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          Chip Lights Up Optical Neural Network Demo      Cache   Translate Page   Web Page Cache   
Researchers at the National Institute of Standards and Technology (NIST) have made a silicon chip that distributes optical signals precisely across a miniature brain-like grid, showcasing a potential new design for neural networks. The human brain has billions of neurons (nerve cells), each with thousands of connections to other neurons. Many computing research projects aim […]
          how to use neural networks to exclude a lottery number?      Cache   Translate Page   Web Page Cache   
Lottery Discussion forum
Reply #1
It appears a lot of people like to use the term neural network but I suspect nobody on this forum knows what a neural network is.
          Comment on When to Use MLP, CNN, and RNN Neural Networks by Jason Brownlee      Cache   Translate Page   Web Page Cache   
You can save the weights to file, then load the weights into the new model.
          Whats new on arXiv      Cache   Translate Page   Web Page Cache   
MCRM: Mother Compact Recurrent Memory A Biologically Inspired Recurrent Neural Network Architecture LSTMs and GRUs are the most common recurrent …

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          Computer Scientist      Cache   Translate Page   Web Page Cache   
DC-Washington, Computer Scientist Research & Development Location: Washington, DC We are looking for an Computer Scientist to perform research and development in signal processing algorithm development, neural network design, data processing and analysis for military applications. The position is on-site at a Government facility. Due to the sensitivity of the work, US Citizenship is required. Must have a PhD in
          Notes from Silicon Beach: AI and Hollywood – What is killing you will make you stronger      Cache   Translate Page   Web Page Cache   

“Hollywood and Silicon Valley are in the same business: producing algorithms,” writes artificial intelligence (AI) pioneer Yves Bergquist, one of a new breed of data scientists focused on the entertainment and media business. Scientists like Berquist believe that to survive and thrive, the media and entertainment industry needs to embrace cognitive science. That’s how they can hope to compete with tech companies and address their failing business models.

The cluster of technologies generally called artificial intelligence (AI) or machine learning (ML), including fields such as big data analytics, deep machine learning, semantics and natural language processing, visual and auditory recognition, prediction and personalization, and conversational agents, among others. These tools enable the creation of software that can be taught to learn and program itself – to automate repetitive tasks and to provide insights that were never before possible.

Tech-assisted Content Development

One active area of AI in the industry is content development. For example, the studio-funded think tank, USC Entertainment Technology Center, where Bergquist leads an AI and neuroscience group, is mapping box office returns against elements of the film narrative. Bergquist is working on data breakdowns of movies, as shown in this demo, the work of two Bergquist AI startups, Corto and Novamente:

Another example is Greenlight Essentials, a member of IDEABOOST Network Connect. They have broken down decades of film screenplays into more than 40,000 unique plot elements, analyzing more than 200 million audience profiles to help filmmakers improve scripts, target audiences and improve marketing. Their product’s analytic terminal allows users with neither programming nor mathematics background to explore and discover repeatable patterns from decades of film data.

Scriptonomics is a ML application that breaks down movie scripts by scene, character, location and other components. Writers and producers can leverage insights and comparisons that the tool extracts from its massive database of past successful movies to improve subsequent drafts, as well as aid in making pitches and targeting audiences – as can be seen in this example of Scriptonomics breakdown for Titanic.

Founder Tammuz Dubnov says that Scriptonomics generates a geometric model of a screenplay – its DNA, if you will – to compare and improve elements when compared with financially successful films of the past. As discussed here, Dubnov believes that this data-driven, quantitative filmmaking process will give rise to a new generation of data-assistant content studios that will help create more hits and fewer flops.

RivetAI offers Agile Producer, a pre-production platform that automates script breakdown, storyboard, shot lists, scheduling and budgeting. Before RivetAI, Toronto native Debajyoti Ray built the earlier AI startup, Video AMP. This AI-powered video advertising solution helped him understand how much commercials owe to storytelling. So he decided to build an AI engine based on thousands of movie scripts, both produced and unproduced, which became RivetAI.

Some early RivetAI projects were: Sunspring, a short film starring Thomas Middleditch; a script credited to “Benjamin, an artificially intelligent neural network,” in conjunction with LA-based production company End Cue; “Bubbles,” an animation about Michael Jackson’s chimpanzee that Ray found while analyzing unproduced screenplays, a project Netflix acquired.

RivetAI’s 500 production companies’ customers will feed ever more data to its self-learning system to augment their storytelling efforts. Ray compares RivetAI to AutoCAD – software that began as a drafting tool and has become a central platform for many creative professionals. To that end, RivetAI is developing products for screenwriters, corporate branded content, series television and reality shows. 

A computer monitor with an image of a man on the screen. The man is standing in front of a green background. #source%3Dgooglier%2Ecom#https%3A%2F%2Fgooglier%2Ecom%2Fpage%2F%2F10000

A demonstration of the artificial intelligence software, Arraiy, being used to process green screen footage quickly.

Photo by Christie Hemm Klok for The New York Times

Content Creation with an AI Assist

Computer-generated visual effects are widely used in blockbuster movies, TV shows and games. Sensei, Adobe’s AI, is now being deployed across the company’s cloud platforms to automate functions and provide intelligence. The more Sensei is used, the smarter it gets.

Also, 3D software giant AutoDesk is moving towards AI-assisted generative design, which the company used on its own new facility in downtown Toronto. Massive Software, which has been used on Peter Jackson’s massive CG films, now uses AI to automate crowd simulation and other time-sucking tasks. Its Ready to Run Agents are prefabricated AI agents that can be dropped into scenes by visual effects artists, saving time in the creation of CGI characters.

Arraiy is a well-funded Silicon Valley startup that uses computer vision and machine learning to automate time-consuming visual effects like rotosoping, to separate layers of an image to allow manipulation. The Black-Eyed Peas’ music video for their song, “Street Living,” utilized Arraiy to superimpose band members over images from the civil rights era.

The work involved in modeling, texturing, lighting, animation and performance will ultimately be automated with machine learning, says Derek Spears, Emmy Award-winning VFX artist for Game of Thrones. “Then, the next frontier will be AI-driven actor performances.”

Simulating People

We’ve seen Carrie Fischer exhumed into Star Wars movies, using past performances. Now we’re seeing the emergence of simulated video and voice. Rival Theory's RAIN AI creates human-like AI for more than 100,000 game developers and agencies. Lyrebird is a tool for the creation of artificial voices. Adobe has demoed Voco, a prototype that generates speech that sounds like a specific person.

