The last links are not official academic papers, but they are quite good resources on deep learning.
Deep Learning Summer School, Montreal 2016 is aimed at graduate students and industrial engineers and researchers who already have some basic knowledge of machine learning (and possibly but not necessarily of deep learning) and wish to learn more about this rapidly growing field of research. If that is you, there are plenty of videos to help you learn more.
Want to learn about data science and the Internet of Things (IoT)? Futuretext is about to start Data Science for Internet of Things. It is a course aimed at people looking to learn the topics and transition into IoT and data science careers. Here are some quick highlights about the course.
- Starts Mid March 2016 and lasts through December 2016
- Personalized Course
- Available Online
Below is a list of topics.
- Data Science
- Machine Learning
- Data Science for IoT methodology
- Deep Learning
Do check out Data Science for Internet of Things for more details.
I was honored to be able to provide the data science introductory article for the Special Data issue of AL MAGNET magazine. The article is titled, Data Science and the Essential Terms. It provides a description of data science and an example workflow. It also points out some of the key terms in data science and what they mean. The closing describes why now is the time to learn data science.
The magazine is open-access, so you can freely read and share the article. Thank you to the AL MAGNET team for the invitation.
Nando de Freitas taught a deep learning course at the University of Oxford. All of the videos are freely available. The playlist is a bit out of order, but starting with Lecture 1 is probably the best technique.
Yoshua Bengio, Ian Goodfellow and Aaron Courville are writing a deep learning book for MIT Press. The book is not yet complete, but the drafts of the chapters are all available online. The authors are also collecting comments about the chapters before the book goes to press.
The book is broken into 3 sections:
- Math and Machine Learning Fundamentals
- Modern Deep Neural Networks
- Current Research in Deep Learning
The book is very technical and probably suitable for a graduate level course. However, if you have the time and interest, resources such as this are highly valuable.
If you are based near San Francisco and interested in machine learning, the Next.ML conference is going on this weekend, January 17, 2015. The conference is a bunch of workshops covering the latest trends in:
- DEEP LEARNING
- PROBABILISTIC PROGRAMMING
- PARALLEL LEARNING
- OTHER MACHINE LEARNING TOPICS AND TOOLS
The lineup of speakers is great, coming from places like MIT, Facebook, Stanford, Domino Data Labs, and others. Bring your laptop because all participants will leave the conference with lots of great software and datasets.
Note: If you would like to attend the conference, you can use the coupon code “media” to save 30% off the conference admission.
Here are some great resources to kickstart your deep learning.
Geoffrey Hinton recently did a Reddit AMA (Ask Me Anything). It contains some good information related to neural networks and deep learning.
Also, below is a lengthy video of a distinguished lecture Geoffrey gave at the Toyota Technological Institute in Chicago.
Deep Learning is the hottest topic in all of data science right now. Adam Gibson, cofounder of Blix.io, has created an open source deep learning library for Java named DeepLearning4j. For those curious, DeepLearning4j is open sourced on github.
Below is a video of Adam introducing deep learning and DeepLearning4j. Also, if you are interested in learning more about deep learning. Here are a couple more very help links.