Probability is one of the most common terminologies, not only in mathematics but also in the real world. We use the word probability frequently. About seven years ago, I was in my secondary level of education and got introduced to the term probability as a topic of mathematics. At that time, I had solved so many mathematical problems regarding probability. Unfortunately, it did not seem interesting to me.

It has amplified outrage on social media and struggled to flag hate speech. It has designated engineers as male and nurses as female when translating language. It has failed to recognize people with darker skin tones when matching faces. Systems powered by machine learning are amassing greater influence on human life, and while they work well most of the time, developers are constantly fixing mistakes like a game of whack-a-mole. That mean's AI's future impact is unpredictable. At best, it will likely continue to harm at least some people because it is often not trained properly; at worst, it will cause harm on a societal scale because its intended use isn't vetted -- think surveillance systems that use facial recognition and pattern matching.

With its help, you can conveniently access a variety of datasets from many categories. However, ImageNet is an exception; it requires a manual setup. While there seem to be some instructions on achieving that, they are somewhat vague. As a result, it took me some time to prepare the dataset, but I ended up with a concise script. Before we do any preparation, we need to obtain the dataset.

Customer satisfaction is one of the methods that businesses can use to effectively manage and monitor their operations. Every product can survive in the marketplace for a long time as long as the customer satisfaction level remains high. Good quality of the product is not the only factor that can make a product can have a high level of customer satisfaction. Many factors, such as delivery times, additional payment, product price, and etc. can have a significant impact on customer satisfaction. Customer satisfaction is critical for customer retention.

In recent years, computational tools based on reinforcement learning have achieved remarkable results in numerous tasks, including image classification and robotic object manipulation. Meanwhile, computer scientists have also been training reinforcement learning models to play specific human games and videogames. To challenge research teams working on reinforcement learning techniques, the Neural Information Processing Systems (NeurIPS) annual conference introduced the MineRL competition, a contest in which different algorithms are tested on the same task in Minecraft, the renowned computer game developed by Mojang Studios. More specifically, contestants are asked to create algorithms that will need to obtain a diamond from raw pixels in the Minecraft game. The algorithms can only be trained for four days and on 8,000,000 samples created by the MineRL simulator, using a single GPU machine.

Freshness provides one of the essential characteristics for consumers. Consumers prefer fresh fruits rather than rotten ones when it comes to hygiene. An efficient fruit detection system is required to facilitate humans. So, for the easiness of people, this desktop application is proposed, named "Detection of Rotten Fruits (DRF)" by using Artificial Intelligence and Computer Vision. DRF is a desktop application for detecting rottenness in fruits that can be used to indicate the fruits according to their rottenness.

While it rarely happens for me, I occasionally observe something that is an actual watershed moment for technological advancement and can acknowledge that the possibility for good using said technologies might outweigh the potential for evil. Streaming your favorite shows has never been easier. For me, such a moment was watching The Beatles: Get Back, which is now airing on Disney . I had heard about it and seen some of its footage slowly being released for about a year, but I had no idea just how much technology had been applied to its production. I've seen many films made from historical footage from the 1960s and even the 1970 and 1980s, and so much of the source material is in poor condition.

Artificial Intelligence used to be a fancy word in the past, a system that was going to change the world overnight. Well, it is everywhere these days but the pace for that proposed change is rather evolutionary, slowly taking over manual operations and processes. The biggest playground of AI is in Data Science, including data extraction and processing. But, it is less about diddling fancy terms and more about the result; the urge to reach meaningful conclusions in the least possible time. The advent and maturation of digital services, such as banking, shopping, etc, called for better processes to ensure the identity of users both during onboarding and during the completion of a transaction.

He is working on improving the performance and safety of our machine learning models. Originally from Zagreb, Croatia, Vilim studied electrical engineering and computer science for his bachelor's degree. He increasingly became intrigued with neuroscience, which became the focus of his Master's degree at LMU in Munich. Vilim stayed on in Munich to obtain his PhD at the Max Planck Institute of Neurobiology, where he investigated neural processing for motor control with whole-brain imaging. Machine learning has always been a part of Vilim's work and studies, and he is excited to completely focus on it for his new role.

If you want to skip ahead and just see how to fine-tune your own dataset you can skip the rest of the article and just check out the colab notebook! Presenting guest lectures is one of my favorite things; all the joy of teaching without any of the responsibility. My goal in a lecture is to get the students excited about machine learning, get them playing around with practical examples, and then get the heck out of the way. This week I had the opportunity to talk with some students from the University of Arizona's Cyber program about machine learning. In a pre-lecture survey on questions one of the major concerns the students in the class identified was the developing threat of high-fidelity bots capable of influencing conversations on social media.