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The Best YouTube Channels for Learning Data Science for Free in 2023


Inrecent years, data science has become an increasingly popular field due to the explosion of data and the need to extract valuable insights from it. While traditional education can be expensive and time-consuming, many aspiring data scientists turn to YouTube to learn the necessary skills. In this article, we've compiled a list of the best YouTube channels for learning data science for free in 2023. We cover a range of topics, including mathematics, programming, data analysis, machine learning and deep learning, career tips and guidance, interview preparation, and staying updated with the latest trends in the field. Whether you're a beginner or an experienced data scientist, these channels can help you improve your skills and knowledge in data science without breaking the bank.

Havana Syndrome 'Patient Zero' rejects intelligence community findings that foreign adversary 'very unlikely'

FOX News

Fox News chief national security correspondent Jennifer Griffin speaks with'Adam,' a former government worker who is reportedly Havana syndrome's'patient zero' on'Special Report.' The Victims of Havana Syndrome say they reject the findings of the US Intelligence Community, whose new assessment after 2 years of intensive research concluded that a foreign adversary is "very unlikely" to be behind the Anomalous Health Incidents, which have left dozens if not hundreds of US government employees debilitated with in some cases brain injuries and vertigo since first being reported by those serving at the US Embassy in Cuba in 2016. "Frankly, I find the report embarrassing and laughable," said Adam, a former US government employee who is known by many as "Patient Zero." "In reading the report, there's a myriad of errors, mistruths, twisting of the truth and flat out, as far as I'm concerned, lies in there." Adam, whose name we have agreed to protect given his past work, is a highly trained former US government employee with years of experience overseas. "To say that a foreign adversary doesn't have the same sort of technology or equipment, frankly, is laughable. I mean, it was just two years ago that China was bragging that they were using microwave weapons on the Indian border against India," Adam told Fox News.

State Space Search Optimization Using Local Search Algorithms


This article was published as a part of the Data Science Blogathon. Until now, we have seen two different approaches to state space search. These search strategies compute the path to the goal state from the initial state. A* Search Strategy is one of the best strategies which provides near-optimum solutions. It uses a heuristic and actual cost function to reach the goal state with minimum cost.

Using sequence action set to mine long sequences


Sequences are an important type of data that often occurs in fields such as medicine, business, finance, and education. The goal of sequential pattern mining is to discover frequently occurring sequences to extract useful knowledge from data. With the increase in the size of databases, mining long sequences is quite a challenging task. The sequence action set provides actions that are effective in sequence mining tasks for various data sets. In this post, we will show how the seqmc action is able to mine long sequences efficiently from a large database.

How Black Box Optimization works part2 (Machine Learning)


Abstract: Universal methods for optimization are designed to achieve theoretically optimal convergence rates without any prior knowledge of the problem's regularity parameters or the accurarcy of the gradient oracle employed by the optimizer. In this regard, existing state-of-the-art algorithms achieve an O(1/T2) value convergence rate in Lipschitz smooth problems with a perfect gradient oracle, and an O(1/T) convergence rate when the underlying problem is non-smooth and/or the gradient oracle is stochastic. On the downside, these methods do not take into account the problem's dimensionality, and this can have a catastrophic impact on the achieved convergence rate, in both theory and practice. Our paper aims to bridge this gap by providing a scalable universal gradient method -- dubbed UnderGrad -- whose oracle complexity is almost dimension-free in problems with a favorable geometry (like the simplex, linearly constrained semidefinite programs and combinatorial bandits), while retaining the order-optimal dependence on T described above. These "best-of-both-worlds" results are achieved via a primal-dual update scheme inspired by the dual exploration method for variational inequalities Abstract: Most successful stochastic black-box optimizers, such as CMA-ES, use rankings of the individual samples to obtain a new search distribution.

How far have we come with Adversarial Learning part1(Machine Learning)


Abstract: Unsupervised image translation using adversarial learning has been attracting attention to improve the image quality of medical images. However, adversarial training based on the global evaluation values of discriminators does not provide sufficient translation performance for locally different image features. We propose adversarial learning with a feedback mechanism from a discriminator to improve the quality of CBCT images. This framework employs U-net as the discriminator and outputs a probability map representing the local discrimination results. The probability map is fed back to the generator and used for training to improve the image translation. Our experiments using 76 corresponding CT-CBCT images confirmed that the proposed framework could capture more diverse image features than conventional adversarial learning frameworks and produced synthetic images with pixel values close to the reference image and a correlation coefficient of 0.93.

