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What is deep learning, Meaning And work?

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Deep learning is a sub-field of machine learning and an aspect of artificial intelligence. To understand this more easily, understand that it is meant to emulate the learning approach that humans use to acquire certain types of knowledge. This is somewhat different from machine learning, often people get confused in this and machine learning. Deep learning uses a sequencing algorithm while machine learning uses a linear algorithm. To understand this more accurately, understand this example that if a child is identified with a flower, then he will ask again and again, is this flower?


An IQ Test Proves That Neural Networks Are Capable of Abstract Reasoning

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Using those primitives, DeepMind generated a dataset known as Procedurally Generated Matrices(PGM) that consists of triplets [progression, shape, color]. The relationship between the attributes in a triplet represent an abstract challenge. For instance, if the first attribute is progression, the values of the other two attributes must along rows or columns in the matrix. In order to show signs of abstract reasoning using PGM, a neural network must be able to explicitly compute relatioships between different matrix images and evaluate the viability of each potential answer in parallel. To address this challenge, the DeepMind team created a new neural network architecture called Wild Relation Network(WReN) in recognition of John Rave's wife Mary Wild who was also a contributor to the original IQ Test. In the WReN architecture, a convolutional neural network(CNN) processes each context panel and an individual answer choice panel independently to produce 9 vector embeddings. This set of embeddings is then passed to an recurrent network, whose output is a single sigmoid unit encoding the "score" for the associated answer choice panel.


Using Machine Learning to Predict Price of Product on Mercari

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Original article was published by Aniket Mishrikotkar on Deep Learning on Medium Using Machine Learning to Predict Price of Product on MercariThe numbers have no way of speaking for themselves. We …


Four steps to accelerate the journey to machine learning - SiliconANGLE

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A good example of solving for the right problems can be seen in Formula One World Championship Ltd. The motorsport company was looking for new ways to deliver race metrics that could change the way fans and teams experience racing, but had more than 65 years of historical race data to sift through. After aligning their technical and domain experts to determine what type of untapped data had the most potential to deliver value for its teams and fans, Formula 1 data scientists then used Amazon SageMaker to train deep learning models on this historical data to extract critical performance statistics, make race predictions and relay engaging insights to their fans into the split-second decisions and strategies adopted by teams and drivers.


WHAT IS EDGE AI…?

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These days we are hearing a lot about AI, but have you ever heard about EDGE AI ..? What does it mean and what is it used for? Network edge or edge, where data resides and collected. Edge computing processes data on local places like computers, IoT devices or Edge servers, here we are doing computation to a network edge which indeed reduces long-distance communication between client and server. Edge AI, where AI algorithms will locally process sensor data or signals that are created on hardware devices in less than a few milliseconds by providing real-time information. Most of the time the AI algorithms are being processed in cloud data centers with deep learning models, which consume heavy compute capacity.


At CAGR 36.2%, Artificial Intelligence Market 2020: Future Challenges And Industry Growth Outlook 2025

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Artificial Intelligence (AI) is the study of "intelligent agents" which can be define as any device that perceives its environment and takes appropriate action that makes the highest probability of achieving its goals. Additionally, it can also be define as a system's ability to interpret external data, learn from gathered data and use those learnings to realize specific goals through adaptation. It is also called as machine intelligence and attributed to the nature of intelligence demonstrated by machines. Some of the features of artificial intelligence are; successfully understanding human language, contending at the highest level in strategic games systems such as chess and go, autonomously operating cars, intelligent routing in content delivery networks and military simulations and others. To solve the problem of learning and perceiving the immediate environment, many approaches have been taken such as statistical methods, computational intelligence, versions of search and mathematical optimization, artificial neural networks, and methods based on statistic, probability and economics.


How (not) to use Machine Learning for time series forecasting: The sequel - KDnuggets

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Time series forecasting is an important area of machine learning. It is important because there are so many prediction problems that involve a time component. However, while the time component adds additional information, it also makes time series problems more difficult to handle compared to many other prediction tasks. Time series data, as the name indicates, differ from other types of data in the sense that the temporal aspect is important. On a positive note, this gives us additional information that can be used when building our machine learning model -- that not only the input features contain useful information, but also the changes in input/output over time.


Streaming System Design for Large-Scale Machine Learning Applications

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In October of 2019 Crunchbase raised $30M in Series C financing from OMERS Ventures. Crunchbase is charging forward, focusing more deeply on the analysis of business signals for both private and public companies. Here at the Engineering Team, we have been working on the interesting challenge of detecting these high value business signals from various sources, such as Tweets and news articles. Some examples of important signals include funding rounds, acquisitions, and key leadership hires. Finding these signals the moment they are announced empowers our customers to make well-informed business decisions.


Deep learning for next-generation sleep diagnostics

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Currently, the diagnosis of sleep disorders relies on polysomnographic recordings with a time-consuming manual analysis with low reliability between different manual scorers. Throughout the night, sleep stages are identified manually in non-overlapping 30-second epochs starting from the onset of the recording based on electroencephalography (EEG), electro-oculography (EOG), and chin electromyography (EMG) signals which require meticulous placement of electrodes. Moreover, the diagnosis of many sleep disorders relies on outdated guidelines. When assessing the severity of obstructive sleep apnea (OSA), the patients are classified based on thresholds of the apnea-hypopnea index (AHI), i.e. the number of respiratory disruptions during sleep. These thresholds are not fully based on solid scientific evidence and remain the same across different measurement techniques.


Global Big Data Conference

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How to pick a cloud machine learning platform? To create an effective machine learning and deep learning model, you need more data, a way to clean the data and perform feature engineering on it. It is also a way to train models on your data in a reasonable amount of time. After that, you need a way to install your models, surveil them for drift over time, and retrain them as required. If you have invested in compute resources and accelerators such as GPUs, you can do all of that on-premises.