In this video, learn about the TensorFlow.js ecosystem: how to bring an existing ML model into your JS app and re-train the model using your data. We’ll also go over our efforts beyond the browser to bring ML to platforms such as React Native, Raspberry Pi, and Electron.
For more information, check out the accompanying blog post.
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)
David Bau, a MIT-IBM Watson AI lab research team member, explains how computers show evidence of learning the structure of the physical world.
In this episode of BlockTalk, watch a discussion on Bitcoin and Lightning technology with Samson Mow, the CSO of BlockStream. They cover Bitcoin and Lightning technology as well dive into how companies like BlockStream are innovating on top of the Bitcoin protocol.