TensorFlow is an open source software library for numerical computation using data flow graphs. In other words, the best way to build deep learning models. If you want to contribute to this list (please do), send me a pull request or contact me @jtoy Also, when you noticed that listed repository should be deprecated.

The MNIST database containing 60,000 examples for training and 10,000 examples for testing can be downloaded from LeCun's website. Instead of downloading the database and converting the data to actual images, we can use the helpful library MNIST digits, which creates test- and training sets automatically. If you're using the MNIST digits library, this is checked automatically. To improve the results, the number of elements in the training set and the iterations for the training should be increased.

Neural networks provide the possibility to solve complicated non linear problems. They can be used in various areas such as signal classification, forecasting timeseries and pattern recognition. A neural network is a model inspired by the human brain and consists of multiple connected neurons. For getting a deeper understanding, I recommend checking out Neural Networks and Deep Learning. Within the last years, multiple Javascript frameworks were developed that can help you to create, train and use Neural Networks for different purposes.

If you tell Siri to set an alarm for 5 am, she'll set an alarm for 5 am. But if you start asking her which prescription pain killer is least likely to upset your stomach, she's not really gonna know what to do--just because that's a pretty complicated sentence. Siri is a long way from what computer scientists call "natural language understanding." She can't truly understand the natural way we humans talk--despite the way Apple portrays her in all those TV ads. In fact, we shouldn't really be talking about her as a "her" at all.

The data generated every day is so massive that no imaginable manpower can comprehend it and turn into valuable and applicable insights without the help of smart machines. To give an idea, IDC estimated that the digital universe is doubling its size yearly and would reach 44 ZB in 2020 from 4.4 ZB of data generated in 2013. It also forecasted that the big data technology and services market will grow at a 26.4% compound annual growth rate to 41.5 billion through 2018, or about six times the growth rate of the overall information technology market. The ability to draw insights and the ability to optimally monetize available data would place companies in a unique position challenging established rules and processes.

Google wanted their bots to be smarter and eventually more conversational than the rest of the bots out there. And in order to do that they are technically feeding their AI machines with 2,865 romance novels so they could respond to conversations better. Google has been working on'educating' their AI engines for some time now, "For the past few months, Google has been feeding text like this to an AI engine - all of it taken from steamy romance novels with titles like Unconditional Love, Ignited,Fatal Desire, and Jacked Up" said Buzzfeed. Google believes that by doing so, they could potentially inject some'personality', conversational skills and who knows, maybe even a dash of'compassion' into their artificial intelligence technology. Andrew Dai, Google software engineer said in an interview with Buzzfeed "In the Google app, the responses are very factual...Hopefully with this work, and future work, it can be more conversational, or can have a more varied tone, or style, or register."

Robots play football in a demonstration of artificial intelligence at the stand of the German Research Center for Artificial Intelligence (Deutsches Forschungszentrum fuer Kuenstliche Intelligenz GmbH) at the CeBIT Technology Fair. There is no doubt that artificial intelligence (AI) plays a huge role in technology, education and research. AI is complex, but it is more efficient and reliable than humans when it comes to the workforce because it is fast and it doesn't get tired. According to new reports, AI investment continues to grow. Most of the things we see about AI springs from science fiction movies.

How do humans make an artificial intelligence system better at conversations? One method-in-testing is a project in which a Google AI was force-fed 2,865 romance books, about 1,500 fantasy books, and more. The work was done by researchers with Google Brain, and involved feeding a total of 11,000 unpublished books to the neural network, then testing whether it could take a couple sentences from the book and create its own corresponding phrases. The results sound like cryptic, dark poems. The AI's task wasn't terribly simple - the two sentences were the start and end of a progression the neural network had to make -- a sort of machine-generated poem that had to form a sensible transition between the two sentences.

Wearables might be set to get a whole lot more useful in future if research being conducted by Carnegie Mellon University's Future Interfaces Group is indicative of the direction of travel. While many companies, big and small, have been jumping into the wearables space in recent years, the use-cases for these devices often feels superficial -- with fitness perhaps the most compelling scenario at this nascent stage. Yet smartwatches have far richer potential than merely performing a spot of sweat tracking. The other problem with the current crop of smartwatches is the experience of using apps on wrist-mounted devices does not always live up to the promise of getting stuff done faster or more efficiently. Just having to load an app on this type of supplementary device can feel like an imposition. If the primary selling point of a smartwatch is really convenience/glanceability the watch wearer really does not want to have to be squinting at lots of tiny icons and manually loading data to get the function they need in a given moment. A wearable needs to be a whole lot smarter to make it worth the wearing vs just using a smartphone.

Gel1…..I've been here 6 months, mostly watching and learning. Lots of smart people on the site and I've learned a lot from Phil and many others. I have to thank Phil and the many contruibutors for a 80% profit for 2009. I have learned a lot and am still learning ( even occasionally about political issues - ha!) Peace of mind / I have a portfolio mainly consisting of long term long calls, short term short calls and puts, and long term BCS. Three years, ago when I started my journey on this board I would be freaking out panicking as to what to do, as many of the short calls are ITM, Three years later (today) I look at the screen and serenely process the information. Three years ago, I inevitably made the wrong decisions which cost me a lot of money. Three years on I calmly roll the positions to whatever makes sense. I guess they call that the power of education. Phil, i wanted to thank you again for helping me protect future stock allocations at work - finally, i feel like i am owning my own destiny with stocks vs. letting the market dictate what you get – thanks again. Phil/BCS - Didn't realise they traded here.