Clarifai is a platform that uses “computer vision,” a form of machine learning, to help customers detect and predict demographics of faces, identify celebrities, and much more. Face2Face offers real-time facial capture and reenactment. Check out this clip of a speech by President Barack Obama which he never gave:

Software pioneer Marc Canter has developed a new AI-based storytelling platform called Instigate, which takes an Instagram or Snapchat story and adds intelligence and interactivity to create what he calls “beings” – who then can have content-enabled conversations with friends.

Canter, who developed Micromind Director multimedia authoring tools, sees Instigate as an AI authoring environment for a new form of storytelling. AI makes Instigate’s beings more intelligent than the standard-issue bots that perform repetitive pre-defined tasks. 

The Ubiquity of AI and ML

Over time, this new layer of AI/ML capabilities will become standard for every company and every product’s technology stack. It will generate billions of dollars for companies across the global business value chain. We can see how media businesses such as digital video, advertising, marketing and VR/AR are already fundamentally driven by AI and ML capabilities, as seen in these examples:

  • Digital Video: AI optimizes video encoding and delivery. Visual and pattern recognition automates editing and content creation. AI-based fingerprinting protects copyright and aids in licensing and micropayments. AI detects “anomalies” like piracy, violence, adult and fake content. AI will lead to almost real-time video quality assessment, which will lead to shorter timelines for content release. IBM’s Watson AI platform released what it called a cognitive movie trailer for the Fox film, Morgan, and has automated highlight reels for the World Cup and other sports events.
  • VR and AR: These applications depend on AI to create viable experiences, and are closely aligned with visual effects and game design. Cloud providers Google, Amazon and Microsoft, all of whom are committed to AR and VR as an engine of growth, are embedding AI into the platforms that will increasingly power immersive applications and experiences.

In the end, Hollywood is just like any other industry – as investor Ben Evans put it, “eventually, pretty much everything will have ML somewhere inside and no one will care.”

Two women standing face to face and between them is a glass wall.#source%3Dgooglier%2Ecom#https%3A%2F%2Fgooglier%2Ecom%2Fpage%2F%2F10000

The cognitive trailer for the AI thriller film Morgan was created with the help of IBM's AI platform, Watson.

Nick DeMartino is a Los Angeles-based media and technology consultant. He served as Senior Vice President of the American Film Institute. He has been part of the IDEABOOST team since its launch in 2012, now serving as chair of its Investment Advisory Group.

          Deep Learning Based Inference of Private Information Using Embedded Sensors in Smart Devices      Cache   Translate Page   Web Page Cache   
Smart mobile devices and mobile apps have been rolling out at swift speeds over the last decade, turning these devices into convenient and general-purpose computing platforms. Sensory data from smart devices are important resources to nourish mobile services, and they are regarded as innocuous information that can be obtained without user permissions. In this article, we show that this seemingly innocuous information could cause serious privacy issues. First, we demonstrate that users' tap positions on the screens of smart devices can be identified based on sensory data by employing some deep learning techniques. Second, it is shown that tap stream profiles for each type of apps can be collected, so that a user's app usage habit can be accurately inferred. In our experiments, the sensory data and mobile app usage information of 102 volunteers are collected. The experiment results demonstrate that the prediction accuracy of tap position inference can be at least 90 percent by utilizing convolutional neural networks. Furthermore, based on the inferred tap position information, users' app usage habits and passwords may be inferred with high accuracy.
          Privacy in Neural Network Learning: Threats and Countermeasures      Cache   Translate Page   Web Page Cache   
Algorithmic breakthroughs, the feasibility of collecting huge amount of data, and increasing computational power, contribute to the remarkable achievements of NNs. In particular, since Deep Neural Network (DNN) learning presents astonishing results in speech and image recognition, the amount of sophisticated applications based on it has exploded. However, the increasing number of instances of privacy leakage has been reported, and the corresponding severe consequences have caused great worry in this area. In this article, we focus on privacy issues in NN learning. First, we identify the privacy threats during NN training, and present privacy-preserving training schemes in terms of using centralized and distributed approaches. Second, we consider the privacy of prediction requests, and discuss the privacy-preserving protocols for NN prediction. We also analyze the privacy vulnerabilities of trained models. Three types of attacks on private information embedded in trained NN models are discussed, and a differential privacy-based solution is introduced.
          BETEGY is a startup providing data- and algorithm-based statistical football predictions and up-to-date sports - Rs. 222      Cache   Translate Page   Web Page Cache   
BETEGY's algorithm is built on over 350 various data points. Team dynamics, public news information, statistical models and neural network functionalities are t...
          Choosing a Neural Network      Cache   Translate Page   Web Page Cache   
Another excellent piece from Jason, suggest you join up with his service:

Jason Brownlee writes:   What neural network is appropriate for your predictive modeling problem?

It can be difficult for a beginner to the field of deep learning to know what type of network to use. There are so many types of networks to choose from and new methods being published and discussed every day.

To make things worse, most neural networks are flexible enough that they work (make a prediction) even when used with the wrong type of data or prediction problem.

In this post, you will discover the suggested use for the three main classes of artificial neural networks.

After reading this post, you will know:

Which types of neural networks to focus on when working on a predictive modeling problem.
When to use, not use, and possible try using an MLP, CNN, and RNN on a project. ... To consider the use of hybrid models and to have a clear idea of your project goals before selecting a model.

Let’s get started.  ... "

          Should I infect this PC, wonders malware. Let me ask my neural net...      Cache   Translate Page   Web Page Cache   

How does it work? Nobody really knows what goes on in the black box

Black Hat Here's perhaps a novel use of a neural network: proof-of-concept malware that uses AI to decide whether or not to attack a victim.…

          BETEGY is a startup providing data- and algorithm-based statistical football predictions and up-to-date sports -       Cache   Translate Page   Web Page Cache   
BETEGY's algorithm is built on over 350 various data points. Team dynamics, public news information, statistical models and neural network functionalities are t...
          BETEGY is a startup providing data- and algorithm-based statistical football predictions and up-to-date sports -       Cache   Translate Page   Web Page Cache   
BETEGY's algorithm is built on over 350 various data points. Team dynamics, public news information, statistical models and neural network functionalities are t...
          TractSeg - Fast and accurate white matter tract segmentation.      Cache   Translate Page   Web Page Cache   
Related Articles

TractSeg - Fast and accurate white matter tract segmentation.

