How I Used Deep Learning To Train A Chatbot To Talk Like Me (Sorta)

How I Used Deep Lear...

Introduction Chatbots are “computer programs which conduct conversation through auditory or textual methods”. Apple’s Siri, Microsoft’s Cortana, Google Assistant, and Amazon’s Alexa are four of the most popular conversational agents today. They can help you get directions, check the scores of sports games, call people in your address book, and can accidently make you order a $170 dollhouse. These products all […]

Surviving the Impending AI/ML/DS Arms Race

Surviving the Impend...

Overview: In this video you will learn how bring the benefits of machine learning and artificial intelligence to your business. Jeremy explains in 5 simple steps the route to become more AI/ML driven in your company. Learning outcomes: You will learn the difference between Artificial Intelligence, Deep Learning, Machine Learning, Data Science. Your will learn […]

Learning Deep Learning Series Part 1: Videos

Learning Deep Learni...

Last week Open Data Science published an article about how I plan to teach myself deep learning using only free resources and after my first week I’m here to report on my progress and take on the resources I’ve used so far. This piece is specifically about video learning content. I purposefully decided to start […]

Deep Learning Research Review Week 2: Reinforcement Learning

Deep Learning Resear...

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 3 Categories of Machine Learning Before […]

Can neural networks solve any problem?

Can neural networks ...

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. — Ian Goodfellow, DLB This is an […]

Deep Learning Research Review Week 1: Generative Adversarial Nets

Deep Learning Resear...

This week, I’ll be doing 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 I’ll begin with Generative Adversarial Networks.  Introduction According to Yann LeCun, “adversarial training is the coolest thing since sliced bread”. I’m inclined […]

An Introduction to Deep Learning using nolearn

An Introduction to D...

NOTE: If you are having trouble with nolearn working properly, make sure you are using version 0.5b1 available here. Otherwise you may run into problems. One of the most well known problems in machine learning regards how to categorize handwritten numbers automatically. Basically, the idea is that you have 10 different digits (0-9) and you […]

Linear algebra cheat sheet for Deep Learning

Linear algebra cheat...

Beginner’s guide to commonly used operations During Jeremy Howard’s excellent deep learning course I realized I was a little rusty on the prerequisites and my fuzziness was impacting my ability to understand concepts like backpropagation. I decided to put together a few wiki pages on these topics to improve my understanding. Here is a very […]

Automated analysis of High‐content Microscopy data with Deep Learning

Automated analysis o...

    Abstract Existing computational pipelines for quantitative analysis of high‐content microscopy data rely on traditional machine learning approaches that fail to accurately classify more than a single dataset without substantial tuning and training, requiring extensive analysis. Here, we demonstrate that the application of deep learning to biological image data can overcome the pitfalls associated […]