Analyzing live videos by leveraging deep learning is the trendiest technology aided by computer vision and multimedia analysis. Analysing live videos is a very challenging task and its application is still at nascent stages. Thanks to the recent developments in deep learning techniques, researchers in both computer vision and multimedia communities have been able to gather momentum to drive business processes and revenues. Whenever you want to express by non-verbal signs you use your hands to communicate, like whenever a traffic cop raises his hand you know he is signalling you to stop till directed. Though easier for mankind, technology finds it harder to work on people's reaction to predict their next behaviour.

So, you've heard the dazzling sales pitch on deep learning and are wondering whether it actually works in production. The top question companies have is on whether the promised land of perennial business benefits is a reality. In a previous article, we saw a simple business introduction to deep learning, a technology that seems to have a swashbuckling solution to every problem. But, what happens when the rubber hits the road? A good gauge of an innovation's maturity level is by understanding how it fares on the ground, long past the sales pitches. At Gramener AI Labs, we've been studying advances in deep learning and translating them into specific projects that map to client problems.

Then I'll explain the internal mechanisms that allow LSTM's and GRU's to perform so well. If you want to understand what's happening under the hood for these two networks, then this post is for you. You can also watch the video version of this post on youtube if you prefer. Recurrent Neural Networks suffer from short-term memory. If a sequence is long enough, they'll have a hard time carrying information from earlier time steps to later ones. So if you are trying to process a paragraph of text to do predictions, RNN's may leave out important information from the beginning.

Given the rise of smart electricity meters and the wide adoption of electricity generation technology like solar panels, there is a wealth of electricity usage data available. This data represents a multivariate time series of power-related variables, that in turn could be used to model and even forecast future electricity consumption. In this tutorial, you will discover a household power consumption dataset for multi-step time series forecasting and how to better understand the raw data using exploratory analysis. How to Load and Explore Household Electricity Usage Data Photo by Sheila Sund, some rights reserved. The Household Power Consumption dataset is a multivariate time series dataset that describes the electricity consumption for a single household over four years. The data was collected between December 2006 and November 2010 and observations of power consumption within the household were collected every minute. Active and reactive energy refer to the technical details of alternative current.

It's a big day for Microsoft today, which announced a slew of updates across virtually all of its product lines at its Ignite conference today. Unsurprisingly, one theme this year is artificial intelligence and machine learning. Microsoft is launching new tools to bring its Cortana assistant to the enterprise, but there is also a number of other developer-centric updates and products that are launching today. One of those is an update to the Azure Machine Learning services, the company's platform for letting anyone build and train machine learning models with a focus on prediction. With today's update, this platform is getting a new tool that automates much of the time-consuming selection, testing and tweaking necessary to build a solid model.

The popularity of Deep Neural Networks has drastically increased over the last half a decade or so. Built around the concept of Deep Learning and Biological Neural Networks, it has managed to capture the imagination of one and all. Deep Learning in its brief stint has already made innovative changes and has aided in eliminating or mitigating issues with computer vision, natural language processing, speech recognition, machine translation and more. In fact, in many sectors, it has managed to improve on the performances of the human experts. Hence, it is interesting to analyse its possible impact on the financial sector.

UpGrad's PG Program In Big Data Engineering has been designed in collaboration with BITS that covers the fundamental topics required for Big Data including OOP (using Java), data structures and algorithms, and relational database management systems (using SQL). The PG Program In ML and AI, on the other hand, especially focuses on creating an all-comprehensive base in ML and AI by helping students learn classification algorithms, Deep Learning, Reinforcement Learning, NLP, and Graph Models. Now, coming to the PG Program In Data Science, this course will definitely prepare you for a bright future in Data Science. Starting from the basic concepts in Analytics, Statistics and Data Management, students are introduced to more advanced concepts like ML, Neural Networks, and Big Data Analytics.

Reinforcement learning has shown great potential in generalizing over raw sensory data using only a single neural network for value optimization. There are several challenges in the current state-of-the-art reinforcement learning algorithms that prevent them from converging towards the global optima. It is likely that the solution to these problems lies in short- and long-term planning, exploration and memory management for reinforcement learning algorithms. Games are often used to benchmark reinforcement learning algorithms as they provide a flexible, reproducible, and easy to control environment. Regardless, few games feature a state-space where results in exploration, memory, and planning are easily perceived. This paper presents The Dreaming Variational Autoencoder (DVAE), a neural network based generative modeling architecture for exploration in environments with sparse feedback. We further present Deep Maze, a novel and flexible maze engine that challenges DVAE in partial and fully-observable state-spaces, long-horizon tasks, and deterministic and stochastic problems. We show initial findings and encourage further work in reinforcement learning driven by generative exploration.

Buried landmines and unexploded remnants of war are a constant threat for the population of many countries that have been hit by wars in the past years. The huge amount of human lives lost due to this phenomenon has been a strong motivation for the research community toward the development of safe and robust techniques designed for landmine clearance. Nonetheless, being able to detect and localize buried landmines with high precision in an automatic fashion is still considered a challenging task due to the many different boundary conditions that characterize this problem (e.g., several kinds of objects to detect, different soils and meteorological conditions, etc.). In this paper, we propose a novel technique for buried object detection tailored to unexploded landmine discovery. The proposed solution exploits a specific kind of convolutional neural network (CNN) known as autoencoder to analyze volumetric data acquired with ground penetrating radar (GPR) using different polarizations. This method works in an anomaly detection framework, indeed we only train the autoencoder on GPR data acquired on landmine-free areas. The system then recognizes landmines as objects that are dissimilar to the soil used during the training step. Experiments conducted on real data show that the proposed technique requires little training and no ad-hoc data pre-processing to achieve accuracy higher than 93% on challenging datasets.

Deep neural networks have established as a powerful tool for large scale supervised classification tasks. The state-of-the-art performances of deep neural networks are conditioned to the availability of large number of accurately labeled samples. In practice, collecting large scale accurately labeled datasets is a challenging and tedious task in most scenarios of remote sensing image analysis, thus cheap surrogate procedures are employed to label the dataset. Training deep neural networks on such datasets with inaccurate labels easily overfits to the noisy training labels and degrades the performance of the classification tasks drastically. To mitigate this effect, we propose an original solution with entropic optimal transportation. It allows to learn in an end-to-end fashion deep neural networks that are, to some extent, robust to inaccurately labeled samples. We empirically demonstrate on several remote sensing datasets, where both scene and pixel-based hyperspectral images are considered for classification. Our method proves to be highly tolerant to significant amounts of label noise and achieves favorable results against state-of-the-art methods.