Deep learning doesn't take a huge amount of time or computational resources. Nor does it require highly complex code, and in some cases not even a large amount of training data. Curated best practices are now available as libraries that make it easy to plug in and write your own neural network architectures using a minimal amount of code to achieve more than 90% prediction accuracies. The two most popular deep learning libraries are: (1) pytorch created by Facebook (we will be using fastai today, which is built on top of pytorch) and (2) the keras-tensorflow framework created by Google. We will build an image classifier using the Convolutional Neural Network (CNN) model to predict if a given image is that of Drogon or Vicerion (any Game of Thrones fans here in the house?

We are really pleased to announce the date of our second Meetup for the IBM PowerAI Melbourne group. Access to Level 28 requires a pass to activate the lifts after 18:30. Please try to arrive before then. The agenda has not yet been finalised, but will loosely follow: 17:30 - 18:00 - Arrive (& drink a refreshment or two) 18:00 - Intro 18:15 - Dr Adam Makarucha - IBM Systems 18:45 - Ben Swinney - IBM Lab Services - "Using PowerAI Vision inc. Our Presenters.... Dr Adam Makarucha is a data scientist within the IBM Systems team where he is developing deep learning use cases and demonstrations for clients on IBM's PowerAI platform.

The purpose of this post is to provide guidance on which combination of final-layer activation function and loss function should be used in a neural network depending on the business goal. This post assumes that the reader has knowledge of activation functions. Like all machine learning problems, the business goal determines how you should evaluate it's success. If so, you next need to think about how many classes there are and how many labels you wish to find. If your data is binary, it is or isn't a class (e.g.

Deep Learning and the great AI revolution has finally arrived. Deep Neural Networks keep making incredible leaps in their ability to surprise us and beat humans at tasks we thought machines could never do better than us, like image recognition and play "Go". Or, we would like to think so. Recent developments and progress in machine learning and in deep learning in particular have been hailed as a new dawn for real artificial intelligence. AlphaGo win over world champion Go player Lee Sedol surprised many, since Go is a game much more complex than chess.

There is much more to a successful technology product than its code. Companies seeking to exploit artificial intelligence need employees who understand how machine learning works and how it can be applied in business. But people who can do both are hard to find. Smith School of Business in Toronto is trying to fill that gap with North America's -- and it believes the world's -- first master of management in artificial intelligence (MMAI). This month, 40 students are beginning the programme, studying topics such as how to apply AI in finance and the ethical implications of the technology, intertwined with hands-on training in natural language processing and deep learning (the use of artificial neural networks in advanced pattern recognition).

This week, Elon Musk praised the work of OpenAI after a team of five neural networks had defeated five humans, who ranked in the top 99.95 percentile of players worldwide, in the popular game Dota 2. The five bots had learned the game by playing against itself at a rate of a staggering 180 years per day. The game requires strong teamwork among the five players and, therefore, the achievement is quite remarkable and more evidence that artificial intelligence (AI) is rapidly becoming more advanced. However, directly after the five bots beat the five humans 2-1, Musk cautioned for the power of AI by urging that OpenAI should focus on AI that works with humans, instead of against humans. His statement is in line with his previous warnings for AI, which Musk believes could result in a robot dictatorship or an AI-arms race amongst superpowers that could be the most plausible cause for World War III. With artificial intelligence becoming increasingly sophisticated, also the warnings against AI become more pervasive, and the question remains then, is AI good or bad?

The updated version of the evaluate_model() function is listed below that creates a three-headed CNN model. We can see that each head of the model is the same structure, although the kernel size is varied. The three heads then feed into a single merge layer before being interpreted prior to making a prediction. When the model is created, a plot of the network architecture is created; provided below, it gives a clear idea of how the constructed model fits together.

In this profile series, we interview AI innovators on the front-lines - those who have dedicated their life's work to improving the human condition through technology advancements. He is also a founding co-director of Sociovestix Labs, a social enterprise in the area of financial data science. Damian's background is in research where he focuses on large- scale multimedia opinion mining applying machine learning and in particular deep learning to mine insights (trends, sentiment) from online media streams. Damian talks about his realization in deep learning and shares why integrating his work with deep learning is an important part to help prevent future natural disasters. What has your journey been like in deep learning?

Mumbai: The US, followed by China, India, Israel, and Germany, rank as the countries with the highest penetration of artificial intelligence (AI) skills among their workforce, according to a new report by professional networking site LinkedIn.com. The findings, released on Monday, further suggest that while changes driven by AI technologies may still be in their infancy, their impact is being felt across the global labour market in all sectors (https://bit.ly/2OroZdA). Moreover, industries with more AI skills present among their workforce are also the fastest-changing industries. Further, even as AI-- broadly defined as the desire to replicate human intelligence in machines -- has no superpower yet, it is undoubtedly becoming smarter with every passing day following advancements in machine learning and deep-learning algorithms, humongous amounts of Big Data on which these algorithms can be trained, and the phenomenal increase in computing power. These developments have, understandably, given rise to the fear that automation and AI will take away our jobs and eventually become smarter than us.

I'm trying to train an LSTM to generate song lyrics. For my input data, I downloaded a bunch of song lyrics (1-D list where each entry is one line of a song) and used Keras Tokenization w one-hot. This is is where I'm having trouble setting up the structure. Once I have converted the lyric-lines to one-hot, what should the input to the LSTM look like? When I fit the model, what should I use for my target?