Neuroimage. 2018 Aug 04;:

Authors: Wasserthal J, Neher P, Maier-Hein KH

The individual course of white matter fiber tracts is an important factor for analysis of white matter characteristics in healthy and diseased brains. Diffusion-weighted MRI tractography in combination with region-based or clustering-based selection of streamlines is a unique combination of tools which enables the in-vivo delineation and analysis of anatomically well-known tracts. This, however, currently requires complex, computationally intensive processing pipelines which take a lot of time to set up. TractSeg is a novel convolutional neural network-based approach that directly segments tracts in the field of fiber orientation distribution function (fODF) peaks without using tractography, image registration or parcellation. We demonstrate that the proposed approach is much faster than existing methods while providing unprecedented accuracy, using a population of 105 subjects from the Human Connectome Project. We also show initial evidence that TractSeg is able to generalize to differently acquired data sets for most of the bundles. The code and data are openly available at and, respectively.

PMID: 30086412 [PubMed - as supplied by publisher]

          A hand gesture could be your next password      Cache   Translate Page   Web Page Cache   

ByJackie Snow 2 minuteRead

A new system can look at a person’s finger making a motion in the air―like a signature or drawing a shape―to authenticate their identity. The framework,called FMCode, employs algorithms fed by a wearable sensor or camera, and can correctly identify users between 94.3% to 96.7% of the time on two different gesture devices after only seeing the passcode a few times, researchers say.



The method, described in a new paper by computer scientists Duo Lu and Dijiang Huang at Arizona State University, gets around some of the tricky privacy concerns surrounding biometrics like face recognition. It also overcomes the issue of remembering long strings of characters needed for most secure logins. Gesture interactions could be useful when a keyboard is impractical, like using a VR headset, or in a situation where minimizing contact with the surroundings is necessary for cleanliness, like an operating room.

In the paper, which was published on the preprint server this month, the researchers spell out some of the hurdles they had to overcome to develop FMCode. Unlike passwords, finger motions in the air won’t be exactly the same each time, so a system has to be robust enough to recognize slightly different speeds and shapes while still catching fraudulent attempts. The system has to be able to do that with only a few examples since most users would be unwilling to write their passcode hundreds or thousands of times.

To tackle those issues, the researchers turned to machine learning. The team designed classifiers that can spot spoofs while tolerating minor variations from the real user, and built a convolutional neural network (CNN) to index finger motion signals with data augmentation methods that limits the amount of training needed at setup.

A hand gesture could be your next password
User login through gesture interface using inertial sensor or 3D depth camera under two different scenarios: (left) VR applications with user mobility, (right) operating theater with touchless interface for doctors to maintain high cleanliness. [Images: courtesy of Duo Lu] Giving a finger

FMCode is pretty secure against most guessing attempts and spoofing, or when an attacker knows the gesture, the researchers say. But no system is foolproof. FMCode can be tricked if the system isn’t first set up to verify the user with an account ID. The researchers also say they are planning future work to study attacks where a person’s gesture passcode is recorded and then replayed later in an attempt to fool the system.

Whether many people will be interested in gesture control, at least anytime soon, remains to be seen. The interest in and development of the technology has waxed and waned over the years, with movies like Minority Report and Iron Man causing spikes in attention around the futuristic interactions. Nintendo released a wired glove that could control some gaming aspects to lackluster sales in 1989 to Leap Motion, which was released to good reviews at its launch in 2013 but is still not mainstream. Companies like Sony are trying to make gesture interfaces happen, while Facebook, Microsoft, Magic Leap, and others are betting that we’ll need gesture control in their VR and AR environments.

Related: The future of security? A good old-fashioned key


The researchers queried the participants in the study on their thoughts on using FMCode versus other login methods, like traditional passwords and face recognition on mobile devices. While FMCode scored high for security, the users found it generally less easy to use and worse for speed. Of course, with improved hardware and a future with more security breaches, those concerns could disappear with a wave of the hand.




          BETEGY is a startup providing data- and algorithm-based statistical football predictions and up-to-date sports - Rs. 2      Cache   Translate Page   Web Page Cache   
BETEGY's algorithm is built on over 350 various data points. Team dynamics, public news information, statistical models and neural network functionalities are t...
          Comment on Adding Animations to Your App – The Boring Flutter Development Show, Ep. 5 by Michael Charles      Cache   Translate Page   Web Page Cache   
Interplying Locomotions to your NeurAl Network mApp.<br />::<br />Code/Cascade; time crystalline.<br />:;<br />As taught by the Word of Reality, of All-Seeing God, Eye in tHis human vessel.<br />;:<br />Step by step. By I, the LORD of the World who has been the Master of the Eternal Spirit many many<br />times over; Michael (as I AM today); Jesus; Laozi; Adam; etc.
          Device-directed Utterance Detection. (arXiv:1808.02504v1 [cs.CL])      Cache   Translate Page   Web Page Cache   

Authors: Sri Harish Mallidi, Roland Maas, Kyle Goehner, Ariya Rastrow, Spyros Matsoukas, Björn Hoffmeister

In this work, we propose a classifier for distinguishing device-directed queries from background speech in the context of interactions with voice assistants. Applications include rejection of false wake-ups or unintended interactions as well as enabling wake-word free follow-up queries. Consider the example interaction: $"Computer,~play~music", "Computer,~reduce~the~volume"$. In this interaction, the user needs to repeat the wake-word ($Computer$) for the second query. To allow for more natural interactions, the device could immediately re-enter listening state after the first query (without wake-word repetition) and accept or reject a potential follow-up as device-directed or background speech. The proposed model consists of two long short-term memory (LSTM) neural networks trained on acoustic features and automatic speech recognition (ASR) 1-best hypotheses, respectively. A feed-forward deep neural network (DNN) is then trained to combine the acoustic and 1-best embeddings, derived from the LSTMs, with features from the ASR decoder. Experimental results show that ASR decoder, acoustic embeddings, and 1-best embeddings yield an equal-error-rate (EER) of $9.3~\%$, $10.9~\%$ and $20.1~\%$, respectively. Combination of the features resulted in a $44~\%$ relative improvement and a final EER of $5.2~\%$.

          Rethinking Numerical Representations for Deep Neural Networks. (arXiv:1808.02513v1 [cs.LG])      Cache   Translate Page   Web Page Cache   

Authors: Parker Hill, Babak Zamirai, Shengshuo Lu, Yu-Wei Chao, Michael Laurenzano, Mehrzad Samadi, Marios Papaefthymiou, Scott Mahlke, Thomas Wenisch, Jia Deng, Lingjia Tang, Jason Mars

With ever-increasing computational demand for deep learning, it is critical to investigate the implications of the numeric representation and precision of DNN model weights and activations on computational efficiency. In this work, we explore unconventional narrow-precision floating-point representations as it relates to inference accuracy and efficiency to steer the improved design of future DNN platforms. We show that inference using these custom numeric representations on production-grade DNNs, including GoogLeNet and VGG, achieves an average speedup of 7.6x with less than 1% degradation in inference accuracy relative to a state-of-the-art baseline platform representing the most sophisticated hardware using single-precision floating point. To facilitate the use of such customized precision, we also present a novel technique that drastically reduces the time required to derive the optimal precision configuration.