New Go-playing trick defeats world-class Go AI--but loses to human amateurs


In the world of deep-learning AI, the ancient board game Go looms large. Until 2016, the best human Go player could still defeat the strongest Go-playing AI. That changed with DeepMind's AlphaGo, which used deep-learning neural networks to teach itself the game at a level humans cannot match. More recently, KataGo has become popular as an open source Go-playing AI that can beat top-ranking human Go players. Last week, a group of AI researchers published a paper outlining a method to defeat KataGo by using adversarial techniques that take advantage of KataGo's blind spots.

Hyperparameter Tuning Using Randomized Search


This article was published as a part of the Data Science Blogathon. Hyperparameter tuning or optimization is important in any machine learning model training activity. The hyperparameters of a model cannot be determined from the given datasets through the learning process. However, they are very crucial to control the learning process itself. These hyperparameters originate from the mathematical formulation of machine learning models. For example, the weights learned while training a linear regression model are parameters, but the learning rate in gradient descent is a hyperparameter.

ripgrep is faster than {grep, ag, git grep, ucg, pt, sift} - Andrew Gallant's Blog


In this article I will introduce a new command line search tool, ripgrep, that combines the usability of The Silver Searcher (an ack clone) with the raw performance of GNU grep. We will attempt to do the impossible: a fair benchmark comparison between several popular code search tools. As someone who has worked on text search in Rust in their free time for the last 2.5 years, and as the author of both ripgrep and the underlying regular expression engine, I will use this opportunity to provide detailed insights into the performance of each code search tool. No benchmark will go unscrutinized! NOTE: I'm hearing reports from some people that rg isn't as fast as I've claimed on their data. I'd love to help explain what's going on, but to do that, I'll need to be able to reproduce your results. If you file an issue with something I can reproduce, I'd be happy to try and explain it. Why should you use ripgrep over any other search tool? In other words, use ripgrep if you like speed, filtering by default, fewer bugs and Unicode support. I'd like to try to convince you why you shouldn't use ripgrep. Often, this is far more revealing than reasons why I think you should use ripgrep. Despite initially not wanting to add every feature under the sun to ripgrep, over time, ripgrep has grown support for most features found in other file searching tools. This includes searching for results spanning across multiple lines, and opt-in support for PCRE2, which provides look-around and backreference support. The binary name for ripgrep is rg. Binaries for ripgrep are available for Windows, Mac and Linux. Linux binaries are static executables. Windows binaries are available either as built with MinGW (GNU) or with Microsoft Visual C (MSVC). When possible, prefer MSVC over GNU, but you'll need to have the Microsoft VC 2015 redistributable installed. If you're a Homebrew user, then you can install it like so: If you're an Archlinux user, then you can install ripgrep from the official repos: If you're a Rust programmer, ripgrep can be installed with cargo: If you'd like to build ripgrep from source, that is also easy to do. If you have a Rust nightly compiler, then you can enable optional SIMD acceleration like so, which is used in all benchmarks reported in this article. The command line usage of ripgrep doesn't differ much from other tools that perform a similar function, so you probably already know how to use ripgrep. The full details can be found in rg --help, but let's go on a whirlwind tour. Coloring works on Windows too! Colors can be controlled more granularly with the --color flag. One last thing before we get started: generally speaking, ripgrep assumes the input is reading is UTF-8. However, if ripgrep notices a file is encoded as UTF-16, then it will know how to search it. For other encodings, you'll need to explicitly specify them with the -E/--encoding flag. To recursively search the current directory, while respecting all .gitignore

Computer Vision - Richard Szeliski


As humans, we perceive the three-dimensional structure of the world around us with apparent ease. Think of how vivid the three-dimensional percept is when you look at a vase of flowers sitting on the table next to you. You can tell the shape and translucency of each petal through the subtle patterns of light and shading that play across its surface and effortlessly segment each flower from the background of the scene (Figure 1.1). Looking at a framed group por- trait, you can easily count (and name) all of the people in the picture and even guess at their emotions from their facial appearance. Perceptual psychologists have spent decades trying to understand how the visual system works and, even though they can devise optical illusions1 to tease apart some of its principles (Figure 1.3), a complete solution to this puzzle remains elusive (Marr 1982; Palmer 1999; Livingstone 2008).