This book is available free in .PDF format via the link above, and the site offers links to all the lab code. Written by professors at USC, Stanford and the University of Washington and focused on R -- the language of statistical computing that is often used for machine learning and AI programs in this area -- the book has been described as "the'how to' manual for statistical learning." Once you're done with this book, move on to the authors' follow-up, " The Elements of Statistical Learning," also available for free online (although both can be purchased, as well).

Understanding hidden memories of recurrent neural networks Ming et al., VAST'17 Last week we looked at CORALS, winner of round 9 of the Yelp dataset challenge. Today's paper choice was a winner in round 10. We're used to visualisations of CNNs, which give interpretations of what is being learned in the hidden layers. But the inner workings of Recurrent Neural Networks (RNNs) have remained something of a mystery. RNNvis is a tool for visualising and exploring RNN models.

Salesforce Artificial Intelligence, Data Science & Data Discovery with Clicks Instead of Code / Salesforce Einstein AI This course is for the absolute beginner to Artificial Intelligence (AI), Machine Learning, Deep Learning, and Data Science. If you are feeling overwhelmed by either the tsunami of data that you are tasked with trying to make sense out of, or overwhelmed by the tsunami of media coverage around Artificial Intelligence, Deep Learning, Data Science, and Machine Learning, I am here to share a competitive advantage. There is an AI and Data Discovery platform that can be constructed and configured with clicks instead of code. The disruptive power of this is that Artificial Intelligence is now available to the masses, and not just to the quants and data scientists among us. You can now not only catch the competition, but leap frog past them, by leveraging Salesforce Einstein as your On-Demand Data Scientist.

In this tutorial, you will learn how to colorize black and white images using OpenCV, Deep Learning, and Python. Image colorization is the process of taking an input grayscale (black and white) image and then producing an output colorized image that represents the semantic colors and tones of the input (for example, an ocean on a clear sunny day must be plausibly "blue" -- it can't be colored "hot pink" by the model). The novel approach we are going to use here today instead relies on deep learning. We will utilize a Convolutional Neural Network capable of colorizing black and white images with results that can even "fool" humans! To learn how to perform black and white image coloration with OpenCV, just keep reading!

If you're wondering which of the growing suite of programming language libraries and tools are a good choice for implementing machine-learning models then help is at hand. More than 1,300 people mainly working in the tech, finance and healthcare revealed which machine-learning technologies they use at their firms, in a new O'Reilly survey. The list is a mix of software frameworks and libraries for data science favorite Python, big data platforms, and cloud-based services that handle each stage of the machine-learning pipeline. Most firms are still at the evaluation stage when it comes to using machine learning, or AI as the report refers to it, and the most common tools being implemented were those for'model visualization' and'automated model search and hyperparameter tuning'. Unsurprisingly, the most common form of ML being used was supervised learning, where a machine-learning model is trained using large amounts of labelled data.

The new MOBOTIX camera platform, jointly developed with Konica Minolta, is based on the distributed intelligence in our camera system and is crucial for artificial intelligence and at the same time the key for the communication of our products with other sensors and devices in the network to enable "Beyond Human Vision" solutions. The state-of-the-art analysis methods located on the camera itself helps users increase process efficiency and develop new business and revenue models. Future software updates will enable completely new functions based on deep learning methods, such as the recognition of human behavior, moods or voices. In order to make this possible, a so-called "plug-in concept" is being further developed that goes far beyond what is available on competing products. This allows customers to easily create new features similar to developing apps for mobile devices while taking full advantage of the system's performance, including full CPU and GPU power and programmable logic.

LAST week, the OpenAI research group announced it had created an artificial intelligence capable of generating hundreds of words of convincing text on almost any topic (see Fears of OpenAI's super-trolling artificial intelligence are overblown). But the group said it wouldn't be releasing the AI, because of its potential to be used as a fake news generator. Fear over the power of fake news is widespread. Damian Collins, who heads a committee of UK MPs looking into the matter, this week proclaimed that "democracy is at risk from the malicious and relentless targeting of citizens with …

Elon Musk, recently busying himself with calling people "pedo" on Twitter and potentially violating US securities law with what was perhaps just a joke about weed – both perfectly normal activities – is now involved in a move to terrify us all. The non-profit he backs, OpenAI, has developed an AI system so good it had me quaking in my trainers when it was fed an article of mine and wrote an extension of it that was a perfect act of journalistic ventriloquism. As my colleague Alex Hern wrote yesterday: "The system [GPT2] is pushing the boundaries of what was thought possible, both in terms of the quality of the output, and the wide variety of potential uses." GPT2 is so efficient that the full research is not being released publicly yet because of the risk of misuse. And that's the thing – this AI has the potential to absolutely devastate.

An exciting deep learning start-up is looking for a VP of Artificial Intelligence to lead its entire machine learning group. The company has strong backing from a leading VC and a product that is set to have a huge impact on the deep learning hardware space. The position will combine research, engineering, management and play a key role in taking the product to market. Get in touch and we'll let you know