This group is for those interested in the various types of Machine Learning from theory to application. Including but not limited to Hierarchical Temporal Memory, Artificial Neural Networks, Natural Language Processing, Artificial Intelligence and their theorems, techniques and algorithms. We will have guest speakers, discuss books and explore which machines (programs), ML Libraries and systems are best suited for particular jobs. Sharing new ideas, best practices, and resources so we can all design, develop and deploy solutions for a better tomorrow.

Have you noticed how computers have gotten smaller while getting smarter? They've also gotten cheaper: now there's a computer inside anything with an on/off switch. All of these newly intelligent devices -- toasters to toothbrushes, thermostats and lightbulbs and cars -- are now being networked, talking to each other, and businesses, and consumers. Why shouldn't your car tell your house that you're nearly home so that the house can tell the oven to preheat to the proper temperature for that fish it knows you just bought, because you made the purchase with your phone and your phone told it so? So, behind every device is a customer, and the next generation of customers expect a connected, smart experience.

It takes years, decades even, for physicians to refine the expertise required to notice details that remain invisible to the untrained. This aptitude, depending on a doctor's specialty, might mark the difference between an oncologist knowing a malignant tumor from a benign cyst. It can help a cardiologist determine the velocity of blood as it flows through a hole in the heart. Or it may tell a reconstructive plastic surgeon whether a severe burn is healing nicely or at risk of infection. None of this is easy unless you know how to see in a certain way.

Your speech-recognizing friends at Nuance are back with a major update to their flagship app, Dragon. The popular productivity software is now in its 15th version, an update that promises some substantial improvements in accuracy courtesy of the company's own deep learning tech, which forms the basis of its speech engine. According to Nuance, this latest upgrade brings better accuracy "upwards of 24 percent," with improved ability to recognize and learn accents and voice patterns, while adapting to the acoustics of the speaker's environment. Here's a quote from the company's CTO, "Training such Deep Neural Net models typically requires large amounts of training data and a high-performance computing environment. However, our new Dragon portfolio includes our latest breakthrough that allows Dragon's Deep Neural Nets to continuously learn from the user's speech during use on a standard personal computer, and drive accuracy rates in some instances up to 24 percent higher." Version 15 also brings a simplified UI, formatting improvements, more support for audio transcription and optimization for a number of touchscreen Windows PCs.

With the trends in personal computing favoring software that gets to know its users, the newest version of Nuance's Dragon voice dictation suite of software--Dragon 15, announced Tuesday--is right on track. Dragon 15--including Dragon Professional Individual ( 300), Dragon Professional Individual for Mac ( 300), Dragon Legal ( 500), and Nuance Dragon Anywhere (free to install; subscriptions run 15/month or 150/year)--is based on Nuance's new machine-learning technology. The company claims that this technology has improved recognition accuracy by at least 24 percent, thanks to its algorithm that learns your distinctive speech patterns over time combined with an improved capability to pick out speech from a noisy room. Lawyers may buy the expanded Legal version, which is trained using a legal vocabulary of more than 400 million words, according to the company. But the majority of Nuance's customers will probably invest in the Dragon Professional Individual version, which is designed for a more general vocabulary.

Machine learning is at the peak of the hype scale, but travel search giant Expedia has been building its core business on the technology for the best part of a decade. Expedia has grown far beyond a search engine for flights – it's now the parent company of a dozen travel brands including Trivago and Hotels.com The business of delivering quality flight search results is tough, and Fleischman describes it as an "unbounded computer science problem". The reason for this is because flight itineraries and schedules are constantly changing, and Expedia's proprietary'best fare search' (BFS) has to'learn' and adapt all the time. The extent of the problem can be summed up by one statistic.

This is a recursive neural network (LSTM type) with 3 layers of 680 neurons each, trying to find patterns in audio and reproduce them as well as it can. It's not a particularly big network considering the complexity and size of the data, mostly due to computing constraints, which makes me even more impressed with what it managed to do. The audio that the network was learning from is voice actress Kanematsu Yuka voicing Hinata from Pure Pure. I used 11025 Hz, 8-bit audio because sound files get big quickly, at least compared to text files - 10 minutes already runs to 6.29MB, while that much plain text would take weeks or months for a human to read. I wrote a program that converts any data into UTF-8 text and vice-versa, and to my excitement, torch-rnn happily processed that text as if there was nothing unusual.

Sometimes an app is so just right for its moment in history that it hits everyone between the eyes and lingers in the limelight. That's what is happening with Prisma (free, iTunes Store link), a free photo/art app for iPhone that touts special effects image "filters" that render the painterly styles of famous artists like Van Gogh, Picasso, Lichtenstein, Mondrian, Kandinsky, and others. The buzz around Prisma and the fascination it has generated persists because the app is super simple to use and yields an endless variety of genuinely unique results. Each style is applied differently in response to the unique patterns in the picture. Use of neural networks for art, which involves interpreting images in layers, is not brand a new concept.

In this project, we will be building an autonomous rc car using supervised learning of a neural network with a single hidden layer. We will use a remote-controlled car with a Raspberry Pi and a Raspberry Pi camera module mounted on top. In the training mode, the camera module would provide images needed to train the neural network and in the autonomous mode; would provide the images to the trained model to predict the movements and direction of the car. You can find the Github repository for this project here. We will need a remote controlled car, a Raspberry Pi, a power bank, an L293D Motor Driver IC and some jumper wires to connect the circuits.

Learn from OpenAI CTO Greg Brockman on how NVIDIA DGX-1 has the power to create impactful AI technologies and achieve unimaginable breakthroughs.