          Detection and Segmentation of Manufacturing Defects with Convolutional Neural Networks and Transfer Learning. (arXiv:1808.02518v1 [cs.CV])      Cache   Translate Page   Web Page Cache   

Authors: Max Ferguson, Ronay Ak, Yung-Tsun Tina Lee, Kincho H. Law

Automatic detection of defects in metal castings is a challenging task, owing to the rare occurrence and variation in appearance of defects. However, automatic defect detection systems can lead to significant increases in final product quality. Convolutional neural networks (CNNs) have shown outstanding performance in both image classification and localization tasks. In this work, a system is proposed for the identification of casting defects in X-ray images, based on the mask region-based CNN architecture. The proposed defect detection system simultaneously performs defect detection and segmentation on input images, making it suitable for a range of defect detection tasks. It is shown that training the network to simultaneously perform defect detection and defect instance segmentation, results in a higher defect detection accuracy than training on defect detection alone. Transfer learning is leveraged to reduce the training data demands and increase the prediction accuracy of the trained model. More specifically, the model is first trained with two large openly-available image datasets before fine-tuning on a relatively small metal casting X-ray dataset. The accuracy of the trained model exceeds state-of-the art performance on the GDXray Castings dataset and is fast enough to be used in a production setting. The system also performs well on the GDXray Welds dataset. A number of in-depth studies are conducted to explore how transfer learning, multi-task learning, and multi-class learning influence the performance of the trained system.

          SchiNet: Automatic Estimation of Symptoms of Schizophrenia from Facial Behaviour Analysis. (arXiv:1808.02531v1 [cs.CV])      Cache   Translate Page   Web Page Cache   

Authors: Mina Bishay, Petar Palasek, Stefan Priebe, Ioannis Patras

Patients with schizophrenia often display impairments in the expression of emotion and speech and those are observed in their facial behaviour. Automatic analysis of patients' facial expressions that is aimed at estimating symptoms of schizophrenia has received attention recently. However, the datasets that are typically used for training and evaluating the developed methods, contain only a small number of patients (4-34) and are recorded while the subjects were performing controlled tasks such as listening to life vignettes, or answering emotional questions. In this paper, we use videos of professional-patient interviews, in which symptoms were assessed in a standardised way as they should/may be assessed in practice, and which were recorded in realistic conditions (i.e. varying illumination levels and camera viewpoints) at the patients' homes or at mental health services. We automatically analyse the facial behaviour of 91 out-patients - this is almost 3 times the number of patients in other studies - and propose SchiNet, a novel neural network architecture that estimates expression-related symptoms in two different assessment interviews. We evaluate the proposed SchiNet for patient-independent prediction of symptoms of schizophrenia. Experimental results show that some automatically detected facial expressions are significantly correlated to symptoms of schizophrenia, and that the proposed network for estimating symptom severity delivers promising results.

          Design Challenges in Named Entity Transliteration. (arXiv:1808.02563v1 [cs.CL])      Cache   Translate Page   Web Page Cache   

Authors: Yuval Merhav, Stephen Ash

We analyze some of the fundamental design challenges that impact the development of a multilingual state-of-the-art named entity transliteration system, including curating bilingual named entity datasets and evaluation of multiple transliteration methods. We empirically evaluate the transliteration task using traditional weighted finite state transducer (WFST) approach against two neural approaches: the encoder-decoder recurrent neural network method and the recent, non-sequential Transformer method. In order to improve availability of bilingual named entity transliteration datasets, we release personal name bilingual dictionaries minded from Wikidata for English to Russian, Hebrew, Arabic and Japanese Katakana. Our code and dictionaries are publicly available.

          Parallax: Automatic Data-Parallel Training of Deep Neural Networks. (arXiv:1808.02621v1 [cs.DC])      Cache   Translate Page   Web Page Cache   

Authors: Soojeong Kim, Gyeong-In Yu, Hojin Park, Sungwoo Cho, Eunji Jeong, Hyeonmin Ha, Sanha Lee, Joo Seong Jeong, Byung-Gon Chun

The employment of high-performance servers and GPU accelerators for training deep neural network models have greatly accelerated recent advances in machine learning (ML). ML frameworks, such as TensorFlow, MXNet, and Caffe2, have emerged to assist ML researchers to train their models in a distributed fashion. However, correctly and efficiently utilizing multiple machines and GPUs is still not a straightforward task for framework users due to the non-trivial correctness and performance challenges that arise in the distribution process. This paper introduces Parallax, a tool for automatic parallelization of deep learning training in distributed environments. Parallax not only handles the subtle correctness issues, but also leverages various optimizations to minimize the communication overhead caused by scaling out. Experiments show that Parallax built atop TensorFlow achieves scalable training throughput on multiple CNN and RNN models, while requiring little effort from its users.

          Training Compact Neural Networks with Binary Weights and Low Precision Activations. (arXiv:1808.02631v1 [cs.CV])      Cache   Translate Page   Web Page Cache   

Authors: Bohan Zhuang, Chunhua Shen, Ian Reid

In this paper, we propose to train a network with binary weights and low-bitwidth activations, designed especially for mobile devices with limited power consumption. Most previous works on quantizing CNNs uncritically assume the same architecture, though with reduced precision. However, we take the view that for best performance it is possible (and even likely) that a different architecture may be better suited to dealing with low precision weights and activations.

Specifically, we propose a "network expansion" strategy in which we aggregate a set of homogeneous low-precision branches to implicitly reconstruct the full-precision intermediate feature maps. Moreover, we also propose a group-wise feature approximation strategy which is very flexible and highly accurate. Experiments on ImageNet classification tasks demonstrate the superior performance of the proposed model, named Group-Net, over various popular architectures. In particular, with binary weights and activations, we outperform the previous best binary neural network in terms of accuracy as well as saving more than 5 times computational complexity on ImageNet with ResNet-18 and ResNet-50.

