TensorFlow Meets Chip Huyen (@chipro), author and instructor of the TensorFlow for Deep Learning class at Stanford University: https://goo.gl/rNb6PW. In this video, she discusses the class, her journey from writing travel stories, to studying computer science, to now teaching students about deep learning at Stanford University!
MIT’s Alexander Amini delivers this lecture as part of their Introduction to Deep Learning 6.S191 class.
Many of the concepts behind deep learning were initially developed in the 1980s, yet it’s only been in the last few years that deep learning has really taken off. This video by ColdFusion explores why that is the case.
Another great video from DeepLearning.TV.
This video, a follow up from his intro video on reinforcement learning, Arxiv dives into three advanced papers that address the problem of the sparse reward setting in Deep Reinforcement Learning and pose interesting research directions for mastering unsupervised learning in autonomous agents.
Machine learning models, especially deep learning ones, can be complex.
In this video from QCon.ai 2018, Chi Zeng walks us through how to debug, monitor, and examine the decisions of a TensorFlow-based model using the TensorBoard suite of visualizations.
Chi Zeng works on the TensorBoard suite of visualizations within Google Brain.
Siraj Raval promises to teach you Deep Learning in 6 weeks. Given his track record, it’s safe to say he can pull it off.
All neural networks use activation functions, but the reasons behind using them are never clear!
In this video, the great Siraj Raval discusses what activation functions are, when they should be used, and what the difference between them is.
In this episode of the AI Show, Micheleen Harris dives into when and why one would use deep learning over classical machine learning.
While many tasks can be performed cheaply and well with classical machine learning and packages like scikit-learn, every once in a while, a task is better suited for a neural network architecture implemented with deep learning methods – e.g. large amounts of data or insufficient accuracy with other methods. Watch to find out more and hear about some Python packages to make life easier.
Sam Witteveen, Machine Learning Developer Expert Google, speaks at a recent conference in Singapore about the strategies on how to get good at deep learning rapidly.