In this post you will discover how you can check-point your deep learning models during training in Python using the Keras library. When training deep learning models, the checkpoint is the weights of the model. Checkpointing is setup to save the network weights only when there is an improvement in classification accuracy on the validation dataset (monitor'val_acc' and mode'max'). In this post you have discovered the importance of checkpointing deep learning models for long training runs.

Deep neural networks and Deep Learning are powerful and popular algorithms. And a lot of their success lays in the careful design of the neural network architecture. I wanted to revisit the history of neural network design in the last few years and in the context of Deep Learning. It is the year 1994, and this is one of the very first convolutional neural networks, and what propelled the field of Deep Learning. This pioneering work by Yann LeCun was named LeNet5 after many previous successful iterations since they year 1988! The LeNet5 architecture was fundamental, in particular the insight that image features are distributed across the entire image, and convolutions with learnable parameters are an effective way to extract similar features at multiple location with few parameters. At the time there was no GPU to help training, and even CPUs were slow.

Deep learning models can take hours, days or even weeks to train. If the run is stopped unexpectedly, you can lose a lot of work. In this post you will discover how you can check-point your deep learning models during training in Python using the Keras library. How to Check-Point Deep Learning Models in Keras Photo by saragoldsmith, some rights reserved. Application checkpointing is a fault tolerance technique for long running processes.

The Department of Physics is looking to recruit a Research Associate in Computational Modelling and Materials Data Generation, Curation and Inference. The work will involve data conversion, selection and compression, to develop "Big Data Analytics" protocols for structure and property prediction via machine learning algorithms. This post will be Fixed Term 3 months. This is a Full-time – 100 % full time equivalent. The salary will be paid at Grade 6, 32,600 to 38,896 per annum, plus 2,323 per annum London Allowance.

Apple is stepping up its artificial intelligence efforts in a bid to keep pace with rivals who have been driving full-throttle down a machine learning-powered AI superhighway, thanks to their liberal attitude to mining user data. Not so Apple, which pitches itself as the lone defender of user privacy in a sea of data-hungry companies. While other data vampires slurp up location information, keyboard behavior and search queries, Apple has turned up its nose at users' information. The company consistently rolls out hardware solutions that make it more difficult for Apple (and hackers, governments and identity thieves) to access your data and has traditionally limited data analysis so it all occurs on the device instead of on Apple's servers. But there are a few sticking points in iOS where Apple needs to know what its users are doing in order to finesse its features, and that presents a problem for a company that puts privacy first.

In the age-old tech struggle between open and controlled systems, Apple has realized that when it comes to artificial intelligence, it needs to edge toward open. The computer giant has announced it will be opening up its digital assistant Siri to third-party apps and at the same time has put out an API to its artificial intelligence technology. Realistically, the company has been given little choice: Amazon's Alexa has taken off, in large part due to it opening up to other companies, and Google's artificial intelligence systems have streaked ahead of Apple and Siri because when it comes to such a complex and wide-ranging interplay of information and action, broader is better. Where Siri was once a wonder – it worked where other systems didn't – it risks becoming an also-ran, with only Apple fanbois crowding round it in excitement at the latest nerd joke. Not that Apple is taking the news well.

Bayesian models are rooted in Bayesian statistics, and easily benefit from the vast literature in the field. In contrast, deep learning lacks a solid mathematical grounding. Instead, empirical developments in deep learning are often justified by metaphors, evading the unexplained principles at play. These two fields are perceived as fairly antipodal to each other in their respective communities. It is perhaps astonishing then that most modern deep learning models can be cast as performing approximate inference in a Bayesian setting. The implications of this statement are profound: we can use the rich Bayesian statistics literature with deep learning models, explain away many of the curiosities with these, combine results from deep learning into Bayesian modelling, and much more.

The market research firm expects the attach rate of AI-based systems in new vehicles to increase from 8 percent in 2015 (the vast majority of today's AI systems in cars are focused on speech recognition) to 109% in 2025. IHS sees multiple AI systems of various types to be installed in many cars. In the human-machine interface in vehicles, IHS believes AI will play a role in speech and gesture recognition, eye-tracking, driver monitoring and natural language interfaces. In the autonomous car, AI will advance machine vision systems, while it will also migrate in sensor fusion electronic control units (ECU). In a phone interview with EE Times, Luca De Ambroggi, principal analyst, automotive semiconductors at IHS told us, "AI is viewed as a key enabler for real autonomous vehicles. Everyone in the automotive supply chain is getting pretty bullish."

As the Director of Data Science for an advanced analytics company, Sean brings machine learning automation into business applications to help organizations build core strategies around data. Having worked across diverse industries, and alongside many talented professionals, Sean has seen the blend of approaches required to successfully convert raw data into real world value. Sean holds his doctorate in scientific computing where he used advanced mathematics, parallel computing and optimization to solve challenges in nanotechnology, chemistry and renewable energy. After completing his Ph.D. Sean started his own Data Science consulting practice, helping companies automate decision-making and uncover the underlying patterns that drive business environments. Sean has since worked for global consulting firms and silicon valley startups to help bring the advances in machine learning to business applications.

If you write marketing or advertising text for a living, you may want to get a second job skill. That's because software that writes text is here, and it is tackling a growing list of assignments. Several companies offer software that regularly churns out thousands of stories and reports based on structured data, like financial results. Ads that literally write themselves emerged last week, as IBM announced a new service based on its Watson supercomputer. A program called Quakebot has generated earthquake stories for the LA Times.