Tag Archives: deep learning

Reinforcement Learning

Simply stated: Reinforcement Learning deals with actions and rewards (positive or negative). The rewards help to dictate the future actions.

Many children learn via reinforcement learning. Here is a simple example from my childhood. As a child, I was told not to touch the hot stove.

My action: I touched the door of the hot stove when it was open.

My reward (negative): I burned my hand, and it hurt.

My future actions resulted in me staying away from the hot stove.

This was a very simplified example of reinforcement learning. Here are two more great introductory references:

  1. Simple Beginner’s guide to Reinforcement Learning
  2. Deep Learning Research Review: Reinforcement Learning

Conversations with future data scientists (YouTube Playlist)

Last week I spent some time chatting with future data scientists. I set up a camera to record some of the answers. Below are a few of the questions addressed.

  • How did I transition to data science?
  • Why start a data science project?
  • Should a new person focus on machine learning or deep learning?
  • What is an example data science project?
  • Why is real-time important?

Hopefully the videos and answers are helpful to others. Enjoy! And I kept most of the videos fairly short. If you enjoy the videos, please subscribe to the YouTube channel, Learn Data Science. Also, if you have a question you would like answered, please leave a comment below.

Deep Learning Coursera Specialization

Andrew Ng, co-founder of Coursera and Deep Learning Expert, is launching a new specialization on Coursera. Details can be found at DeepLearning.ai or the Deep Learning Specialization Page. The specialization consists of 5 courses. They are free to audit and watch the videos. There is a fee to get graded assignments and receive a certificate of completion. The first course just started this week, so it is great time to start learning some deep learning.

Good Luck!

Deep Learning Research Paper Lists for Summer 2017

The last links are not official academic papers, but they are quite good resources on deep learning.

Deep Learning Summer School 2016 Videos

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.

Data Science and IoT Course

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
  • IoT
  • Machine Learning
  • Spark
  • Data Science for IoT methodology
  • Deep Learning

Do check out Data Science for Internet of Things for more details.

Data Science and the Essential Terms

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.

Deep Learning in 2015 at Oxford

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.

Free Deep Learning Book

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:

  1. Math and Machine Learning Fundamentals
  2. Modern Deep Neural Networks
  3. 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.

Next.ML Machine Learning Conference

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
  • JULIA
  • 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.