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Gradient Descent, clearly explained in Python, Part 1: The troubling theory.

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If you have ever done a Kaggle competition, these would be commonly referred to as evaluation metrics. Typically, the lower the loss, the better the performance of your model. So if,for example, you were predicting house prices and using Mean Squared Error, and your cost was $25000, that means that your model is performing poorly as it is making a prediction error of $25000. Going back to our analogy, if you imagine that instead of a mountain there is a U-shaped curve, and instead of a person there is the cost function with maybe an initial cost value of 25,500. The aim of Gradient Descent would be to minimise this cost to either 0(global minimum), or something much smaller(local minimum).


University of Helsinki launches Ethics of AI online course

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Finland has released a free online artificial intelligence (AI) course which aims to educate public administration, businesses and the general public about the technology and to consider what it should be used for. The Ethics of AI course offered by the University of Helsinki has been designed in a partnership with the cities of Helsinki, Amsterdam and London as well as Finland's Ministry of Finance. Questions pertaining to the ethics of AI are topical, as many people are already making ethical choices in their work, for example, on data use. The course sets out to help them understand what the ethical use of artificial intelligence means and what it requires from both society and individuals. "These questions include how our data is used, who is responsible for decisions made by computers and whether, say, facial recognition systems are used in a way that acknowledges human rights. In a broader sense, it's also about how we wish to utilise advancing technical solutions," said Anna-Mari Rusanen, course coordinator for Ethics of AI.


This AI model will help you summarise a research paper in seconds

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Researchers at the Allen Institute for Artificial Intelligence have developed an AI-powered model that summarises scientific papers into a few sentences. In other words, it condenses a research paper into TLDR (Too Long; Didn't Read) format so you can decide which papers are worth reading. It does this by extracting the most important parts from the abstract, introduction, and conclusion sections, creating a snippet to describe the paper.


Is Your Machine Learning Model Likely to Fail?

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Add in a dash of Data Science tinkering ("I think I ran my Jupyter Notebook cells out of order") -- and it's no surprise 9 out of 10 Data Science projects never see the light of day. Given that the only data product I've deployed thus far is this clustering-based Neighborhood Explorer dashboard, I'll leave it to the more experienced to walk you through the deployment process. Data Scientists should understand how to deploy and scale their own models…Overspecialization is generally a mistake. She recommends courses and reading material on Kubernetes for Data Science. This container management tool represents the dominant force in cloud deployment.


All-in-One:Machine Learning,DL,NLP,AWS Deply [Hindi][Python]

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Online Courses Udemy - All-in-One:Machine Learning,DL,NLP,AWS Deply [Hindi][Python], Complete hands-on Machine Learning Course with Data Science, NLP, Deep Learning and Artificial Intelligence Created by Rishi Bansal English Students also bought Java from Zero to First Job: Part 1 - Java Basics and OOP C Programming for Beginners - Master the C Fundamentals Full-Stack Web Development For Beginners The Complete Java Programmer: From Scratch to Advanced Python and Django Full-Stack Web Development for beginners Learn To Create AI Assistant (JARVIS) With Python Preview this course GET COUPON CODE Description This course is designed to cover maximum Concept of Machine Learning. Anyone can opt for this course. No prior understanding of Machine Learning is required. As a Bonus Introduction Natural Language Processing and Deep Learning is included. Below Topics are covered Chapter - Introduction to Machine Learning - Machine Learning?


Goodhart's law, diversity and a series of seemingly unrelated toy problems

AIHub

Goodhart's Law is an adage which states the following: "When a measure becomes a target, it ceases to be a good measure." This is particularly pertinent in machine learning, where the source of many of our greatest achievements comes from optimizing a target in the form of a loss function. Updates of this form have led to a series of breakthroughs from computer vision to reinforcement learning, and it is easy to see why it is so popular: 1) it is relatively cheap to compute using backprop 2) it is guaranteed to locally reduce the loss at every step and finally 3) it has an amazing track record empirically. However, we wouldn't be writing this if SGD was perfect! In fact there are some negatives.


Top 12 Javascript Libraries for Machine Learning

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Rapidly evolving technologies like Machine Learning, Artificial Intelligence, and Data Science were undoubtedly among the most booming technologies of this decade. The s specifically focusses on Machine Learning which, in general, helped improve productivity across several sectors of the industry by more than 40%. It is a no-brainer that Machine Learning jobs are among the most sought-after jobs in the industry. There are various programming languages, such as JavaScript, Python, and many others, that act as a reputable entry point into the world of Machine Learning, and that brings us to the goal behind this write-up. Through this article, we will try to shed some light on more than 10 of the most popular JavaScript libraries to help you learn Machine Learning.


IT Pro Panel: The practical CIO's guide to AI

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Artificial intelligence (AI) has been a hot-button topic in business IT for the last several years.


14 Open Datasets for Text Classification in Machine Learning

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Text classification datasets are used to categorize natural language texts according to content. For example, think classifying news articles by topic, or classifying book reviews based on a positive or negative response. Text classification is also helpful for language detection, organizing customer feedback, and fraud detection. Though time consuming when done manually, this process can be automated with machine learning models. The result saves companies time while also providing valuable data insights.


Understanding Naïve Bayes and Support Vector Machine and their implementation in Python

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This article was published as a part of the Data Science Blogathon. In this digital world, spam is the most troublesome challenge that everyone is facing. Sending spam messages to people causes various problems that may, in turn, cause economic losses. By spamming messages, we lose memory space, computing power, and speed. To remove these spam messages, we need to spend our time.