Videos have become omnipresent, from streaming our favorite movies and TV shows to participating in video conferences and calls. With the increasing use of smartphones and other capture devices, the quality of videos has risen in importance. However, due to various factors like low light, digital noise, or simply low acquisition quality, the quality of videos captured by these devices is often far from perfect. In these situations, video enhancement techniques come into play, aiming to improve resolution and visual features. Over the years, various video enhancement techniques have been developed until the arrival of complex machine learning algorithms to remove noise and improve image quality.

One of the most common dramas experienced by data scientists is the uncertainty that the available volume of data will suffice for model building. Challenging such insecurity, I've decided to make a Machine Learning algorithm fitted with the lowest possible amount of instances. After many reflections, I've found that working with a binary classification task would be an interesting idea. Since I wished to use the minimum amount of data, the final model needed to be fed with only two instances, each one belonging to a specific category. The data points selection would be performed with K-Means.

Model development and experimentation is part of any machine learning lifecycle. However, without careful planning, keeping track of experiments can become tedious and challenging; especially given the number of configurations we typically deal with. MLflow is a machine learning lifecycle framework that allows ML engineers and teams to keep track of their experiments. In PART 1 of the series, we are going to focus on the first two steps -- tracking experiments and sharing code. PART 2 will be dedicated to model packaging, while PART 3 will show how the concepts outlined in the previous parts can be used in a React web application. For now, let's try to understand what MLflow is, and what it can do for us!

From Deep Blue's victory over chess grandmaster Garry Kasparov to AlphaGo being the first computer program to defeat a Go World Champion, unbeatable superhuman agents have paved a new path for remarkable advancements made in AI. However, the primary question remains whether AI can create agents that can use language to negotiate and collaborate with others to achieve strategic goals in a manner comparable to humans. As it involves players mastering the art of understanding other people's perspectives and devising methods appropriately to persuade them to make agreements and form alliances with others, Diplomacy has long been considered a near-impossible challenge in AI. The complexity of human emotions makes it simple to learn these diplomatic skills. Nevertheless, the question remains: can artificially intelligent machines achieve this level of understanding and persuasion skills?

Welcome to the first advanced and project-based Pandas Data Science Course! No matter if you need excellent Pandas skills for Data Analysis, Machine Learning or Finance purposes, this is the right Course for you to get your skills to Expert Level! Efficiently import and merge Data from many text/CSV files. Clean, handle and flatten nested and stringified Data in DataFrames. Know how to handle and normalize Unicode strings.

Practical explanation and live implementation of all important diagrams used in UML.

In the last century, oil was considered as the'black gold'. But, with the industrial revolution and the emergence of the automotive industry, oil became the main driving source of human civilization. However, with time, its value dwindled due to the gradual exhaustion and resorting to alternative renewable sources of energy. In the 21st century, the new driving force behind industries is Data. As a matter of fact, even automobile industries are using data to impart autonomy and improve the safety of their vehicles.

In 1818, author Mary Shelley told the story of Victor Frankenstein and his "creature" -- a tale that explored humanity's fascination with animating the unliving. This Halloween, an AI-powered séance is doing much of the same: "resurrecting" Shelley, and others, from beyond the grave. It is just one of many ways horror is being explored in different disciplines, putting a new spin on the genre's traditionally grotesque, unnatural and psychological elements. Ahead of Halloween, Grid spoke with three artists -- known for their creepy creations in artificial intelligence, dance and photography -- about how horror is explored in the media they work in. Resurrection -- and the idea of the "undead" -- is a Halloween hallmark.

Ethics and Eric Schmidt are rare bedfellows. The former Google/Alphabet CEO/Chairman exudes a sense of predatory self-interest, always making the point that what he wants aligns with what is supposedly good for the United States. He has splashed money on numerous projects, including such artificial intelligence outfits as Rebellion Defense, all the time maintaining uncomfortably close ties to the government advisory circuit. For years, he has been hectoring the Department of Defense to uncritically embrace AI, in other words, machine-learning technology. "You absolutely suck at machine learning," Schmidt boldly told General Raymond Thomas in July 2016, head of US Special Operations Command.

Python is a dynamic language, so usually, you'll only see errors in your code when you attempt to run it. This library is a static checker, so it finds bugs in your programs without even running them. It will also add type checkers that will help to ensure that you're using variables and functions in your code correctly. It includes lots of features such as type inference, generics, callable types, tuple types, union types, structural subtyping, and more. It has more than 14k stars on GitHub. If you are using Jupyter Notebooks then this is a good resource for you.