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Framework for Data Preparation Techniques in Machine Learning

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There are a vast number of different types of data preparation techniques that could be used on a predictive modeling project. In some cases, the distribution of the data or the requirements of a machine learning model may suggest the data preparation needed, although this is rarely the case given the complexity and high-dimensionality of the data, the ever-increasing parade of new machine learning algorithms and limited, although human, limitations of the practitioner. Instead, data preparation can be treated as another hyperparameter to tune as part of the modeling pipeline. This raises the question of how to know what data preparation methods to consider in the search, which can feel overwhelming to experts and beginners alike. The solution is to think about the vast field of data preparation in a structured way and systematically evaluate data preparation techniques based on their effect on the raw data.


Using substructures for provably expressive graph neural networks

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The figure below shows an example most of us are familiar with, the molecule of caffeine, whose level in my bloodstream is alarmingly low. TL;DR: In this post, I discuss how to design local and computationally efficient provably powerful graph neural networks that are not based on the Weisfeiler-Lehman tests hierarchy. This is the second in the series of posts on the expressivity of graph neural networks. In Part 3, I will argue why we should abandon the graph isomorphism problem altogether._ Recent groundbreaking papers [1–2] established the connection between graph neural networks and the graph isomorphism tests, observing the analogy between the message passing mechanism and the Weisfeiler-Lehman (WL) test [3].


Building a Deep-Learning-Based Movie Recommender System

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With the continuous development of network technology and the ever-expanding scale of e-commerce, the number and variety of goods grow rapidly and users need to spend a lot of time to find the goods they want to buy. To solve this problem, the recommendation system came into being. The recommendation system is a subset of the Information Filtering System, which can be used in a range of areas such as movies, music, e-commerce, and Feed stream recommendations. The recommendation system discovers the user's personalized needs and interests by analyzing and mining user behaviors and recommends information or products that may be of interest to the user. Unlike search engines, recommendation systems do not require users to accurately describe their needs but model their historical behavior to proactively provide information that meets user interests and needs.


Blockdrop to Accelerate Neural Network training by IBM Research

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IBM Research, with the help of the University of Texas Austin and the University of Maryland, has created a technology, called BlockDrop, that promises to speed convolutional neural network operations without any loss of fidelity. This could further excel the use of neural nets, particularly in places with limited computing capability. Increase in accuracy level have been accompanied by increasingly complex and deep network architectures. This presents a problem for domains where fast inference is essential, particularly in delay-sensitive and realtime scenarios such as autonomous driving, robotic navigation, or user-interactive applications on mobile devices. Further research results show regularization techniques for fully connected layers, is less effective for convolutional layers, as activation units in these layers are spatially correlated and information can still flow through convolutional networks despite dropout.


Adoption Picking Up for AI in Cybersecurity; More Skilled Humans Needed Too

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AI is increasingly being put to use in the technology stacks of cybersecurity companies, but not at the expense of human experts who guide the rollout and work alongside the smart tools. Before 2019, one in five cybersecurity software and service providers were employing AI, according to a study last year by Capgemini Research Institute, in a review of recent research published in DarkReading. Adoption was found to be "poised to skyrocket" by the end of 2020, with 63% of the firms planning to deploy AI in their solutions. Planned use in IT operations and the Internet of Things are predicted to see the most uptick. Increased adoption of AI does not mean that security professionals on IT staffs are ready to hand off their responsibilities.


Natural Language Processing: A Simple Explanation

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Natural language processing, or NLP, is a type of artificial intelligence (AI) that specializes in analyzing human language. Have you ever used Apple's Siri and wondered how it understands (most of) what you're saying? This is an example of NLP in practice. NLP is becoming an essential part of our lives, and together with machine learning and deep learning, produces results that are far superior to what could be achieved just a few years ago. In this article we'll take a closer look at NLP, see how it's applied and learn how it works.


Musk Says Tesla Is 'Very Close' to Developing Fully Autonomous Vehicles

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Tesla Inc.'s Elon Musk said the carmaker is on the verge of developing technology to render its vehicles fully capable of driving themselves, repeating a claim he's made for years but been unable to achieve. The chief executive officer has long offered exuberant takes on the capabilities of Tesla cars, even going so far as to start charging customers thousands of dollars for a "Full Self Driving" feature in 2016. Years later, Tesla still requires users of its Autopilot system to be fully attentive and ready to take over the task of driving at any time. Tesla's mixed messages have drawn controversy and regulatory scrutiny. In 2018, the company blamed a driver who died after crashing a Model X while using Autopilot for not paying attention to the road.


Why Tesla Invented A New Neural Network

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Recently, Tesla filed a patent called'Systems and methods for adapting a neural network on a hardware platform.' In the patent, they described the systems and methods to select a neural network model configuration that satisfies all constraints. According to the patent, the constraints mainly include an embodiment that computes a list of valid configurations and a constraint satisfaction solver to classify valid configurations for the particular platform, where the neural network model will run efficiently. Neural network models are increasingly relied upon for different problems due to the ease at which they can label or classify the input data. Different neural networks are trained with different hyperparameters, and then they are used to analyse the same validation training set.


How to start with Machine Learning ?

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The journey of machine learning started in 1959 when Arthur Samuel introduced the term called Machine Learning. It is defined as a Field of study that gives computers the capability to learn without being explicitly programmed. Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. The main aim is to allow the machine to learn automatically from the examples that have been provided during learning. Now, when the term Machine Learning has become familiar to everyone and has become the most popular career and research choice as it is getting adopted by many industries, it has become important for everyone working in all industries to learn and explore Machine Learning and see what it has to offer. Machine Learning engineer is surveyed as the best job of 2019 and has shown the growth rate above 300%.


Human Resource Management in the Age of AI

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You don't have to be a prophet to foresee that artificial intelligence will also play an essential role in the field of human resource management. It will have a decisive impact on the way we connect people in the future. Using human-machine partnerships to improve the process of connecting people to the right job is relatively new to how most organizations hire. While there are many favorable advancements and novel solutions that promote more inclusive hiring, there are several risks to consider. First and foremost, we must challenge the assumption that hiring managers know what constitutes an ideal employee.