Jabrils developed an app for the 2019 Call for Code global challenge.
You can also submit yours by 7/29 for a chance to win $200,000 USD.
Computerphile’s Rob Miles takes a closer look at GPT-2, the AI deemed “too dangerous” by its creators to release. If you’ve not heard about it, it’s an AI that, given a bit of text to prime it, it continues writing a believable and coherent way.
Code Bullet explores Deep Q Learning by teaching an AI to play Snake.
Lex Fridman sits down the great Kai-Fu Lee to talk about his life and work in AI across the US and China.
In this video, Two Minute Papers takes a closer look at the paper “Scalable Muscle-actuated Human Simulation and Control.”
Think of the medical applications of this technology.
Siraj Raval provides this curated ordered reading list of free books to help anyone learn machine learning efficiently.
From beginner to doing cutting edge research in 10 books, enjoy!
Siraj Raval has designed a free curriculum to help anyone learn Computer Vision in the most efficient way possible.
My curriculum starts off with low level vision techniques and progressively increases in difficulty until we get to high level analysis techniques i.e deep learning. Don’t worry if you’ve never coded before, i’ve included links to help you learn Python as well. Now is the time to build computer vision solutions, the world needs these menial tasks automated to help liberate humans from drudgery. The tools needed are python, OpenCV, and Tensorflow, all of which have their place and I’ll explain all the details of how it fits together in this video. Enjoy!
Two Minute Papers examines the paper “Text-based Editing of Talking-head Video” and how to alter video content by changing text.
Just imagine where AI was just 5 years ago. Sure, neural networks have been around for decades, but they were not practical for the average business problem. Think of all the breakthroughs in machine learning, natural language processing, knowledge graphs, and more just in 2019.
Here’s an interesting report, aptly titled State of AI Report 2019 published on June 28. In it, Benaich and Hogarth embark on a 136-slide long journey on all things AI. From technology breakthroughs and their capabilities to supply, demand and concentration of talent working in the field. There are even special sections on the politics of AI and AI in China.
The report lives up to Benaich’s goals as set in his reply. The first 40 pages of the report, which comes in the shape of a slide deck, are focused on progress in AI research — technology breakthroughs and their capabilities. Key areas covered are reinforcement learning, applications in games and future directions, natural language processing breakthroughs, deep learning in medicine, and AutoML.
The Data Science & AI community has truly embraced open source. In fact, there are so many libraries and tools out there, that it can be challenging to keep up. Fortunately, here’s a great round up of 21 open source tools for Machine Learning. Some of them you may have heard of, but I guarantee there are a few surprises in this list, even for the seasoned expert.
- Presenting 21 open source tools for Machine Learning you might not have come across
- Each open-source tool here adds a different aspect to a data scientist’s repertoire
- Our focus is primarily on tools for five machine learning aspects – for non-programmers(Ludwig, Orange, KNIME), model deployment(CoreML, Tensorflow.js), Big Data(Hadoop, Spark), Computer Vision(SimpleCV), NLP(StanfordNLP), Audio, and Reinforcement Learning(OpenAI Gym)
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Free Data Science Full Course for Beginners
Another 10 Free Must-See Courses for Machine Learning and Data Science
Another 10 Free Must-See Courses for Machine Learning and Data Science
16 Best Resources to Learn AI & Machine Learning in 2019
LinkedIn Open Sources a Tool that Formats Big Data for TensorFlow