PERFORMANCE


Machine Learning Classification Algorithms using MATLAB

@machinelearnbot

This course is designed to cover one of the most interesting areas of machine learning called classification. Following that we will look into the details of how to use different machine learning algorithms using MATLAB. Specifically, we will be looking at the MATLAB toolbox called statistic and machine learning toolbox.We will implement some of the most commonly used classification algorithms such as K-Nearest Neighbor, Naive Bayes, Discriminant Analysis, Decision Tress, Support Vector Machines, Error Correcting Ouput Codes and Ensembles. Though it does not cover Matlab toolboxes etc, it is still a great basic introduction for the platform.


Data Science Simplified Part 7: Log-Log Regression Models

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So does it mean for linear regression models? Hypothesis testing discussed the concept of NULL and alternate hypothesis. Simple linear regression models made regression simple. So far the regression models built had only numeric independent variables.


Deep Learning and Neural Networks Primer: Basic Concepts for Beginners

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Enjoying a surge in research and industry, due mainly to its incredible successes in a number of different areas, deep learning is the process of applying deep neural network technologies - that is, neural network architectures with multiple hidden layers - to solve problems. Deep learning is a process, like data mining, which employs deep neural network architectures, which are particular types of machine learning algorithms. In this chapter we will look at how we can train an ANN algorithm to recognize images of handwritten digits. Matching the performance of a human brain is a difficult feat, but techniques have been developed to improve the performance of neural network algorithms, 3 of which are discussed in this post: Distortion, mini-batch gradient descent, dropout.


Get your priorities straight before you start applying artificial intelligence in your business solutions

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AI solutions require vast amounts of good data to be meaningful. This slowdown offers companies an opportunity to focus on the right thing: building a proper business technology platform to enable AI solutions when they are ready for mass market implementations. It provides basic data models and analytical capabilities that can make the company smarter overtime. It enables machine learning, predictive analytics, deep learning, natural language processing and other AI capabilities.


How to Regulate Dangerous Artificial Intelligence – Intuition Machine – Medium

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Please, in your comments, avoid short cutting the discussion by asking why AI regulation is needed. Level 1 (Attended Process) -- Users are aware of the initiation and completion of the performance of each automated task. Level 5 (Fully Automated Process) -This is a final and future state where human involvement in the processes is not required. Level 6 (Self Optimizing Process) -This is an automation that requires no human involvement and is also capable of improving itself over time.


A.I. Business Applications (and How It May Impact You)

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Technology companies of all sizes and in locations all around the world are developing AI-driven products aimed at reducing operating costs, improving decision-making and enhancing consumer services across a range of client industries. The sum of these drivers -- new programming techniques, more data and faster chips -- has seen AI converge with human-level performance in the key areas of image classification and speech recognition over recent years (see EXHIBIT 2). Chipmakers stand to benefit from increased demand for processing power, particularly makers of graphical processing units for AI program training. And internet companies with AI at the core of their consumer services (such as digital assistants and new software features) stand to benefit directly from improvements in speech recognition and image classification.


Deep Learning for NLP Best Practices

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I will then outline practices that are relevant for the most common tasks, in particular classification, sequence labelling, natural language generation, and neural machine translation. For training deep neural networks, some tricks are essential to avoid the vanishing gradient problem. Let us augment the layer output \(h\) and layer input \(x\) with indices \(l\) indicating the current layer. They have also found to be useful for Multi-Task Learning of different NLP tasks (Ruder et al., 2017) [49], while a residual variant that uses summation has been shown to consistently outperform residual connections for neural machine translation (Britz et al., 2017) [27].


Supporting humans and networks: AI and machine learning

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They can correctly mitigate the effects of latency without your organisation having to unnecessarily spend money on ever increasingly large bandwidths, WAN Optimisation, SD-WAN and WAN optimisation solutions. Boulton also explains: "SD-WANs allow companies to set up and manage networking functionality, including VPNs, WAN optimisation, VoIP and firewalls, using software to program traffic routing typically conducted by routers and switches. What's certain is that data acceleration makes big data and predictive analytics increasingly viable. On the other hand, data acceleration solutions can create performance increases.


AI the 'big winner' as banks and fund managers dig deep on tech

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Octavio Marenzi, chief executive of the consultancy Opimas, which this week published a report entitled Fintech Spending and Innovation in Capital Markets, said AI would be "be the big winner" as banks, brokers, fund managers and other firms poured money into new technologies and data sources. The report from Opimas found that AI would have the most potential to transform banks' sales and trading divisions and the way fund managers make investment decisions. Financial firms that have this year shown their intent in this area include the £166.6bn Opimas said spending on AI would hit $1.68bn across the capital markets this year, increasing by 14% next year and hitting just under $3bn by 2021. Mark Beeston, the founder and chief executive officer of venture capital firm Illuminate Financial Management, told Financial News in May: "We went through a blockchain hype cycle and now we're going through an AI hype cycle... AI for the sake of AI is not the answer."


Can Machine Learning Make HR Better? - TalentCulture

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AI programs have unique capabilities to analyze past experiences outlined on a resume, as well as personality traits revealed during the interview process, and compare this data to that of successful workers in a given position, helping HR executives match the best talent to the right job. AI can analyze data and make predictions faster and with a greater degree of accuracy. Meghan's thought leadership in HR technology, social strategy, and the future of work has helped hundreds of companies--from early-stage ventures to major brands--successfully recruit and empower stellar talent. Meghan has been voted one of the Top 100 Social Media Power Influencers by StatSocial and Forbes, Top 50 Most Valuable Social Media Influencers by General Sentiment, Top 100 on Twitter Business, Leadership, and Tech by Huffington Post, Top 25 HR Trendsetters by HR Examiner, and is a go-to expert resource for all things talent, branding, HR tech, leadership, and digital media.