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Jakub Bareš, What skills are needed for machine learning jobs
http://bares.co/post/151924679718/what-skills-are-needed-for-machine-learning-jobs
What skills are needed for machine learning jobs ». UPDATE: I create a repo on github of hundreds of software links that should help get you started: https:/ github.com/josephmisiti/…. 6 Become familiar with the Hadoop sub-projects: HBase, Zookeeper [32], Hive [33], Mahout, etc. These projects can help you store/access your data, and they scale. Posted 4 months ago. Student of artificial intelligence and machine learning. 2011 2017 Powered by Tumblr.
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Jakub Bareš, Deep learning lecture series | Oxford
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Deep learning lecture series Oxford ». Posted 6 months ago. Student of artificial intelligence and machine learning. 2011 2017 Powered by Tumblr.
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Jakub Bareš
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Neural Networks for Machine Learning University of Toronto ». Posted 7 months ago. Machine Learning Stanford University ». Posted 7 months ago. 40 Techniques Used by Data Scientists ». The 40 data science techniques. Clustering - (aka Unsupervised Learning). Principal Component Analysis - (PCA). Support Vector Machine - (SVM). Nearest Neighbors - (k-NN). Feature Selection - (aka Variable Reduction). Indexation / Cataloguing *. Posted 7 months ago. The Mathematics of Machine Learning ». 5 Others: This com...
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Jakub Bareš, Machine Learning Exercises In Python
http://bares.co/post/148942522358/machine-learning-exercises-in-python
Machine Learning Exercises In Python ». Part 1 - Simple Linear Regression. Part 2 - Multivariate Linear Regression. Part 3 - Logistic Regression. Part 4 - Multivariate Logistic Regression. Part 5 - Neural Networks. Part 6 - Support Vector Machines. Part 7 - K-Means Clustering and PCA. Part 8 - Anomaly Detection and Recommendation. Posted 6 months ago. Student of artificial intelligence and machine learning. 2011 2017 Powered by Tumblr.
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Jakub Bareš, Learning From Data - Online Course | Caltech
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Learning From Data - Online Course Caltech ». Lecture 1: The Learning Problem. Lecture 2: Is Learning Feasible? Lecture 3: The Linear Model I. Lecture 4: Error and Noise. Lecture 5: Training versus Testing. Lecture 6: Theory of Generalization. Lecture 7: The VC Dimension. Lecture 8: Bias-Variance Tradeoff. Lecture 9: The Linear Model II. Lecture 10: Neural Networks. Lecture 14: Support Vector Machines. Lecture 15: Kernel Methods. Lecture 16: Radial Basis Functions. Lecture 17: Three Learning Principles.
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Jakub Bareš, The New Artificial Intelligence Market | O'Reilly
http://bares.co/post/148975289198/the-new-artificial-intelligence-market-oreilly
The New Artificial Intelligence Market O'Reilly ». Posted 6 months ago. Student of artificial intelligence and machine learning. 2011 2017 Powered by Tumblr.
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Jakub Bareš, Deep Learning Summer School, Montreal 2015
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Deep Learning Summer School, Montreal 2015 ». Posted 6 months ago. Student of artificial intelligence and machine learning. 2011 2017 Powered by Tumblr.
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Jakub Bareš, Stat212b: Topics Course on Deep Learning |...
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Stat212b: Topics Course on Deep Learning Berkeley ». Posted 6 months ago. Student of artificial intelligence and machine learning. 2011 2017 Powered by Tumblr.
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Jakub Bareš, CS231n Convolutional Neural Networks for Visual...
http://bares.co/post/148827449123/cs231n-convolutional-neural-networks-for-visual
CS231n Convolutional Neural Networks for Visual Recognition Stanford University ». Posted 6 months ago. Student of artificial intelligence and machine learning. 2011 2017 Powered by Tumblr.
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