(testing signal)

Tag: machinelearning

Without language understanding the relationship with AI will be much worse and less friendly

Today’s virtual assistants and chatbots typically follow simple rules (if this then that) in order to respond to questions. Recent advances in statistical machine learning can add some flexibility by, for example, letting a machine find an answer to a question by searching through large amounts of text. However, both of these approaches can fall victim to the vast complexity and ambiguity of meaning often encoded in language.

Machines watch not what you say, but how you say things. NLP (Natural Language Processing) is not just about words but about context. Google used to match keywords and offer list of links.… Read more...

Best of arXiv.org for AI, Machine Learning, and Deep Learning – September 2021

In this recurring monthly feature, we filter recent research papers appearing on the arXiv.org preprint server for compelling subjects relating to AI, machine learning and deep learning – from disciplines including statistics, mathematics and computer science – and provide you with a useful “best of” list for the past month. Researchers from all over the world contribute to this repository as a prelude to the peer review process for publication in traditional journals. arXiv…… Read more...

Famous Modern Math Problems: The Beal Conjecture

One of the most famous generalizations of Fermat’s Last Theorem.Source: https://www.daviddarling.info/encyclopedia/B/Beals_conjecture.htmlI recently started an AI-focused educational newsletter, that already has over 100,000 subscribers. TheSequence is a no-BS (meaning no hype, no news etc) ML-oriented newsletter that takes 5 minutes to read. The goal is to keep you up to date with machine learning projects, research papers and concepts. Please give it a try by subscribing below:Continuing…… Read more...

Transparent Machine Learning: Key Steps and Tips for Getting Started

Click to learn more about author Deepak Dube.

While machine learning is an involved science with complex models, what distinguishes transparent machine learning is that it explains itself – how it works, its predictions, its insights – so that the user understands and trusts the outcome. In this article, I explain what transparent machine learning is and the considerations for implementing it.

What Is Transparent Machine Learning?

Enterprises have reams of data, which…… Read more...

What is a Model in Machine Learning

Image by Ali Shah Lakhani — UnsplashMachine Learning Models play a vital part in Artificial Intelligence. In simple words, they are mathematical representations. In other words, they are the output we receive after training a process.What a machine learning model does is discovers the patterns in a training dataset. In other words, machine learning models map inputs to the outputs of the given dataset.These classification models can be classified in different ways called Principal…… Read more...

🎙 Paroma Varma/Snorkel on programmatic approaches to data labeling

Getting to know the experience gained by researchers, engineers, and entrepreneurs doing real ML work is an excellent source of insight and inspiration. Share this interview if you find it enriching. No subscription is needed.Share👤 Quick bio / Paroma VarmaTell us a bit about yourself. Your background, current role and how did you get started in machine learning? Paroma Varma (PV): I am a co-founder of Snorkel AI, which started as a research project in the Stanford AI Lab in 2015,…… Read more...

Not So Common Machine Learning Examples That Challenge Your Knowledge

Image by: Nature.comMachine Learning refers to the process through which a computer learns and changes its operations based on patterns identified in vast quantities of data. When we think about machine learning, we think of a few well-known instances. For example, the way Amazon recommends products is remarkably similar to Google searches you’ve done. Machine learning’s reach is far broader than what we are familiar with and observes in our daily lives.Because machine learning is such a…… Read more...

The insideBIGDATA IMPACT 50 List for Q4 2021

The team here at insideBIGDATA is deeply entrenched in following the big data ecosystem of companies from around the globe. We’re in close contact with most of the firms making waves in the technology areas of big data, data science, machine learning, AI and deep learning. Our in-box is filled each day with new announcements, commentaries, and insights about what’s driving the success of our industry so we’re in a unique position to publish our quarterly IMPACT 50 List of the most…… Read more...

A Basic Overview of the Reinforcement Learning Techniques Behind DeepMind’s AlphaStar

AlphaStar has evolved in two versions achieving superhuman performance in StarCraft IISource: https://venturebeat.com/2019/10/30/deepminds-alphastar-final-beats-99-8-of-human-starcraft-2-players/I recently started an AI-focused educational newsletter, that already has over 100,000 subscribers. TheSequence is a no-BS (meaning no hype, no news etc) ML-oriented newsletter that takes 5 minutes to read. The goal is to keep you up to date with machine learning projects, research papers and…… Read more...

