In this tutorial, you will learn the three primary reasons your validation loss may be lower than your training loss when training your own custom deep neural networks. I first became interested in studying machine learning and neural networks in late high school. Back then there weren't many accessible machine learning libraries -- and there certainly was no scikit-learn. Every school day at 2:35 PM I would leave high school, hop on the bus home, and within 15 minutes I would be in front of my laptop, studying machine learning, and attempting to implement various algorithms by hand. I rarely stopped for a break, more than occasionally skipping dinner just so I could keep working and studying late into the night.

"I bought the first version of this book, and now also the second. The new version is very comprehensive. If you are using Python - it's almost a reference. I also like the emphasis on neural networks (and TensorFlow) - which (in my view) is where the Python community is heading. I am also planning to use this book in my teaching at Oxford University. The data pre-processing sections are also good. I found the sequence flow slightly unusual - but for an expert level audience, it's not a major issue."

What a time this is to be working in the machine learning field! The last few years have been a dream run for anyone associated with machine learning as there have been a slew of developments and breakthroughs at an unprecedented pace. There's just one thing to keep in mind here – these breakthroughs did not happen overnight. It took years and in some cases, decades, of hard work and persistence. We are used to working with established machine learning algorithms like neural networks and random forest (and so on). We tend to lose sight of the effort it took to make these algorithms mainstream. To actually create them from scratch. The people who lay the groundwork for us – those are the true heroes of machine learning.

We need to get a taste of machine learning before understanding it fully. This segment is made up of three parts. These are not the exact courses I took to learn Python and getting an intro to data science. But they are quite similar and they serve the purpose. This course is about learning to use Python and creating things on your own.

Formula E Racing, like its Formula 1 counterpart, relies on speed and strategy to win. But how do you crunch through the reams of data that you can get from an electric race car and analyze it in a way that would help your driver and your racing team beat the competition? And that's why he has partnered with Sanjay Srivastava, Chief Digital Officer of Genpact, to leverage data analytics and artificial intelligence (AI) to build a multi-layer platform that turns a mountain of data into actionable analysis. Formula E racing produces different types of data across many fronts. There's a set of telemetry data from the cars, a stream of large data sets that cars produce while they are on the road, and data from competing drivers and their vehicles. Then there's data gleaned from weather, satellite, traffic, and road patterns. All that needs a data analytics system that can interpolate the information as it comes in from all these sources and analyze it in real-time in a way that the driver and the racing team can absorb and act upon instantaneously. But, as Sylvain points out, that's easier said than done, especially since a Formula E race happens in just one day, and every second counts. As Sylvain and Sanjay explain, it starts with knowing how to structure the incoming information so that the driver and engineers can act upon it quickly. That means setting up the correct algorithms, developing an analytical infrastructure that--with the help of AI--integrates all of the different types of data, and synchronizing it to give the driver and engineers the whole picture and predict the likeliest outcomes in any given scenario in order to make the right decisions during the race. That also means creating a user interface for the data that's both comprehensive and instantly comprehensible to the driver. The work that Sylvain and Sanjay are doing has notable implications for business that goes beyond racing. The technologies they are developing will trickle down to make electric cars and sustainable energy better. The analytics tools they are creating can potentially be utilized by other companies to make better sense of data coming from multiple sources in order to make well-informed business and digital transformation decisions and do so quickly, and to manage their resources more efficiently. This transcript has been edited for length and clarity. Michael Krigsman: Formula E Racing involves cars, speed, data, and advanced technologies such as AI and machine learning.

According to some interpretations of quantum mechanics, it is, explaining why we can't precisely predict the motions of single particles. In the famous double-slit experiment (which, as Richard Feynman declared, "has in it the heart of quantum mechanics"), we cannot predict where exactly an individual photon passing through two slits will land on the photo-sensitive wall on the other side. But we can make extremely precise predictions of the distribution of multiple particles, suggesting that nature may be deterministic after all. Indeed, we can predict to many decimal places what the distribution of billions of photons shot at the double slit will look like. This dichotomy between unpredictable individual behavior and precise group behavior is not unique to quantum mechanics.

The Develop program is designed for the technical talent and builders of AI to experience first-hand the methods, frameworks, and opportunities associated with the latest tools and products on the market.

WASHINGTON, D.C.--(BUSINESS WIRE)--At the Association for the United States Army (AUSA) conference today, General Micro Systems (GMS) announced that its new S422-SW and X422 combination has been chosen for two new military development programs. The system pair brings a massive amount of server processing power, 10/40/100 Gigabit networking ports for sensors, and general-purpose graphics processing unit (GPGPU) artificial intelligence (AI) onto the battlefield for the first time in two small "shoebox-sized" rugged chassis designed to survive the harshest conditions where regular rackmount servers cannot. The two programs that selected the S422-SW "Thunder" and X422 "Lightning" combo will deploy it in mobile platforms to move IP-based sensor data instantaneously over multi-sensor LANs into the server and AI processor. Once processed, the server reports out to operators information that can help maneuver a vehicle or UAS in real-time, calculate a fire control solution for a weapon, or identify threats such as stationary IEDs or incoming objects such as projectiles. "The tremendous processing power of this combo makes it a highly attractive option for these two development programs as well as others creating autonomous, self-driving or self-piloting vehicles," said Ben Sharfi, chief architect and CEO, General Micro Systems.

I am the co-founder and CEO of Neurala Inc., a Boston-based company building Artificial Intelligence emulating brain function in software. Neurala's deep learning tech makes robots, drones, cars, consumer electronics, toys and smart devices more useful, engaging and autonomous. Neurala stems out of 10 years of research at Boston University Neuromorphics Lab, where as AI Professor I have pioneered the research and fielding of brain-inspired (also called Deep Learning, or Artificial Neural Networks) algorithms that allow robots and drones to perceive, navigate, interact and learn real-time in complex environments. Over my academic and industrial career, I have lectured and spoken at dozens of events and venues, including TEDx, keynote at Mobile World Congress Drone Summit, NASA, the Pentagon, GTC, InterDrone, Los Alamo National Lab, GE, Air Force Research Labs, HP, iRobot, Samsung, LG, Qualcomm, Huawei, Ericsson, BAE Systems, AI World, Mitsubishi, ABB and Accenture, among many others. My work has been featured in TIME, IEEE Spectrum, Fortune, CNBC, The Boston Globe, Xconomy, The Chicago Tribune, TechCrunch, VentureBeat, Nasdaq, Associated Press and many other media.

Keras is a simple-to-use but powerful deep learning library for Python. In this post, we'll build a simple Convolutional Neural Network (CNN) and train it to solve a real problem with Keras. This post is intended for complete beginners to Keras but does assume a basic background knowledge of CNNs. My introduction to Convolutional Neural Networks covers everything you need to know (and more) for this post - read that first if necessary. The full source code is at the end.