Welcome to the first post of my series Deep Learning for Rookies by me, a rookie. I'm writing as a reinforcement learning strategy to process and digest the knowledge better. But if you are a deep learning rookie, then this is for you as well because we can learn together as rookies! Deep learning is probably one of the hottest tech topics right now. Large corporations and young startups alike are all gold-rushing this fancy field. If you think big data is important, then you should care about deep learning. The Economist says that data is the new oil in the 21st Century. If data is the crude oil, databases and data warehouses are the drilling rigs that digs and pumps the data on the internet, then think of deep learning as the oil refinery that finally turns crude oil into all the useful and insightful final products.

Me (author of Learning Deep Learning with Keras) and Jakub Czakon are giving a 45 min free webinar with a practical introduction to deep learning. So, we focus on how to use layers (convolution filter size? We will work on the notMNIST dataset of A-J letters, as it is way more interesting than MNIST, yet - still within easy reach of laptops. Indeed, I once even proposed that the toughest challenge facing AI workers is to answer the question: "What are the letters'A' and'I'?

Convolutional neural networks are the current state-of-art architecture for image classification. They're used in practice today in facial recognition, self driving cars, and detecting whether an object is a hot-dog. The basics of a CNN architecture consist of 3 components. A convolution, pooling, and fully connected layer. These components work together to learn a dense feature representation of an input.

Which machine learning algorithm should I use?

Tom studied English Literature and History at Sussex University before gaining a Masters in Newspaper Journalism from City University.

Marketers are investing heavily in content marketing, and many are looking to emerging technologies like artificial intelligence (AI) to help shape their strategies, new research suggests. Data from BrightEdge, an enterprise search engine optimization (SEO) and content performance marketing company, and SurveyMonkey looked at the probability that US market leaders will use AI or deep learning to develop their 2017 content marketing efforts. While a large share of respondents (57.1%) said they're unlikely to use AI or deep learning in their content marketing, a significant number felt differently. For example, nearly a third (31.4%) of respondents said they were somewhat likely to use AI to help flesh out their content marketing strategy. And an additional 8.7% said they were very likely to do so.

For R users, there hasn't been a production grade solution for deep learning (sorry MXNET). This post introduces the Keras interface for R and how it can be used to perform image classification. The post ends by providing some code snippets that show Keras is intuitive and powerful . Last January, Tensorflow for R was released, which provided access to the Tensorflow API from R. This was signficant, as Tensorflow is the most popular library for deep learning. However, for most R users, the Tensorflow for R interface was not very R like.

Welcome back to my second post of the series Deep Learning for Rookies (DLFR), by yours truly, a rookie;) Feel free to refer back to my first post here or my blog if you find it hard to follow. Or highlight on this page with notes or leave a comment below! Your feedback will be highly appreciated, too. We will go deeper into neural networks this time and the post will be slightly more technical than last time. But no worries, I will make it as easy and intuitive as possible for you to learn the basics without CS/Math background.

Recommender systems aim to find an accurate and efficient mapping from historic data of user-preferred items to a new item that is to be liked by a user. Towards this goal, energy-based sequence generative adversarial nets (EB-SeqGANs) are adopted for recommendation by learning a generative model for the time series of user-preferred items. By recasting the energy function as the feature function, the proposed EB-SeqGANs is interpreted as an instance of maximum-entropy imitation learning.

Co-founded by veteran AI entrepreneur Jean-François Gagné and deep learning pioneer, Yoshua Bengio, our 100 team includes PhDs, software engineers and a network of faculty professors whose labs are leaders in their respective domains.