          Question-Guided Hybrid Convolution for Visual Question Answering. (arXiv:1808.02632v1 [cs.CV])      Cache   Translate Page   Web Page Cache   

Authors: Peng Gao, Pan Lu, Hongsheng Li, Shuang Li, Yikang Li, Steven Hoi, Xiaogang Wang

In this paper, we propose a novel Question-Guided Hybrid Convolution (QGHC) network for Visual Question Answering (VQA). Most state-of-the-art VQA methods fuse the high-level textual and visual features from the neural network and abandon the visual spatial information when learning multi-modal features.To address these problems, question-guided kernels generated from the input question are designed to convolute with visual features for capturing the textual and visual relationship in the early stage. The question-guided convolution can tightly couple the textual and visual information but also introduce more parameters when learning kernels. We apply the group convolution, which consists of question-independent kernels and question-dependent kernels, to reduce the parameter size and alleviate over-fitting. The hybrid convolution can generate discriminative multi-modal features with fewer parameters. The proposed approach is also complementary to existing bilinear pooling fusion and attention based VQA methods. By integrating with them, our method could further boost the performance. Extensive experiments on public VQA datasets validate the effectiveness of QGHC.

          Natural Language Generation by Hierarchical Decoding with Linguistic Patterns. (arXiv:1808.02747v1 [cs.CL])      Cache   Translate Page   Web Page Cache   

Authors: Shang-Yu Su, Kai-Ling Lo, Yi-Ting Yeh, Yun-Nung Chen

Natural language generation (NLG) is a critical component in spoken dialogue systems. Classic NLG can be divided into two phases: (1) sentence planning: deciding on the overall sentence structure, (2) surface realization: determining specific word forms and flattening the sentence structure into a string. Many simple NLG models are based on recurrent neural networks (RNN) and sequence-to-sequence (seq2seq) model, which basically contains an encoder-decoder structure; these NLG models generate sentences from scratch by jointly optimizing sentence planning and surface realization using a simple cross entropy loss training criterion. However, the simple encoder-decoder architecture usually suffers from generating complex and long sentences, because the decoder has to learn all grammar and diction knowledge. This paper introduces a hierarchical decoding NLG model based on linguistic patterns in different levels, and shows that the proposed method outperforms the traditional one with a smaller model size. Furthermore, the design of the hierarchical decoding is flexible and easily-extensible in various NLG systems.

          Choose Your Neuron: Incorporating Domain Knowledge through Neuron-Importance. (arXiv:1808.02861v1 [cs.CV])      Cache   Translate Page   Web Page Cache   

Authors: Ramprasaath R. Selvaraju, Prithvijit Chattopadhyay, Mohamed Elhoseiny, Tilak Sharma, Dhruv Batra, Devi Parikh, Stefan Lee

Individual neurons in convolutional neural networks supervised for image-level classification tasks have been shown to implicitly learn semantically meaningful concepts ranging from simple textures and shapes to whole or partial objects - forming a "dictionary" of concepts acquired through the learning process. In this work we introduce a simple, efficient zero-shot learning approach based on this observation. Our approach, which we call Neuron Importance-AwareWeight Transfer (NIWT), learns to map domain knowledge about novel "unseen" classes onto this dictionary of learned concepts and then optimizes for network parameters that can effectively combine these concepts - essentially learning classifiers by discovering and composing learned semantic concepts in deep networks. Our approach shows improvements over previous approaches on the CUBirds and AWA2 generalized zero-shot learning benchmarks. We demonstrate our approach on a diverse set of semantic inputs as external domain knowledge including attributes and natural language captions. Moreover by learning inverse mappings, NIWT can provide visual and textual explanations for the predictions made by the newly learned classifiers and provide neuron names. Our code is available at

          Additional Representations for Improving Synthetic Aperture Sonar Classification Using Convolutional Neural Networks. (arXiv:1808.02868v1 [cs.CV])      Cache   Translate Page   Web Page Cache   

Authors: Isaac D Gerg, David P Williams

Object classification in synthetic aperture sonar (SAS) imagery is usually a data starved and class imbalanced problem. There are few objects of interest present among much benign seafloor. Despite these problems, current classification techniques discard a large portion of the collected SAS information. In particular, a beamformed SAS image, which we call a single-look complex (SLC) image, contains complex pixels composed of real and imaginary parts. For human consumption, the SLC is converted to a magnitude-phase representation and the phase information is discarded. Even more problematic, the magnitude information usually exhibits a large dynamic range (>80dB) and must be dynamic range compressed for human display. Often it is this dynamic range compressed representation, originally designed for human consumption, which is fed into a classifier. Consequently, the classification process is completely void of the phase information. In this work, we show improvements in classification performance using the phase information from the SLC as well as information from an alternate source: photographs. We perform statistical testing to demonstrate the validity of our results.

          Parkinson's Disease Assessment from a Wrist-Worn Wearable Sensor in Free-Living Conditions: Deep Ensemble Learning and Visualization. (arXiv:1808.02870v1 [cs.CV])      Cache   Translate Page   Web Page Cache   

Authors: Terry Taewoong Um, Franz Michael Josef Pfister, Daniel Christian Pichler, Satoshi Endo, Muriel Lang, Sandra Hirche, Urban Fietzek, Dana Kulić

Parkinson's Disease (PD) is characterized by disorders in motor function such as freezing of gait, rest tremor, rigidity, and slowed and hyposcaled movements. Medication with dopaminergic medication may alleviate those motor symptoms, however, side-effects may include uncontrolled movements, known as dyskinesia. In this paper, an automatic PD motor-state assessment in free-living conditions is proposed using an accelerometer in a wrist-worn wearable sensor. In particular, an ensemble of convolutional neural networks (CNNs) is applied to capture the large variability of daily-living activities and overcome the dissimilarity between training and test patients due to the inter-patient variability. In addition, class activation map (CAM), a visualization technique for CNNs, is applied for providing an interpretation of the results.

          Visualizing Convolutional Networks for MRI-based Diagnosis of Alzheimer's Disease. (arXiv:1808.02874v1 [cs.CV])      Cache   Translate Page   Web Page Cache   

Authors: Johannes Rieke, Fabian Eitel, Martin Weygandt, John-Dylan Haynes, Kerstin Ritter

Visualizing and interpreting convolutional neural networks (CNNs) is an important task to increase trust in automatic medical decision making systems. In this study, we train a 3D CNN to detect Alzheimer's disease based on structural MRI scans of the brain. Then, we apply four different gradient-based and occlusion-based visualization methods that explain the network's classification decisions by highlighting relevant areas in the input image. We compare the methods qualitatively and quantitatively. We find that all four methods focus on brain regions known to be involved in Alzheimer's disease, such as inferior and middle temporal gyrus. While the occlusion-based methods focus more on specific regions, the gradient-based methods pick up distributed relevance patterns. Additionally, we find that the distribution of relevance varies across patients, with some having a stronger focus on the temporal lobe, whereas for others more cortical areas are relevant. In summary, we show that applying different visualization methods is important to understand the decisions of a CNN, a step that is crucial to increase clinical impact and trust in computer-based decision support systems.

          Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks. (arXiv:1703.10371v3 [cs.NE] UPDATED)      Cache   Translate Page   Web Page Cache   

Authors: Andrea Soltoggio, Kenneth O. Stanley, Sebastian Risi

Biological plastic neural networks are systems of extraordinary computational capabilities shaped by evolution, development, and lifetime learning. The interplay of these elements leads to the emergence of adaptive behavior and intelligence. Inspired by such intricate natural phenomena, Evolved Plastic Artificial Neural Networks (EPANNs) use simulated evolution in-silico to breed plastic neural networks with a large variety of dynamics, architectures, and plasticity rules: these artificial systems are composed of inputs, outputs, and plastic components that change in response to experiences in an environment. These systems may autonomously discover novel adaptive algorithms, and lead to hypotheses on the emergence of biological adaptation. EPANNs have seen considerable progress over the last two decades. Current scientific and technological advances in artificial neural networks are now setting the conditions for radically new approaches and results. In particular, the limitations of hand-designed networks could be overcome by more flexible and innovative solutions. This paper brings together a variety of inspiring ideas that define the field of EPANNs. The main methods and results are reviewed. Finally, new opportunities and developments are presented.

          Deep Rewiring: Training very sparse deep networks. (arXiv:1711.05136v5 [cs.NE] UPDATED)      Cache   Translate Page   Web Page Cache   

Authors: Guillaume Bellec, David Kappel, Wolfgang Maass, Robert Legenstein

Neuromorphic hardware tends to pose limits on the connectivity of deep networks that one can run on them. But also generic hardware and software implementations of deep learning run more efficiently for sparse networks. Several methods exist for pruning connections of a neural network after it was trained without connectivity constraints. We present an algorithm, DEEP R, that enables us to train directly a sparsely connected neural network. DEEP R automatically rewires the network during supervised training so that connections are there where they are most needed for the task, while its total number is all the time strictly bounded. We demonstrate that DEEP R can be used to train very sparse feedforward and recurrent neural networks on standard benchmark tasks with just a minor loss in performance. DEEP R is based on a rigorous theoretical foundation that views rewiring as stochastic sampling of network configurations from a posterior.

          A Scalable Near-Memory Architecture for Training Deep Neural Networks on Large In-Memory Datasets. (arXiv:1803.04783v2 [cs.DC] UPDATED)      Cache   Translate Page   Web Page Cache   

Authors: Fabian Schuiki, Michael Schaffner, Frank K. Gürkaynak, Luca Benini

Most investigations into near-memory hardware accelerators for deep neural networks have primarily focused on inference, while the potential of accelerating training has received relatively little attention so far. Based on an in-depth analysis of the key computational patterns in state-of-the-art gradient-based training methods, we propose an efficient near-memory acceleration engine called NTX that can be used to train state-of-the-art deep convolutional neural networks at scale. Our main contributions are: (i) a loose coupling of RISC-V cores and NTX co-processors reducing offloading overhead by 7x over previously published results; (ii) an optimized IEEE754 compliant data path for fast high-precision convolutions and gradient propagation; (iii) evaluation of near-memory computing with NTX embedded into residual area on the Logic Base die of a Hybrid Memory Cube; and (iv) a scaling analysis to meshes of HMCs in a data center scenario. We demonstrate a 2.7x energy efficiency improvement of NTX over contemporary GPUs at 4.4x less silicon area, and a compute performance of 1.2 Tflop/s for training large state-of-the-art networks with full floating-point precision. At the data center scale, a mesh of NTX achieves above 95% parallel and energy efficiency, while providing 2.1x energy savings or 3.1x performance improvement over a GPU-based system.

          Averaging Weights Leads to Wider Optima and Better Generalization. (arXiv:1803.05407v2 [cs.LG] UPDATED)      Cache   Translate Page   Web Page Cache   

Authors: Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry Vetrov, Andrew Gordon Wilson

Deep neural networks are typically trained by optimizing a loss function with an SGD variant, in conjunction with a decaying learning rate, until convergence. We show that simple averaging of multiple points along the trajectory of SGD, with a cyclical or constant learning rate, leads to better generalization than conventional training. We also show that this Stochastic Weight Averaging (SWA) procedure finds much broader optima than SGD, and approximates the recent Fast Geometric Ensembling (FGE) approach with a single model. Using SWA we achieve notable improvement in test accuracy over conventional SGD training on a range of state-of-the-art residual networks, PyramidNets, DenseNets, and Shake-Shake networks on CIFAR-10, CIFAR-100, and ImageNet. In short, SWA is extremely easy to implement, improves generalization, and has almost no computational overhead.

          A Machine Learning Framework for Stock Selection. (arXiv:1806.01743v2 [q-fin.PM] UPDATED)      Cache   Translate Page   Web Page Cache   

Authors: XingYu Fu, JinHong Du, YiFeng Guo, MingWen Liu, Tao Dong, XiuWen Duan

This paper demonstrates how to apply machine learning algorithms to distinguish good stocks from the bad stocks. To this end, we construct 244 technical and fundamental features to characterize each stock, and label stocks according to their ranking with respect to the return-to-volatility ratio. Algorithms ranging from traditional statistical learning methods to recently popular deep learning method, e.g. Logistic Regression (LR), Random Forest (RF), Deep Neural Network (DNN), and the Stacking, are trained to solve the classification task. Genetic Algorithm (GA) is also used to implement feature selection. The effectiveness of the stock selection strategy is validated in Chinese stock market in both statistical and practical aspects, showing that: 1) Stacking outperforms other models reaching an AUC score of 0.972; 2) Genetic Algorithm picks a subset of 114 features and the prediction performances of all models remain almost unchanged after the selection procedure, which suggests some features are indeed redundant; 3) LR and DNN are radical models; RF is risk-neutral model; Stacking is somewhere between DNN and RF. 4) The portfolios constructed by our models outperform market average in back tests.