Future of Data Science: Machine Learning or Artificial intelligence

Image by: javapointWhen we think about the future of AI, we could picture highly sophisticated robots that can imitate humans so effectively that they are indistinguishable from people. True, artificial intelligence’s capacity to swiftly learn, process, and evaluate data in order to make choices is a significant attribute.However, what most of us think of as AI is actually a subdiscipline known as machine learning. Artificial intelligence has become a blanket phrase encompassing a variety…… Read more...

“Above the Trend Line” – Your Industry Rumor Central for 10/8/2021

Above the Trend Line: your industry rumor central is a recurring feature of insideBIGDATA. In this column, we present a variety of short time-critical news items grouped by category such as M&A activity, people movements, funding news, industry partnerships, customer wins, rumors and general scuttlebutt floating around the big data, data science and machine learning industries including behind-the-scenes anecdotes and curious buzz. Our intent is to provide you a one-stop source of…… Read more...

📝 Guest post: Data Aggregation is Unavoidable! (And Other Big Data Lies)

In TheSequence, we like to experiment with different formats, and today we introduce TheSequence Guest Post. Here we give space to our partners to explain in detail what machine learning (ML) challenges they help deal with. In this post, Molecula’s team talks about:Pre-aggregation strategies are workarounds to deal with data infrastructure limitations.Pre-aggregating data often creates performance problems and may mask insights that could be found with more granular data.Eliminating the…… Read more...

Low Code Data Science is Not the Same as Automated Machine Learning

Sponsored Post

In this special guest feature, Rosaria Silipo, Ph.D., Principal Data Scientist at KNIME, discusses the difference between automated Machine Learning and low code tools for data science. Rosaria is the author of 50+ technical publications, including her most recent book “Practicing Data Science: A Collection of Case Studies.” She holds a doctorate degree in bioengineering and has spent more than 25 years working on data science projects for companies in a broad…

https://insidebigdata.com/2021/10/07/low-code-data-science-is-not-the-same-as-automated-machine-learning/… Read more...

How Much Training Data Do You Require For Machine Learning?

It is a crucial component of machine learning (ML), and having the proper quality and amount of data sets is critical for accurate outcomes. The more training data available for the machine learning algorithm, the better the model will be able to identify different sorts of objects, making it simpler to distinguish them in real-life predictions.However, how will you determine how much training is sufficient for your machine learning? As insufficient data will affect…… Read more...

Download book for data science beginners – Learn Data Science with R

The book Learn Data Science with R covers minimal theory, practical examples, and projects. It starts with an explanation of the underlying concepts of data science, followed by implementing them in R language. Learn linear regression, logistic regression, random forests, and other machine learning algorithms. The hands-on projects provide a detailed step-by-step guide for analyzing and predicting data.
The book covers the following topics –
R Language
Statistics and Mathematics
Data…… Read more...

Machine Learning

Image by: AuthorIn computer vision, semantic segmentation is one of the most important components for fine-grained inference (CV). To achieve the appropriate precision levels, models must grasp the context of the environment in which they operate. As a result, through pixel accuracy, semantic segmentation supplies them with that insight.Before we dig deep into the topic, let us understand what is semantic segmentation.The goal of semantic segmentation is to group pixels in a meaningful way….

https://pub.towardsai.net/machine-learning-23997460cbc4?source=rss—-98111c9905da—4… Read more...

Here’s Why You Need Python Skills as a Machine Learning Engineer

If you want to learn how to apply Python programming skills in the context of AI applications, the UC San Diego Extension Machine Learning Engineering Bootcamp can help. Read on to find out more about how machine learning engineers use Python, and why the language dominates today’s machine learning landscape.

Sponsored Post.
Python is one of the most popular programming languages used in the field of machine learning. According to Kaggle’s annual survey of machine learning…

https://www.kdnuggets.com/2021/10/bootcamp-python-skills-machine-learning-engineer.html… Read more...