Fall detection is a major problem in healthcare. Old people fall often and there should be immediate aid for them as injury can be serious, even lead to death. In this project, we will use Convolutional Neural Network architecture to detect whether a person has fallen or not in a given image. For this purpose, we use public dataset that contains videos of falling people. Each frame has been given as a picture, and we use 2 videos that make a total of 240 images.

In this post we will be looking into the paper "Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization"(AdaIN) by Huang et. We are looking this paper because it had some key advantages over the other state-of-the-art methods at the time or release. Most important of all, this method, once trained, can be used to transfer style between any arbitrary content-style image pair, even ones not seen during training. While the method proposed by Gatys et. The AdaIN method is also flexible, it allows for control over the strength of the transferred style in the stylised image and also allows for extensions such as style interpolation and spatial controls.

The Latest research study released by Data Bridge Market Research "Deep Learning in Computer Vision Market" with 100 pages of analysis on business Strategy taken up by key and emerging industry players and delivers know how of the current market development, landscape, technologies, drivers, opportunities, market viewpoint and status. Deep Learning in Computer Vision market report contains market data that can be relatively essential when it comes to dominate the market or make a mark in the market as a new emergent. The purpose of Deep Learning in Computer Vision market report is to provide a detailed analysis of this industry and its impact based on applications and on different geographical regions. This market research report is a resource for getting current as well as upcoming technical and financial details of the industry. Deep Learning in Computer Vision market report also enlists the leading competitors and provides the insights about the strategic industry analysis of the key factors influencing this industry.

In this post, we will take a tour of the most popular machine learning algorithms. It is useful to tour the main algorithms in the field to get a feeling of what methods are available. There are so many algorithms that it can feel overwhelming when algorithm names are thrown around and you are expected to just know what they are and where they fit. I want to give you two ways to think about and categorize the algorithms you may come across in the field. Both approaches are useful, but we will focus in on the grouping of algorithms by similarity and go on a tour of a variety of different algorithm types.

GPT-2 was a great success. OpenAI didn't want to publish the most enormous and mightiest version, with 1.5B parameters. At least, claiming that they were afraid of misusing it for less ethical purposes. Lately, they claimed that they didn't found shreds of evidence of such. All of this is legit, considering the volume of the false "news" generated using it. And the truth is that it can be very successful in developing false news/stories.

Machine learning has become an important component of many applications we use today. And adding machine learning capabilities to applications is becoming increasingly easy. Many ML libraries and online services don't even require a thorough knowledge of machine learning. However, even easy-to-use machine learning systems come with their own challenges. Among them is the threat of adversarial attacks, which has become one of the important concerns of ML applications.

Welcome to AI book reviews, a series of posts that explore the latest literature on artificial intelligence. Recent advances in deep learning have rekindled interest in the imminence of machines that can think and act like humans, or artificial general intelligence. By following the path of building bigger and better neural networks, the thinking goes, we will be able to get closer and closer to creating a digital version of the human brain. But this is a myth, argues computer scientist Erik Larson, and all evidence suggests that human and machine intelligence are radically different. Larson's new book, The Myth of Artificial Intelligence: Why Computers Can't Think the Way We Do, discusses how widely publicized misconceptions about intelligence and inference have led AI research down narrow paths that are limiting innovation and scientific discoveries.

Model Deployment is the method by which you integrate a machine learning model into an existing production environment to make practical business decisions based on data. It is one of the last stages in the machine learning life cycle and can be one of the most cumbersome. Model deployment is probably the most important part of the Machine Learning model lifecycle but still, the least studied one. Most of the courses out there around the ML/DL universe teach how to explore data, engineer the features, train the model, and generate predictions. But they miss the most important part: what to do after that? Apart from the models developed for learning or for Kaggle competitions, all other models are built to generate revenue, and if you don't deploy a model into production then there's no one using it and thus no revenue.

A transition to a low-carbon electricity supply is crucial to limit the impacts of climate change. Reducing carbon emissions could help prevent the world from reaching a tipping point, where runaway emissions are likely. Runaway emissions could lead to extremes in weather conditions around the world -- especially in problematic regions unable to cope with these conditions. However, the movement to a low-carbon energy supply can not happen instantaneously due to the existing fossil-fuel infrastructure and the requirement to maintain a reliable energy supply. Therefore, a low-carbon transition is required, however, the decisions various stakeholders should make over the coming decades to reduce these carbon emissions are not obvious. This is due to many long-term uncertainties, such as electricity, fuel and generation costs, human behaviour and the size of electricity demand. A well choreographed low-carbon transition is, therefore, required between all of the heterogenous actors in the system, as opposed to changing the behaviour of a single, centralised actor. The objective of this thesis is to create a novel, open-source agent-based model to better understand the manner in which the whole electricity market reacts to different factors using state-of-the-art machine learning and artificial intelligence methods. In contrast to other works, this thesis looks at both the long-term and short-term impact that different behaviours have on the electricity market by using these state-of-the-art methods.

Tracking a turbulent plume to locate its source is a complex control problem because it requires multi-sensory integration and must be robust to intermittent odors, changing wind direction, and variable plume statistics. This task is routinely performed by flying insects, often over long distances, in pursuit of food or mates. Several aspects of this remarkable behavior have been studied in detail in many experimental studies. Here, we take a complementary in silico approach, using artificial agents trained with reinforcement learning to develop an integrated understanding of the behaviors and neural computations that support plume tracking. Specifically, we use deep reinforcement learning (DRL) to train recurrent neural network (RNN) agents to locate the source of simulated turbulent plumes. Interestingly, the agents' emergent behaviors resemble those of flying insects, and the RNNs learn to represent task-relevant variables, such as head direction and time since last odor encounter. Our analyses suggest an intriguing experimentally testable hypothesis for tracking plumes in changing wind direction -- that agents follow local plume shape rather than the current wind direction. While reflexive short-memory behaviors are sufficient for tracking plumes in constant wind, longer timescales of memory are essential for tracking plumes that switch direction. At the level of neural dynamics, the RNNs' population activity is low-dimensional and organized into distinct dynamical structures, with some correspondence to behavioral modules. Our in silico approach provides key intuitions for turbulent plume tracking strategies and motivates future targeted experimental and theoretical developments.