Designing the course and the labs to be accessible to as many people as possible was a big priority for us. So, Lecture 1 focuses on neural network fundamentals, and the first module in Lab 1 provides a clean introduction to TensorFlow, and is written in preparation for the upcoming release of TensorFlow 2.0. Our introduction to TensorFlow exercises highlight a few key concepts in particular: how to execute computations using math operators, how to define neural network models,and how to use automatic differentiation to train networks with backpropagation. Following the Intro to TensorFlow module, Lab 1's second module dives right into building and applying a recurrent neural network (RNN) for music generation, designed to accompany Lecture 2 on deep sequence modeling. You'll build an AI algorithm that can generate brand new, never-heard-before Irish folk music.

Unlike traditional rule-based personalization systems, Segmentino's AI-driven solution reflects real-time changes and complexity of user behavior online and thus enables you to personalize 1:1 improving your user experience and KPI's. Our solution analyzes user interactions and behavior online as well as product attributes and generates recommendations which are more likely to spark the interest of the customer. At Segmentino, we conduct research in collaboration with academia, which keeps us at the leading edge of innovation. We utilize deep-learning and collaborative filtering, as well as content-based algorithms (such as image and text processing algorithms) to ensure the most accurate content for all visitors. What is more, our solution is real-time, meaning that our models are adapting after every interaction of the users.

In collaboration with its Britain-based Artificial Intelligence (AI) subsidiary DeepMind, Google has developed a system to predict wind power output 36 hours ahead of actual generation. Google said that these type of predictions can boost the value of wind energy and can strengthen the business case for wind power and drive further adoption of carbon-free energy on electric grids worldwide. "Over the past decade, wind farms have become an important source of carbon-free electricity as the cost of turbines has plummeted and adoption has surged," Sims Witherspoon, Programme Manager at DeepMind and Will Fadrhonc, Carbon Free Energy Programme Lead at Google wrote in a blog post this week. "However, the variable nature of wind itself makes it an unpredictable energy source - less useful than one that can reliably deliver power at a set time," they said. In search of a solution to this problem, DeepMind and Google started applying machine learning algorithms to 700 megawatts of wind power capacity in the central US.

Machine-learning engineer was the fastest growing job category in the five years to 2017, according to LinkedIn. But tech's hottest role isn't a simple field to break into, requiring at least high school math and some programming knowledge, even to get started. Luckily there are an increasing number of options for those wanting to get a grounding in the field, with Amazon Web Services (AWS) being the latest tech giant to release a set of machine-learning courses for free. That's in addition to the existing well-regarded material available online from the likes of fast.ai and Andrew Ng and Coursera. If you're interested in these courses, it's worth noting that you'll benefit more if you have a basic knowledge of Python and high school linear algebra, statistics, and calculus.

Graphs and their study have received a lot of attention since ages due to their ability of representing the real world in a fashion that can be analysed objectively. Indeed, graphs can be used to represent a lot of useful, real world datasets such as social networks, web link data, molecular structures, geographical maps, etc. Apart from these cases which have a natural structure to them, non-structured data such as images and text can also be modelled in the form of graphs in order to perform graph analysis on them. Due to the expressiveness of graphs and a tremendous increase in the available computational power in recent times, a good amount of attention has been directed towards the machine learning way of analysing graphs, i.e. According to this paper, Graph neural networks (GNNs) are connectionist models that capture the dependence of graphs via message passing between the nodes of graphs. They are extensions of the neural network model to capture the information represented as graphs.

In 2019 we live in a world where Deepfake videos and even images of people can be created by AI that are totally manufactured. Meanwhile OpenAI's GPT-2 is being hyped for its ability to write convincingly and deceptively. In this new world AI can mimic human content in a variety of ways while potentially being used by bad actors and state-sponsored propaganda campaigns to influence public sentiment in a variety of ways. In February 2019, we are witnessing an explosion of deception online. In an era when even Facebook refuses to be called a Media company, what exactly are deepfakes?

In 2017, we predicted that artificial general intelligence will gain traction in research and certain areas will aid towards AGI systems. The prediction was made in a set of other AI predictions in an article titled 18 striking AI Trends to watch in 2018. Let's see how 2018 went for AGI research. Artificial general intelligence or AGI is an area of AI in which efforts are made to make machines have intelligence closer to the complex nature of human intelligence. Such a system could possibly, in theory, perform tasks that a human can with the ability to learn as it progresses through tasks, collects data/sensory input.

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.

Recently, deep generative models have become increasingly popular in unsupervised anomaly detection. However, deep generative models aim at recovering the data distribution rather than detecting anomalies. Besides, deep generative models have the risk of overfitting training samples, which has disastrous effects on anomaly detection performance. To solve the above two problems, we propose a Self-adversarial Variational Autoencoder with a Gaussian anomaly prior assumption. We assume that both the anomalous and the normal prior distribution are Gaussian and have overlaps in the latent space. Therefore, a Gaussian transformer net T is trained to synthesize anomalous but near-normal latent variables. Keeping the original training objective of Variational Autoencoder, besides, the generator G tries to distinguish between the normal latent variables and the anomalous ones synthesized by T, and the encoder E is trained to discriminate whether the output of G is real. These new objectives we added not only give both G and E the ability to discriminate but also introduce additional regularization to prevent overfitting. Compared with the SOTA baselines, the proposed model achieves significant improvements in extensive experiments. Datasets and our model are available at a Github repository.

A significant advance in accelerating neural network training has been the development of normalization methods, permitting the training of deep models both faster and with better accuracy. These advances come with practical challenges: for instance, batch normalization ties the prediction of individual examples with other examples within a batch, resulting in a network that is heavily dependent on batch size. Layer normalization and group normalization are data-dependent and thus must be continually used, even at test-time. To address the issues that arise from using explicit normalization techniques, we propose to replace existing normalization methods with a simple, secondary objective loss that we term a standardization loss. This formulation is flexible and robust across different batch sizes and surprisingly, this secondary objective accelerates learning on the primary training objective. Because it is a training loss, it is simply removed at test-time, and no further effort is needed to maintain normalized activations. We find that a standardization loss accelerates training on both small- and large-scale image classification experiments, works with a variety of architectures, and is largely robust to training across different batch sizes.