          A Multi-task Deep Learning Architecture for Maritime Surveillance using AIS Data Streams. (arXiv:1806.03972v3 [cs.LG] UPDATED)      Cache   Translate Page   Web Page Cache   

Authors: Duong Nguyen, Rodolphe Vadaine, Guillaume Hajduch, René Garello, Ronan Fablet

In a world of global trading, maritime safety, security and efficiency are crucial issues. We propose a multi-task deep learning framework for vessel monitoring using Automatic Identification System (AIS) data streams. We combine recurrent neural networks with latent variable modeling and an embedding of AIS messages to a new representation space to jointly address key issues to be dealt with when considering AIS data streams: massive amount of streaming data, noisy data and irregular timesampling. We demonstrate the relevance of the proposed deep learning framework on real AIS datasets for a three-task setting, namely trajectory reconstruction, anomaly detection and vessel type identification.

          RAPIDNN: In-Memory Deep Neural Network Acceleration Framework. (arXiv:1806.05794v2 [cs.NE] UPDATED)      Cache   Translate Page   Web Page Cache   

Authors: Mohsen Imani, Mohammad Samragh, Yeseong Kim, Saransh Gupta, Farinaz Koushanfar, Tajana Rosing

Deep neural networks (DNN) have demonstrated effectiveness for various applications such as image processing, video segmentation, and speech recognition. Running state-of-theart DNNs on current systems mostly relies on either generalpurpose processors, ASIC designs, or FPGA accelerators, all of which suffer from data movements due to the limited onchip memory and data transfer bandwidth. In this work, we propose a novel framework, called RAPIDNN, which processes all DNN operations within the memory to minimize the cost of data movement. To enable in-memory processing, RAPIDNN reinterprets a DNN model and maps it into a specialized accelerator, which is designed using non-volatile memory blocks that model four fundamental DNN operations, i.e., multiplication, addition, activation functions, and pooling. The framework extracts representative operands of a DNN model, e.g., weights and input values, using clustering methods to optimize the model for in-memory processing. Then, it maps the extracted operands and their precomputed results into the accelerator memory blocks. At runtime, the accelerator identifies computation results based on efficient in-memory search capability which also provides tunability of approximation to further improve computation efficiency. Our evaluation shows that RAPIDNN achieves 68.4x, 49.5x energy efficiency improvement and 48.1x, 10.9x speedup as compared to ISAAC and PipeLayer, the state-of-the-art DNN accelerators, while ensuring less than 0.3% of quality loss.

          BETEGY is a startup providing data- and algorithm-based statistical football predictions and up-to-date sports - Rs. 231      Cache   Translate Page   Web Page Cache   
BETEGY's algorithm is built on over 350 various data points. Team dynamics, public news information, statistical models and neural network functionalities are t...
          how to use neural networks to exclude a lottery number?      Cache   Translate Page   Web Page Cache   
Lottery Discussion forum
Reply #3
im just Checking the new and winning numbers on L-O-T-T-O-P-I-A to have a sure hit predict
          How Aphex Twin’s “T69 Collapse” video used a neural network for hallucinatory visuals      Cache   Translate Page   Web Page Cache   

“I feel I’m still merely scratching the surface.”

For the last few years, musician Aphex Twin’s visuals have been created by an equally elusive and hermetic artist–Nicky Smith, aka Weirdcore. Last summer, for instance, Weirdcore created dark yet hilarious visuals for Aphex Twin’s shows at Primavera Sound and Field Day Festival, which looked like the hallucinations of some corrupted artificial intelligence.

Read Full Story

          BETEGY is a startup providing data- and algorithm-based statistical football predictions and up-to-date sports -       Cache   Translate Page   Web Page Cache   
BETEGY's algorithm is built on over 350 various data points. Team dynamics, public news information, statistical models and neural network functionalities are t...
          Introducing the Splunk Machine Learning Toolkit Version 3.4      Cache   Translate Page   Web Page Cache   
Check out key features in the Splunk Machine Learning Toolkit version 3.4, including new functionalities, more visualization and a neural network algorithm out of the box
          الشبكات العصبية والتعليم العميق neural network Perceptrons الجزء الثالث – Arab University      Cache   Translate Page   Web Page Cache   
          Neural Network Software Market Expected to Reach 22.55 Billion USD by 2021      Cache   Translate Page   Web Page Cache   

Northbrook, IL -- (SBWIRE) -- 08/09/2018 -- Analytical tool is expected to dominate the neural network software market in terms of software type

The research study for global neural network software market encompasses the analysis of the market on the basis of software types, which is further segmented into data mining and archiving, analytical software, visualization software, and optimization software. The deployment of analytical software is mainly driven by the increasing demand for data predictive solutions across various end-use sectors especially in Banking, Financial Services, and Insurance (BFSI), healthcare, energy & utilities, and media.

BFSI sector is expected to hold the largest market share

The neural network software end users are segmented into BFSI, government & defense, energy & utilities, media, healthcare, industrial manufacturing, retail & eCommerce, transportation & logistics, telecom & IT, and others. The BFSI sector holds the large scale application areas for neural network technology, which include stock market analysis, foreign exchange perdition, and other such activities, thereby holding the largest market share among other end-use verticals studied for the market analysis.

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North America is expected to be the most lucrative market in 2016

The research study encompasses regional market analysis for North America, Europe, Asia-Pacific (APAC), Middle East and Africa (MEA), and Latin America along with some of the major countries in the specific regions. North America is expected to hold the largest share of the neural network software market in 2016, followed by Europe.

The rapid developments in infrastructure and higher adoption of digital technologies are the two major drivers that increase the demand for the neural network software market. Furthermore, the U.S is the most technologically advanced region with the presence of different business verticals such as BFSI, healthcare, retail & eCommerce, energy & utilities, and many others.

The prominent players in the artificial neural network ecosystem are Google Inc. (California, U.S.), IBM Corporation (New York, U.S.), Microsoft Corporation (Washington DC, U.S.), Intel Corporation (California, U.S.), Oracle Corporation (California, U.S.), SAP SE (Waldorf, Germany), and Qualcomm Technologies Inc. (California, U.S.). The key innovators concentrating mainly on neural network software include Alyuda Research LLC (California, U.S.), Neural Technologies Ltd. (England, U.K.), Ward Systems Group Inc. (Maryland U.S.), Afiniti (Washington DC, U.S.), GMDH LLC (New York, U.S.), Starmind International AG (Kuesnacht, Switzerland), Neuralware (Pennsylvania, U.S.), Slagkryssaren AB (Stockholm, Sweden), AND Corporation (Ontario, Canada), and Swiftkey (London, U.K.).

