Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. If you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry - and prepare you for a move into this hot career path. This comprehensive course includes over 80 lectures spanning 12 hours of video, and most topics include hands-on Python code examples you can use for reference and for practice. I'll draw on my 9 years of experience at Amazon and IMDb to guide you through what matters, and what doesn't. Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon.

From Facebook's research to DeepMind's legendary algorithms, deep learning has climbed its way to the top of the data science world. It has led to amazing innovations, incredible breakthroughs, and we are only just getting started! However if you are a newcomer to this field, the word "deep" might throw you into doubt. Deep learning is one of the hottest topics of this industry today, but it is unfortunately foreign and cryptic to most people. A lot of people carry an impression that deep learning involves a lot of mathematics and statistical knowledge. If you had similar questions about deep learning, but were not sure how, when and where to ask them – you are at the right place. This article should answer most of what you would want to know.

This is a tutorial of how to classify the Fashion-MNIST dataset with tf.keras, using a Convolutional Neural Network (CNN) architecture. In just a few lines of code, you can define and train a model that is able to classify the images with over 90% accuracy, even without much optimization. Fashion-MNIST can be used as drop-in replacement for the original MNIST dataset (10 categories of handwritten digits). It shares the same image size (28x28) and structure of training (60,000) and testing (10,000) splits. Keras is popular and well-regarded high-level deep learning API.

Artificial Intelligence has leaped from hype to reality, and there's still so much to understand and explore. Take a look at this interactive guide to all things in #AI, machine learning and deep learning by the McKinsey Analytics.

One method of AI is machine learning – programs that perform better over time and with more data input. Deep learning is among the most promising approaches to machine learning. The post The Many Ways to Define Artificial Intelligence appeared first on Intel Newsroom.

In this tutorial, I classify Yelp round-10 review datasets. The reviews contain a lot of metadata that can be mined and used to infer meaning, business attributes, and sentiment. For simplicity, I classify the review comments into two class: either as positive or negative. Reviews that have star higher than three are regarded as positive while the reviews with star less than or equal to 3 are negative. Therefore, the problem is a supervised learning.

Very important breakthroughs in data centric deep learning algorithms led to impressive performance in transactional point applications of Artificial Intelligence (AI) such as Face Recognition, or EKG classification. With all due appreciation, however, knowledge blind data only machine learning algorithms have severe limitations for non-transactional AI applications, such as medical diagnosis beyond the EKG results. Such applications require deeper and broader knowledge in their problem solving capabilities, e.g. integrating anatomy and physiology knowledge with EKG results and other patient findings. Following a review and illustrations of such limitations for several real life AI applications, we point at ways to overcome them. The proposed Wikipedia for Smart Machines initiative aims at building repositories of software structures that represent humanity science & technology knowledge in various parts of life; knowledge that we all learn in schools, universities and during our professional life. Target readers for these repositories are smart machines; not human. AI software developers will have these Reusable Knowledge structures readily available, hence, the proposed name ReKopedia. Big Data is by now a mature technology, it is time to focus on Big Knowledge. Some will be derived from data, some will be obtained from mankind gigantic repository of knowledge. Wikipedia for smart machines along with the new Double Deep Learning approach offer a paradigm for integrating datacentric deep learning algorithms with algorithms that leverage deep knowledge, e.g. evidential reasoning and causality reasoning. For illustration, a project is described to produce ReKopedia knowledge modules for medical diagnosis of about 1,000 disorders. Data is important, but knowledge deep, basic, and commonsense is equally important.

This paper presents the Crossmodal Attentive Skill Learner (CASL), integrated with the recently-introduced Asynchronous Advantage Option-Critic (A2OC) architecture [Harb et al., 2017] to enable hierarchical reinforcement learning across multiple sensory inputs. We provide concrete examples where the approach not only improves performance in a single task, but accelerates transfer to new tasks. We demonstrate the attention mechanism anticipates and identifies useful latent features, while filtering irrelevant sensor modalities during execution. We modify the Arcade Learning Environment [Bellemare et al., 2013] to support audio queries, and conduct evaluations of crossmodal learning in the Atari 2600 game Amidar. Finally, building on the recent work of Babaeizadeh et al. [2017], we open-source a fast hybrid CPU-GPU implementation of CASL.

Combining Bayesian nonparametrics and a forward model selection strategy, we construct parsimonious Bayesian deep networks (PBDNs) that infer capacity-regularized network architectures from the data and require neither cross-validation nor fine-tuning when training the model. One of the two essential components of a PBDN is the development of a special infinite-wide single-hidden-layer neural network, whose number of active hidden units can be inferred from the data. The other one is the construction of a greedy layer-wise learning algorithm that uses a forward model selection criterion to determine when to stop adding another hidden layer. We develop both Gibbs sampling and stochastic gradient descent based maximum a posteriori inference for PBDNs, providing state-of-the-art classification accuracy and interpretable data subtypes near the decision boundaries, while maintaining low computational complexity for out-of-sample prediction.

In recent years, due to the booming development of online social networks, fake news for various commercial and political purposes has been appearing in large numbers and widespread in the online world. With deceptive words, online social network users can get infected by these online fake news easily, which has brought about tremendous effects on the offline society already. An important goal in improving the trustworthiness of information in online social networks is to identify the fake news timely. This paper aims at investigating the principles, methodologies and algorithms for detecting fake news articles, creators and subjects from online social networks and evaluating the corresponding performance. This paper addresses the challenges introduced by the unknown characteristics of fake news and diverse connections among news articles, creators and subjects. Based on a detailed data analysis, this paper introduces a novel automatic fake news credibility inference model, namely FakeDetector. Based on a set of explicit and latent features extracted from the textual information, FakeDetector builds a deep diffusive network model to learn the representations of news articles, creators and subjects simultaneously. Extensive experiments have been done on a real-world fake news dataset to compare FakeDetector with several state-of-the-art models, and the experimental results have demonstrated the effectiveness of the proposed model.