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Data Science, AI, Machine Learning Cheat Sheets
A curated set of resources for artificial intelligence (AI), machine learning, data science, big data, internet of things (IoT), and more.   Python Pandas Cheat Sheet – Python for Data Science NumPy Cheat Sheet – Python for Data Science Bokeh Cheat Sheet – DataCamp PySpark Cheat Sheet: Spark in... Read more
Introduction Link to Part 1 Link to Part 2 In this post, we’ll go into summarizing a lot of the new and important developments in the field of computer vision and convolutional neural networks. We’ll look at some of the most important papers that have been published over the... Read more
36 Questions to Ask Your Chatbot
Teaching the Art of Conversation to Chatbots Sure, you’ve asked Siri for a nearby restaurant. Maybe you’ve had M make a reservation for you. You may have even kicked back a beer with Untappd. But how well do you really know the bots in your life? Every day, I seem to... Read more
An Overview of Multi-Task Learning in Deep Neural Networks
Note: If you are looking for a review paper, this blog post is also available as an article on arXiv. Table of contents: Introduction Motivation Two MTL methods for Deep Learning Hard parameter sharing Soft parameter sharing Why does MTL work? Implicit data augmentation Attention focusing Eavesdropping Representation bias Regularization... Read more
Intro. How I Plan to Teach Myself Deep Learning Using Only Free Resources Learning Deep Learning Series Part 1: Videos Learning Deep Learning Part 2: Online Courses Learning Deep Learning Part 3: Github Repos We’re already familiar with the popularity of big data of the past five or so... Read more
Building a Chatbot: analysis & limitations of modern platforms
The TryoLabs R&D department is in the process of releasing a very interesting AI open source toolkit oriented towards image processing, utilizing state of the art Deep Learning technologies and build upon TensorFlow and Sonnet. Come see Javier Rey, Research Engineer and Alan Descoins, CTO from TryoLabs Speak at ODSC... Read more
Deep Learning Research Review Week 2: Reinforcement Learning
This is the 2nd installment of a new series called Deep Learning Research Review. Every couple weeks or so, I’ll be summarizing and explaining research papers in specific subfields of deep learning. This week focuses on Reinforcement Learning. Last time was Generative Adversarial Networks ICYMI Introduction to Reinforcement Learning... Read more
Artificial Neural Networks (ANN) Introduction
Training a Computer to Recognize your Handwriting Take a look at the picture below above and try to identify what it is. One should be able to tell that it is a giraffe, despite it being strangely fat. We recognize images and objects instantly, even if these images are... Read more
Can neural networks solve any problem?
Visualizing the Universal Approximation Theorem At some point in your deep learning journey you probably came across the Universal Approximation Theorem. A feedforward network with a single layer is sufficient to represent any function, but the layer may be infeasibly large and may fail to learn and generalize correctly.... Read more
Learning in Brains and Machines (1): Temporal Differences
We all make mistakes, and as is often said, only then can we learn. Our mistakes allow us to gain insight, and the ability to make better judgements and fewer mistakes in future. In their influential paper, the neuroscientists Robert Rescorla and Allan Wagner put this more succinctly, ‘organisms only learn when... Read more