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It is very common to not know the difference between machine learning, deep learning, and…

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It is very common to not know the difference between machine learning, deep learning, and artificial intelligence(AI), almost everyone who is new to technological learning thinks that all three things are the same. Not just that, We often alternatively use the word machine learning in place of deep learning & relate to the word AI without knowing the actual definitions of each of the three. Well, let's clear this confusion now. Let's just consider these two words -- Machine & Learning, now think and form a simple sentence…, Yes! So basically, Machine learning algorithms build a model to make predictions or informed decisions by analyzing sample data a.k.a


7 MLops myths debunked

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We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. With the massive growth of machine learning (ML)-backed services, the term MLops has become a regular part of the conversation -- and with good reason. Short for "machine learning operations," MLops refers to a broad set of tools, work functions and best practices to ensure that machine learning models are deployed and maintained in production reliably and efficiently. Its practice is core to production-grade models -- ensuring quick deployment, facilitating experiments for improved performance and avoiding model bias or loss in prediction quality. Without it, ML becomes impossible at scale.


The Data Science of Hyper-Parameter Tuning

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The inner operations of advanced machine learning models are nebulous to the average business user, regulator, or customer impacted by the outputs of this form of statistical Artificial Intelligence. At best, such laymen are vaguely aware that neural networks, for example, function in a manner that's somewhat similar to how the human brain does. The most sophisticated may have heard something about the notion of parameters; most are blissfully unaware of the presence of hyper-parameters or their import to applications of deep learning. "Basically, in [these] machine learning models, there are two sets of parameters," explained Suman Bera, Senior Software Engineer at Katana Graph. "One set of parameters you are trying to learn through your machine learning algorithm. And, there is another set of parameters which are predefined. You are not trying to learn them. Hyper-parameters are invaluable to devising accurate predictions from advanced machine learning models, which are oftentimes ...


Guide to Panoptic Segmentation - A Semantic + Instance Segmentation Approach

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Panoptic segmentation is an image segmentation method used for Computer Vision tasks. It unifies two distinct concepts used to segment images namely, semantic segmentation and instance segmentation. Panoptic segmentation technique was introduced by Kaiming He, Ross Girshick and Piotr Dollar of Facebook AI Research (FAIR), Carsten Rother of HCI/IWR, Heidelberg University (Germany) as well as Alexander Kirillov, a member of both the above mentioned organizations in April 2019 (version v3). Let us first understand semantic segmentation and instance segmentation approaches in order to have clarity about panoptic segmentation. A Computer Vision project aims at developing a deep learning model which can accurately and precisely detect real-world objects comprising the input data in the form of images or videos.


Attacking machine learning training by re-ordering data

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We have increasingly outsourced our decision-making to machine learning models ("the algorithm"). The whole point of building recommendation, sorting, and "decision support" systems on ML is to undertake assessments at superhuman speed and scale, which means that the idea of a "human in the loop" who validates machine…


Estimating Lake Water Volume With Regression and Machine Learning Methods

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The volume of a lake is a crucial component in understanding environmental and hydrologic processes. The State of Minnesota (USA) has tens of thousands of lakes, but only a small fraction has readily available bathymetric information. In this paper we develop and test methods for predicting water volume in the lake-rich region of Central Minnesota. We used three different published regression models for predicting lake volume using available data. The first model utilized lake surface area as the sole independent variable. The second model utilized lake surface area but also included an additional independent variable, the average change in land surface area in a designated buffer area surrounding a lake. The third model also utilized lake surface area but assumed the land surface to be a self-affine surface, thus allowing the surface area-lake volume relationship to be governed by a scale defined by the Hurst coefficient. These models all utilized bathymetric data available for 816 lakes across the region of study. The models explained over 80% of the variation in lake volumes. The sum difference between the total predicted lake volume and known volumes were <2%. We applied these models to predicting lake volumes using available independent variables for over 40,000 lakes within the study region. The total lake volumes for the methods ranged from 1,180,000- and 1,200,000-hectare meters. We also investigated machine learning models for estimating the individual lake volume...


Top Resources To Learn Feature Engineering

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Data analysing, irrespective of its form, can be extremely chaotic and challenging. This is where feature engineering steps in. A method to ease data analysis, feature engineering simplifies data reading for machine learning models. A feature or variable is nothing but the numerical representation of all kinds of data– structured and unstructured. Feature engineering is a vital part of the process of predictive modelling.


Amazon - MACHINE LEARNING: 5 Books in 1 – The Mathematics of Computer Science and Applied Artificial Intelligence: Callaway, Jason: 9798776163982: Books

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MACHINE LEARNING: 5 Books in 1 – The Mathematics of Computer Science and Applied Artificial Intelligence [Callaway, Jason] on Amazon.com. *FREE* shipping on qualifying offers. MACHINE LEARNING: 5 Books in 1 – The Mathematics of Computer Science and Applied Artificial Intelligence


Monitoring Machine Learning Models in Production

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Daily AI Roundup: Biggest Machine Learning, Robotic Automation Updates

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Peltarion is a Swedish artificial intelligence (AI) software company and developer of a no-code, machine learning operations platform that empowers users to design, train and manage deep learning models in the Cloud at scale and at speed. King's acquisition of Peltarion will accelerate the current use of AI and machine learning technology in King's game platform, a key area of ongoing strategic direction for the company. With this investment, King aims to continue to build top-tier AI and machine learning capabilities and teams – enabling a new generation of innovative game design, development and live operations capabilities and becoming a hub for the world's top talent in game AI. Veritone, Inc., creator of aiWARE, a hyper-expansive enterprise AI platform, announced its sponsorship of the Snowflake Summit 2022, a flagship event running June 13 to 16 in Las Vegas that focuses on the "World of Data Collaboration." Zyxel Networks, a leader in delivering secure, AI- and cloud-powered business and home networking solutions, announced a family of WiFi 6E access points (APs) that enable businesses to enjoy the performance benefits provided through use of the newly-opened 6GHz WiFi spectrum.