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deep learning

Simultaneous clustering and representation learning


The success of deep learning over the last decade, particularly in computer vision, has depended greatly on large training data sets. Even though progress in this area boosted the performance of many tasks such as object detection, recognition, and segmentation, the main bottleneck for future improvement is more labeled data. Self-supervised learning is among the best alternatives for learning useful representations from the data. In this article, we will briefly review the self-supervised learning methods in the literature and discuss the findings of a recent self-supervised learning paper from ICLR 2020 [14]. We may assume that most learning problems can be tackled by having clean labeling and more data obtained in an unsupervised way.

Artificial Intelligence Before Explosion – Here are Promising AI Projects - Intelvue


Artificial Intelligence (AI) is not the one that is borne by the overwhelming science fiction vision. In the near future, we will see almost every area of life in order to make our activities more effective and interactive. According to China's search engine, Baidu's top researcher, "Reliability of speech technology approaches the point we will only use and do not even think about." Andrew Ng says the best technology is often invisible, and speech recognition will disappear in the background as well. Baidu is currently working on more accurate speech recognition and more efficient sentence analysis, which expects sound technologies to be able to interact with multiple devices such as household appliances.

Stock price prediction using LSTM (Long Short-Term Memory)


Convert the Xtrain and Ytrain data set into NumPy array because it will take for training the LSTM model.LSTM model has a 3-Dimensional data set [number of samples, time steps, features]. Therefore, we need to reshape the data from 2-Dimensional to 3-Dimensional. Below the code, snapshot illustrates a clear idea about reshaping the data set.Create the LSTM model which has two LSTM layers that contain fifty neurons also it has 2 Dense layers that one layer contains twenty-five neurons and the other has one neuron. In order to create a model that sequential input of the LSTM model which creates by using Keras library on DNN (Deep Neural Network). The compile LSTM model is using MSE (Mean Squared Error) for loss function and the optimizer to be the "adam".

Real Time Anomaly Detection for Cognitive Intelligence - XenonStack


Classical Analytics – Around ten years ago, the tools for analytics or the available resources were excel, SQL databases, and similar relatively simple ones when compared to the advanced ones that are available nowadays. The analytics also used to target things like reporting, customer classification, sales trend whether they are going up or down, etc.In this article we will discuss about Real Time Anomaly Detection. As time passed by the amount of data has got a revolutionary explosion with various factors like social media data, transaction records, sensor information, etc. in the past five years. With the increase of data, how data is stored has also changed. It used to be SQL databases the most and analytics used to happen for the same during the ideal time. The analytics also used to be serialized. Later, NoSQL databases started to replace the traditional SQL databases since the data size has become huge and the analysis also changed from serial analytics to parallel processing and distributed systems for quick results.

How Deep Learning Can Keep You Safe with Real-Time Crime Alerts


Citizen scans thousands of public first responder radio frequencies 24 hours a day in major cities across the US. The collected information is used to provide real-time safety alerts about incidents like fires, robberies, and missing persons to more than 5M users. Having humans listen to 1000 hours of audio daily made it very challenging for the company to launch new cities. To continue scaling, we built ML models that could discover critical safety incidents from audio. Our custom software-defined radios (SDRs) capture large swathes of radio frequency (RF) and create optimized audio clips that are sent to an ML model to flag relevant clips.

Artificial Intelligence and Machine Learning – Path to Intelligent Automation


With evolving technologies, intelligent automation has become a top priority for many executives in 2020. Forrester predicts the industry will continue to grow from $250 million in 2016 to $12 billion in 2023. With more companies identifying and implementation the Artificial Intelligence (AI) and Machine Learning (ML), there is seen a gradual reshaping of the enterprise. Industries across the globe integrate AI and ML with businesses to enable swift changes to key processes like marketing, customer relationships and management, product development, production and distribution, quality check, order fulfilment, resource management, and much more. AI includes a wide range of technologies such as machine learning, deep learning (DL), optical character recognition (OCR), natural language processing (NLP), voice recognition, and so on, which creates intelligent automation for organizations across multiple industrial domains when combined with robotics.

MIT PixelPlayer "Sees" Where Sounds Are Coming From


The "cocktail party effect" describes humans' ability to hold a conversation in a noisy environment by listening to what their conversation partner is saying while filtering out other chatter, music, ambient noises, etc. We do it naturally but the problem has been widely studied in machine learning, where the development of environmental sound recognition and source separation techniques that can tune into a single sound and filter out all others is a research focus. MIT CSAIL researchers recently introduced their PixelPlayer system, which has learned to identify objects that produce sound in videos. The system uses deep learning and was trained by binge-watching 60 hours of musical performances to identify the natural synchronization of visual and audio information. The team trained deep neural networks to concentrate on images and audio and identify pixel-level image locations for sound sources in the videos.

Image Classification Model


Image classification is one of the most important applications of computer vision. Its applications ranges from classifying objects in self driving cars to identifying blood cells in healthcare industry, from identifying defective items in manufacturing industry to build a system that can classify persons wearing masks or not. Image Classification is used in one way or the other in all these industries. Which framework do they use? You must have read a lot about the differences between different deep learning frameworks including TensorFlow, PyTorch, Keras, and many more.

Embedded Vision Systems Adopt AI and IoT Tech


Machine vision has come a long way from the simpler days of cameras attached to frame grabber boards--all arranged along an industrial production line. While the basic concepts are the same, emerging embedded systems technologies such as Artificial Intelligence (AI), deep learning, the Internet-of-Things (IoT) and cloud computing have all opened up new possibilities for machine vision system developers. To keep pace, companies that used to only focus on box-level machine vision systems are now moving toward AI-based edge computing systems that provide all the needed interfacing for machine vision, but also add new levels of compute performance to process imaging in real-time and over remote network configurations. AI IN MACHINE VISION ADLINK Technology appears to be moving in this direction of applying deep learning and AI to machine vision. The company has a number of products, listed "preliminary" at present, that provide AI machine vision solutions. These systems are designed to be "plug and play" (PnP) so that machine vision system developers can evolve their existing applications to AI-enablement right away with no need to replace existing hardware.