Siraj Raval explains how neuroscience and machine learning are related as well as how the two fields will help each other advance.
In case you’re curious about how Convolutional Neural Networks work in regards to image recognition, but were scared off by the math, then here’s the video for you.
Last week, I blogged a video by CGP Grey about how machines really learn. While the explanation was accurate, succinct, and beautiful it didn’t cover all the ways that a machine could learn.
This video explains a few more ways that machines learn.
Two Minute Papers has a great video explaining the “Distilling a Neural Network Into a Soft Decision Tree” paper.
What’s actually happening to a neural network as it learns and what does back-propagation have to do with it?
Siraj Raval covers the latest happenings on the latest research on neural networks, including a potential replacement to Convolutional Neural Networks.
If you’ve heard the term “neural network” and was curious to exactly what it is, then watch this explainer video on the topic.
Tensorflow, Google’s framework for machine learning and neural networks , has recently been open-sourced.
With this new tool, deep machine learning transitions from an area of research into the mainstream of software engineering.
In this session, Martin Görner teaches you how to pick the correct neural network type for your problem and how to make it behave.
A PhD or familiarity with differential equations is no longer required.
Computerphile interviews Aaron Jackson from the Computer Vision Laboratory at University of Nottingham, who talks about his work on converting a single 2D photo into a 3D model of your face.
Is it magic? No, it’s Convolutional Neural Networks at work.