Gradient descent is one of the most popular algorithms to perform optimization and by far the most common way to optimize neural networks. At the same time, every state-of-the-art Deep Learning library contains implementations of various algorithms to optimize gradient descent (e.g. These algorithms, however, are often used as black-box optimizers, as practical explanations of their strengths and weaknesses are hard to come by. This blog post aims at providing you with intuitions towards the behaviour of different algorithms for optimizing gradient descent that will help you put them to use. We are first going to look at the different variants of gradient descent. We will then briefly summarize challenges during training. Subsequently, we will introduce the most common optimization algorithms by showing their motivation to resolve these challenges and how this leads to the derivation of their update rules. We will also take a short look at algorithms and architectures to optimize gradient descent in a parallel and distributed setting.

In the next chapter event, we will be exploring How IoT, video data and machine learning can improve predictive models AKA'The Rise of Engineering Driven Data Analytics'. Audio, image, video, and other sensor-generated data is being combined with traditional business and transactional data to create opportunities for sophisticated analytics on more complex phenomena. Our guest speaker, Boris Savkovic, Lead Data Scientist, BuildingIQ will present on'The rise of IoT data analytics and machine learning in the smart building of tomorrow' Large-scale buildings (skyscrapers, hospitals, shopping centres etc.) account for approximately 40% of global energy consumption, with a large proportion of this energy consumed by heating, ventilation and cooling (HVAC) systems that regulate comfort and internal conditions/temperatures in a building. The business challenges in terms of the resulting energy costs and associated greenhouse gas emissions are substantial. However, these challenges also provide opportunities for innovative companies who can provide services and solutions that address these challenges, driving value for building owners while at the same time helping reduce global greenhouse gas emissions in a post-Paris agreement world.

Every year there is a new hot topic in tech. The difference between now and the past is that everything is becoming interconnected at a faster rate. We are entering an extremely critical time in history where society will change dramatically – how we work, live and play. Science fiction is morphing into reality. Flying cars exist, cars that drive themselves are on the road, and artificial intelligence that automates our lives is here.

A London-based startup has combined some of today's most disruptive technologies in a bid to change the way we'll build the future. By retrofitting industrial robots with 3D printing guns and artificial intelligence algorithms, Ai Build has constructed machines that can see, create, and even learn from their mistakes.

Back in September, Digital Trends exclusively reported that Google would open pop-up shops in New York City to showcase its latest and greatest hardware. Google will open the doors of its new 96 Spring Street pop-up shop on October 20 to the public. The space will be open throughout the holidays to tempt people into buying Google's Pixel phones, Daydream View VR headset, Google Home, and Chromecast Ultra. We strolled into 96 Spring Street to see Google's new hardware ecosystem in action and walked away with a better understanding of the search giant's future trajectory into the great unknown landscape of artificial intelligence. Google's AI Assistant is at the core of every experience we saw at the pop-up shop.

Chris Baker is managing director of UK Enterprise at Concur. What reactions did people have to the movie trailer for Morgan (which was created entirely -- and for the first time -- by an AI bot, and a pretty famous one at that)? Which is a fair reaction. Computers can now write, read, learn and speak. And for some, this is pretty scary -- people are terrified that bots will snatch their jobs and eventually take over the world and render humans useless (films like I, Robot haven't helped this).

Analysis Research in AI is expanding quickly, and the UK and US governments have begun to notice. Official reports about the new technology and future strategies were dropped by both governments this month. Blighty's Science and Technology Committee released Robotics and Artificial Intelligence, while the White House delivered Preparing for the Future of Artificial Intelligence and National Artificial Intelligence Research and Development Strategic Plan. The titles of the British and American reports provide a clue as to how both governments are responding. There is no "preparing" or "strategic plan" in the UK's reports.

We are looking for smart, energetic individuals to help us develop and launch groundbreaking career-centered products. We are currently seeking a Machine Learning Engineer to join our team in San Jose, CA. At RiseSmart, the engineering culture is talent-centric. We believe in bringing together a team of talented engineers that are passionate about programming, technologies and developing solutions for complex human capital issues. About the Job Samples of products and the kind solutions we are developing: • Job and Profile Matching Engine: Build distributed search engine of jobs and candidates.

Topic identification is a top-of-the-list need for organizations working with large volumes of online, social, and enterprise text. Along with entity resolution, relation extraction, summarization, and sentiment analysis, topic modeling is a key natural language processing (NLP) function. Premise number 2: Applied NLP -- text analytics -- remains as much art as science, requiring a combination of domain and technical expertise. How better to explore topic modeling and NLP advances than via an interview with a leading practitioner? This article features an interview with Lev Konstantinovskiy, a data scientist who is community manager for gensim, which offers open-source topic modeling for Python programmers.

There's been a lot of buzz over the last two years around the "Internet of Things," or IoT. However, more recently a subcategory of IoT, the Industrial Internet of Things (IIoT), has been getting a lot of well-deserved attention. For those who've not yet heard of this trend, the IIoT is basically the use of Internet of Things (IoT) technologies in manufacturing. It brings together many key technologies--including machine learning, big data, sensors, machine-to-machine (M2M) computing, and more--in an orchestrated fashion within manufacturing operations. The IIoT promises to drive massive economic transformations in the coming years across multiple industries, including manufacturing, health care, and mining.