About MarketsandMarkets™
MarketsandMarkets™ provides quantified B2B research on 30,000 high growth niche opportunities/threats which will impact 70% to 80% of worldwide companies' revenues. Currently servicing 7500 customers worldwide including 80% of global Fortune 1000 companies as clients. Almost 75,000 top officers across eight industries worldwide approach MarketsandMarkets™ for their painpoints around revenues decisions.

Our 850 fulltime analyst and SMEs at MarketsandMarkets™ are tracking global high growth markets following the "Growth Engagement Model – GEM". The GEM aims at proactive collaboration with the clients to identify new opportunities, identify most important customers, write "Attack, avoid and defend" strategies, identify sources of incremental revenues for both the company and its competitors. MarketsandMarkets™ now coming up with 1,500 MicroQuadrants (Positioning top players across leaders, emerging companies, innovators, strategic players) annually in high growth emerging segments. MarketsandMarkets™ is determined to benefit more than 10,000 companies this year for their revenue planning and help them take their innovations/disruptions early to the market by providing them research ahead of the curve.

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          SA sees more digital initiatives focusing on learners | Training and e-Learning - ITWeb      Cache   Translate Page   Web Page Cache   
"I-Innovate, Qberty and IFS introduce programmes to help students excel in the digital age" notes Regina Pazvakavambwa, an experienced Journalist.
Digital skills initiatives help educators keep up with the changes technology innovation is bringing to classrooms.
Photo: ITWeb

The race to teach students skills is heating up, with three companies this week introducing digital learning initiatives.

Educational specialist company I-Innovate introduced the " and Robotics for the Future" programme at the Diepsloot Combined School.

Sponsored by IT service provider Tata Consultancy Services (TCS) SA, the programme helps learners and educators explore advanced (AI) technologies such as automation, machine learning, pattern recognition and neural networks through a series of hands-on innovation sessions, says I-Innovate.

Grade nine students from the school will learn how to create and use AI to problem-solve and innovate in their own lives and communities. TCS employees will mentor learner-led teams throughout the experience, both in-person and online.

"[The programme] connects learning in the classroom to real-world opportunities and career pathways. It is an inspiring and highly relevant way to show children that they can make giant leaps in learning and be a real part of the solutions to some of our most pressing local and global challenges," says I-Innovate CEO Trisha Crookes.

Learners will be introduced to coding and robotics, and will discover how to use these technologies for creative problem-solving, she notes...

Coding for innovation Meanwhile, Qberty has opened the Coders and Innovators Hub, at Northriding and Northlands Corner Shopping Centre in Randburg.

The centre will be open to children of all ages; however, the main target market is public schools, says Qberty. The company hopes to bridge the digital gap between private and public schools, and offer students an equal chance of digital experiential learning, it says.

Children will learn how to code, create apps and Web sites, and build robotics, notes Qberty, but highlights its main focus is Minecraft in education.

Source: ITWeb

          EPISODE 463 - Full Body Elixir by Day, Lucid Dreaming at Night      Cache   Translate Page   Web Page Cache   
For Beyond 50's "Natural Healing & Spirituality" talks, listen to an interview with Calley O'Neill and her son, Noa Eads. Learn from Calley about a self-healing practice she developed called Full Body Elixir that is a fun fusion of yoga, qigong, deep breathing and isometrics in a slow flow of practical love and active kindness. Full Body Elixir cleanses your neural network, recharges your immune system, reinvigorates, and strengthens your body. Calley's son Noa, an avid lucid dreamer and dream teacher, will describe the basic practices and benefits of lucid dreaming, and teach you skills for developing lucidity in waking and dreaming life. Tune in to Beyond 50: America's Variety Talk Radio Show on the natural, holistic, green and spiritual lifestyle. Visit and sign up for our Exclusive Updates.
          Senior Analytics Engineer/R Developer      Cache   Translate Page   Web Page Cache   
CA-San Francisco, Role: Senior Analytics Engineer / R Developer Location: San Francisco, CA Duration: Long-Term Qualifications: Tools: R, Python, Spark, Hadoop, SAS, SQL (SQL Server, Teradata) Modeling concepts: Machine Learning, Time series analysis, Clustering, Generalized Linear and Additive Models, Nonlinear Regression, Classification, Neural Networks, Decision trees, Text mining, OCR Utility or regulated indus
          Cognitive Factors in Students' Academic Performance Evaluation using Artificial Neural Networks      Cache   Translate Page   Web Page Cache   

Performance evaluation based on some cognitive factors especially Students’ Intelligent Quotient rating (IQR), Confidence Level (CoL) and Time Management ability gives an equal platform for better evaluation of students’ performance using Artificial Neural Network. Artificial Neural Networks (ANN) models, which has the advantage of being trained, offers a more robust methodology and tool for predicting, forecasting and modeling phenomena to ascertain conformance to desired standards as well as assist in decision making. This work employs Machine Learning and cognitive science which uses Artificial Neural networks (ANNs) to evaluated students’ academic performance in the Department of Computer Science, Akwa Ibom State University. It presents a survey of the design, building and functionalities of Artificial Neural Network for the evaluation of students’ academic performance using cognitive factors that could affect student’s performances.

Keywords: Cognitive, Intelligent Quotient Rating, Machine Learning, Artificial Neural Network.


          Come lavorano i Data Scientist?      Cache   Translate Page   Web Page Cache   
Analizzare, gestire e convertire i big data in informazioni utili per ottimizzare il mercato permette di migliorare la customer experience creando offerte personalizzate, non invasive e user oriented sfruttando le logiche dell’IA attraverso lo studio dell’artificial neural networks e del machine learning.
          Senior Analytics Engineer/R Developer      Cache   Translate Page   Web Page Cache   
CA-San Francisco, Role: Senior Analytics Engineer / R Developer Location: San Francisco, CA Duration: Long-Term Qualifications: Tools: R, Python, Spark, Hadoop, SAS, SQL (SQL Server, Teradata) Modeling concepts: Machine Learning, Time series analysis, Clustering, Generalized Linear and Additive Models, Nonlinear Regression, Classification, Neural Networks, Decision trees, Text mining, OCR Utility or regulated indus
          how to use neural networks to exclude a lottery number?      Cache   Translate Page   Web Page Cache   
Lottery Discussion forum
Reply #3
hello =BOBP the binary is to find paths to see the empty ranges or blocks

in the lines and columns Cartesian type, ok we have two conditions in the lottery = 1 try to hit the numbers, 2 try to find blocks to delete numbers, with number and also the last digit,

clear that in the neural network will have to create special trees for lotteries, analyzing repetitions

and cycles.

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