- NIPS 2017 Key Points & Summary Notes - Dec 18, 2017.
Third year Ph.D student David Abel, of Brown University, was in attendance at NIP 2017, and he labouriously compiled and formatted a fantastic 43-page set of notes for the rest of us. Get them here.
Bias, Conference, Machine Learning, NeurIPS, NIPS, Reinforcement Learning
- 5 Free Resources for Furthering Your Understanding of Deep Learning - Oct 20, 2017.
This post includes 5 specific video-based options for furthering your understanding of neural networks and deep learning, collectively consisting of many, many hours of insights.
Andrew Ng, Deep Learning, Neural Networks, NIPS, Summer School
- Learning to Learn by Gradient Descent by Gradient Descent - Feb 2, 2017.
What if instead of hand designing an optimising algorithm (function) we learn it instead? That way, by training on the class of problems we’re interested in solving, we can learn an optimum optimiser for the class!
Gradient Descent, Machine Learning, NIPS, Optimization
- Generative Adversarial Networks – Hot Topic in Machine Learning - Jan 3, 2017.
What is Generative Adversarial Networks (GAN) ? A very illustrative explanation of GAN is presented here with simple examples like predicting next frame in video sequence or predicting next word while typing in google search.
Deep Learning, Generative Adversarial Network, Machine Learning, Neural Networks, NIPS
- Embrace the Random: A Case for Randomizing Acceptance of Borderline Papers - May 16, 2016.
A case for using randomization in the selection of borderline academic papers, a particular use case which has parallels with many other possible scenarios.
Academics, ICML, NIPS, Random, Randomization
