Understanding Neural Network Bias Values
In my other articles, I have discussed the many different neural network hyper parameters that contribute to optimal success. While hyper parameters are crucial  for training successful algorithms, the importance of neural network bias values are not to be forgotten as well. In this article I’ll delve into the... Read more
An Infinite Parade of Giraffes: Collaborative Cartooning with AI
What is recognizable about a particular artist’s style? What parts can be delegated to an assistant? Can AI play the role of assistant or even collaborator? How would we ever get enough data for training? How little data could we get away with? Exploring these questions using GANs, image... Read more
Pervasive Simulator Misuse with Reinforcement Learning
The surge of interest in reinforcement learning is great fun, but I often see confused choices in applying RL algorithms to solve problems. There are two purposes for which you might use a world simulator in reinforcement learning: Reinforcement Learning Research: You might be interested in creating reinforcement learning algorithms for... Read more
To solve machine learning problems, there is a wide range of different techniques and methods required, some suited better than others. As a data scientist it can be difficult to encapsulate all of them, and choose which work best for specific scenarios. If one is starting out in this... Read more
There are many different algorithms used to train a neural network, and many variations of each. In this article, I am going to outline five algorithms that will give you an all-rounded understanding of how a neural network works. I will start with an overview of how a neural... Read more
Requests for Research
Table of contents: Task-independent data augmentation for NLP Few-shot learning for NLP Transfer learning for NLP Multi-task learning Cross-lingual learning Task-independent architecture improvements It can be hard to find compelling topics to work on and know what questions are interesting to ask when you are just starting as a... Read more
Let’s start with machine learning In short, machine learning algorithms are algorithms that learn (often predictive) models from data. I.e., instead of formulating “rules” manually, a machine learning algorithm will learn the model for you. So, let me give you an example to illustrate what that means! Say you... Read more
Thanks a lot to @aerinykim, @suzatweet and @hardmaru for the useful feedback! The academic Deep Learning research community has largely stayed away from the financial markets. Maybe that’s because the finance industry has a bad reputation, the problem doesn’t seem interesting from a research perspective, or because data is difficult and expensive to obtain.... Read more
Natural and Artificial Intelligence
How are we making computers do the things we used to associated only with humans? Have we made a breakthrough in understanding human intelligence? While recent achievements might give the sense that the answer is yes, the short answer is that we are nowhere near. All we’ve achieved for... Read more
Failure to replicate Schwartz-Ziv and Tishby
Opening the Black Box of Deep Neural Networks via Information didn’t appear at any conferences, to my knowledge, but it still built up some buzz. It has been difficult to replicate, for both bloggers and academics. I attempted to replicate some aspects, and emailed the authors with the message below in an attempt to... Read more