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Neural Networks for Machine Learning - University of Toronto. About the Course Neural networks use learning algorithms that are inspired by our understanding of how the brain learns, but they are evaluated by how well they work for practical applications such as speech recognition, object recognition, image retrieval and the ability to recommend products that a user will like. As computers become more powerful, Neural Networks are gradually taking over from simpler Machine Learning methods. They are already at the heart of a new generation of speech recognition devices and they are beginning to outperform earlier systems for recognizing objects in images.

The course will explain the new learning procedures that are responsible for these advances, including effective new proceduresr for learning multiple layers of non-linear features, and give you the skills and understanding required to apply these procedures in many other domains. Recommended Background Programming proficiency in Matlab, Octave or Python. Course Format. Advice on Learning Deep Learning ( Neural Networks) Manu prakash wrote: I think of starting with a small project like Digit recognition, and learn the techniques needed to complete that small project.

You could use the UFLDL Tutorial by Andrew Ng as a starting point. He even uses MATLAB and a digit recognition task to teach you the main ideas of Unsupervised Feature Learning and Deep Learning. With this you could delve into the digit recognition competition. However, my personal advise is to get familiar with machine learning / statistical learning and programming first, get your feet wet.

Shreekanth wrote: If you are interested in learning about handling big data and you have already have a good grasp of Machine Learning/Statistical Learning then you should take a look at Hadoop and NoSQL databases. Week 1:MapReduceRageRank I hope that helps. Where are the Deep Learning Courses? — Data Community DC. This is a guest post by John Kaufhold. Dr. Kaufhold is a data scientist and managing partner of Deep Learning Analytics, a data science company based in Arlington, VA.

He presented an introduction to Deep Learning at the March Data Science DC. Why aren't there more Deep Learning talks, tutorials, or workshops in DC2? It's been about two months since my Deep Learning talk at Artisphere for DC2. First some preemptive answers to the “FAQ” downstream of the talk: Mary Galvin wrote a blog review of this event.Yes, the slides are available.Yes, corresponding audio is also available (thanks Geoff Moes).A recently "reconstructed" talk combining the slides and audio is also now available!

There actually was a class... Aaron Schumacher and Tommy Shen invited me to come talk in April for General's Data Science course. Resources to learn Deep Learning This is the first reason I don't think it's all that valuable for DC to have more of its own Deep Learning “academic” tutorials. UFLDL Tutorial - Ufldl. Lecun tutorial icml 2013. Deeplearning:slides:start | CILVR Lab @ NYU. On-Line Material from Other Sources A quick overview of some of the material contained in the course is available from my ICML 2013 tutorial on Deep Learning: Q&A about deep learning (Spring 2013 course on large-scale ML) 2012 IPAM Summer School deep learning and representation learning 2014 International Conference on Learning Representations (ICLR 2014) Week 1 2014-01-27 Lecture * Intro to Deep Learning Topics: Reading material: Scaling Learning Algorithms Towards AI (Y. 2014-01-29 Lab * Roy Lowrance's tutorial on Lua Topics: Reading Material: Not relevant Week 2 2014-02-03 Lecture * Modular Learning, Neural Nets and Backprop Topics: : Backprop, modular models Reading Material: Gradient-Based Learning Applied to Document Recognition (Y. 2014-02-05 Lab * Clement Farabet's tutorial on the Torch ML library Video (audio seems broken) another Torch tutorial Video (from 2013, this one with audio).

Week 3 2014-02-10 Lecture * Mixture of experts, recurrent nets, intro to ConvNets 2014-02-12 Lab Week 4. Where to Learn Deep Learning – Courses, Tutorials, Software. Deep Learning is a very hot Machine Learning techniques which has been achieving remarkable results recently. We give a list of free resources for learning and using Deep Learning. By Gregory Piatetsky, @kdnuggets, May 26, 2014. Deep Learning is a very hot area of Machine Learning Research, with many remarkable recent successes, such as 97.5% accuracy on face recognition, nearly perfect German traffic sign recognition, or even Dogs vs Cats image recognition with 98.9% accuracy. Many winning entries in recent Kaggle Data Science competitions have used Deep Learning. The term "deep learning" refers to the method of training multi-layered neural networks, and became popular after papers by Geoffrey Hinton and his co-workers which showed a fast way to train such networks. Yann LeCun, a student of Geoff Hinton, also developed a very effective algorithm for deep learning, called ConvNet, which was successfully used in late 80-s and early 90-s for automatic reading of amounts on bank checks.

Deep Learning - Where to Learn Deep Learning – Courses, Tutorials, Software.