1-day long) intro session on Machine Learning in Python & scikit-learn as as well :)

https://www.youtube.com/watch?v=OB1reY6IX-o&index=91&list=PLYx7XA2nY5Gf37zYZMw6OqGFRPjB1jCy6
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What is the Difference Between Deep Learning and “Regular” Machine Learning? http://www.kdnuggets.com/2016/06/difference-between-deep-learning-regular-machine-learning.html
That's an interesting question, and I try to answer this is a very general way. The tl;dr version of this is: Deep learning is essentially a set of techniques that help we to parameterize deep neural network structures, neural networks with many, many layers and parameters.
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Sebastian Raschka

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Software engineering is about developing programs or tools to automate tasks. Instead of "doing things manually," we write programs; a program is basically just a machine-readable set of instructions that can be executed by a computer. Let's consider a classic example: e-mail spam filtering.
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The popular machine learning projects, in general, are popular because they either provide a wide range of needed services or they were the first (or possibly best) to provide a particular niche service to users. These popular projects include Scikit-learn, TensorFlow, Theano, MXNet (maybe?) ...
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Comparing and Computing Performance Metrics in Cross-Validation – Imbalanced Class Problems and 3 Different Ways to Compute the F1 Score

http://sebastianraschka.com/faq/docs/computing-the-f1-score.html

A little section I removed from an upcoming blog post; before deleting it I thought it may still be useful sharing it still it's quite informative.
PS: How do you compute F1 scores? Have to admit I did it the way I listed as scenario (1) for a long time, but will adjust my code to scenario 3 now ...
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Oh, thanks; that's true, somehow that's got partially flipped! :P
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Work
Occupation
PhD candidate at Michigan State University
Skills
Machine Learning, Pattern Classification, Python, C++, SQL Databases, Bioinformatics
Employment
  • Michigan State University
    PhD student, 2011 - present
    Developing software for protein structure analyses.
Places
Map of the places this user has livedMap of the places this user has livedMap of the places this user has lived
Currently
East Lansing
Previously
Herne, Germany - Moers, Germany
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Tagline
Data scientist and Machine learning enthusiast with a big passion for Python & open source. Author of "Python Machine Learning"
Introduction

Some of my greatest passions are "Data Science" and machine learning. I enjoy everything that involves working with data: The discovery of interesting patterns and coming up with insightful conclusions using techniques from the fields of data mining and machine learning for predictive modeling.

I am a big advocate of working in teams and the concept of "open source." In my opinion, it is a positive feedback loop: Sharing ideas and tools that are useful to others and getting constructive feedback that helps us learn! 

A little bit more about myself: Currently, I am sharpening my analytical skills as a PhD candidate at Michigan State University where I am currently working on a highly efficient virtual screening software for computer-aided drug-discovery and a novel approach to protein ligand docking (among other projects). Basically, it is about the screening of a database of millions of 3-dimensional structures of chemical compounds in order to identifiy the ones that could potentially bind to specific protein receptors in order to trigger a biological response.


In my free-time I am also really fond of sports: Either playing soccer and tennis in the open air or building models for predictions. I always enjoy creative discussions, and I am happy to connect with people. Please feel free to contact me by email or in one of those many other networks!

Education
  • Michigan State University
    Molecular Biology, 2011 - present
    Computational Biology Ph.D. program
Basic Information
Gender
Male
Sebastian Raschka's +1's are the things they like, agree with, or want to recommend.
A Concise Overview of Standard Model-fitting Methods
www.kdnuggets.com

In order to explain the differences between alternative approaches to estimating the parameters of a model, let's take a look at a concrete

How to Explain Machine Learning to a Software Engineer
www.kdnuggets.com

Software engineering is about developing programs or tools to automate tasks. Instead of "doing things manually," we write programs; a progr

Why Implement Machine Learning Algorithms From Scratch?
www.kdnuggets.com

By Sebastian Raschka, Michigan State University. There are several different reasons why implementing algorithms from scratch can be useful:

The Development of Classification as a Learning Machine
www.kdnuggets.com

By Sebastian Raschka, Michigan State University. There are two fundamental milestones I'd say. The first one is Fisher's Linear Discriminant

Conversation with data scientist Sebastian Raschka: A New Podcast Episode
www.kdnuggets.com

In this post we present a interview of Sebastian Raschka, data scientist and author of Python Machine Learning. Who discussed about machine

How To Use Plugins In MacPyMOL
scientific-ocean.com

When I was working with protein structures, I usually used PyMOL on a Linux machibe for this task; only occasionally I used MacPyMOL at home