I'm starting to tinker with Torch and I've come across some difficulties in figuring out how to combine layers with the'rnn' and'nn' libraries. I've spent a couple of days on this and I wasn't sure where else I could post to get some answers, but I apologize in advance is this isn't the appropriate place for Torch Questions (I've also posted on Stack Overflow). I have both text and non-text data, so I'd like to combine an LSTM layer (for the text data) and a hidden layer from an MLP (for the non-text data) into a larger MLP to predict my outcome. Here's where things go wrong, the last line fails, so my hack was to combine the predictions after piping it through the first two models, which is what I do below. I'm not sure where to go from here so any help from all you would be sincerely appreciated.

Well, point 1 and point 2 are hard to simultaneously satisfy, since doing reinforcement learning at scale is pretty much a research topic on its own. Reasoning: the number of decisions it has to make during a game is small, so reinforcement learning should work pretty well. There's a couple of levels of refinement you could try to extend the project - basic model is'given game state, how do I bid?'; first extension is'given game sequence, how do I bid?'; second extension is'given a sequence of previous games with these particular players, how do I bid?'.

Britons are terrified of being enveloped by a dystopian future in which robots take control of society, research has found. More than a third of people fear the rise of artificial intelligence (AI) could lead to robots evolving beyond our understanding and taking over. And around 40% of us think so-called humanoids could eventually destroy humanity as we know it - concerns echoed by Professor Stephen Hawking and Elon Musk, founder of the SpaceX programme who has previously described AI as mankind's "biggest existential threat". Our paranoia about androids was revealed in studies ahead of the launch of Westworld, a new Sky Atlantic programme starting on Tuesday in which guests at a futuristic park based in the Old West live out their wildest fantasies. While the new series, produced by JJ Abrams - the man behind Star Wars: The Force Awakens, Star Trek and Cloverfield - is pure fantasy, our fears of robots becoming the supreme beings on Earth are very real.

Hi All, I would like to guidance on Machine Learning for text prediction. I have large amount of data, around 10GB of SNMP traps weekly coming through SNMPTT and manipulated and exported to multiple databases, sql, elasticsearch etc.. and wondering what framework the group would recommend I spend my time on for SNMP trap prediction.

I am a self taught coder for the past 6 months. When I apply for jobs, I keep getting rejected as I was told i have no portfolio to show and i dont have a software engineering background so why hire me when they can hire an experienced software engineer and train him on machine learning skills. I am getting depressed over this situation over the past few weeks and considering looking for jobs in my previous non-tech related job field. So kindly, please provide me with an interesting project that can allow me to showcase my skills. I am also willing to do free projects for anyone out there as long im guided on the best practices.

Media attending the event included BBC News, The Guardian, The Wall Street Journal, Bloomberg, VentureBeat, Digital Trends, Financial Times, Ars Technica and more. News coverage focused on a range of topics, exploring advancements in robotics, chatbot personalities, machine vision for understanding differences in language and culture, as well as startup acquisitions and funding. We've shared just a few of the great articles from the summit below. Why Data is the New Coal - The Guardian Deep learning needs to become more efficient if it is going to move from using data to categorise images of cats to diagnosing rare illnesses. Alex Hern reports on revelations in this area from speaker Neil Lawrence, the newly appointed Senior Principal Scientist at Amazon.

I see, well I'm trying to achieve (in regards to the project) exactly what I said before. The title of the project is "Reusable Machine Learning components" and I'm aiming to implement the algorithms above. As long as it works it's fine. What I'm trying to get out of it personally, is to deepen my understanding of some of the common machine learning algorithms and learn as much as I can in the process (plus get a good mark). Also if I could learn skills that mimic what would be done in the industry.

Does anyone know of experiments with using y in multiple locations in a neural network (using back-propagation)? Might it not be another solution for short-term vs long-term issue? E.g. some nodes are allowed short access to y and some are having a larger distance to y, generating more complex features.

Josh Fischel, the founder of last weekend's inaugural Music Tastes Good Festival in Long Beach, died Thursday afternoon of liver disease, according to festival organizers. The news came as a shock to the Long Beach and Southern California music communities, who just days ago saw Fischel overseeing the culmination of a life's work in local music. Though family and festival organizers knew he had been sick, no one knew how rapidly his disease would progress after the festival. Music Tastes Good was a three-day event in downtown Long Beach headlined by the likes of the Specials, Warpaint and the Squeeze, among many others. "He couldn't go more than a few feet in his golf cart without somebody stopping him to say'Hey, Josh!' " said Jon Halperin, the talent buyer and co-promoter of Music Tastes Good.

Will "Miss Peregrine's Home for Peculiar Children" have a fairy-tale ending at the box office? While its final chapter has yet to be written, Tim Burton's fantasy film is earning pretty good grades at the multiplex so far. The picture about a group of extraordinary children collected 9 million on Friday, according to an estimate from distributor 20th Century Fox. That means the movie is on track to gross around 27 million by weekend's end -- a so-so start, considering the picture cost the studio 110 million to make. The weekend's other big debut, "Deepwater Horizon," lagged slightly behind in ticket sales Friday, with 7.1 million.