Author Archives: 365Data Science

Observation bias and sub-group differences generate statistical paradoxes. Originally from KDnuggets https://ift.tt/2Q5qSCS source https://365datascience.weebly.com/the-best-data-science-blog-2020/top-3-statistical-paradoxes-in-data-science

A simple explanation of Machine Learning and Neural Networks and A New Perspective for ML Experts This is an introduction to machine learning an neural networks in a simple and intuitive way. The concepts are explained in a way different from the traditional explanations for neural networks, with a new perspective. Image by author (Moon aboveContinue reading “A simple explanation of Machine Learning and Neural Networks”

Artificial Intelligence in Radiology — Advantages, Use Cases & Trends The radiology department of an average healthcare facility is likely searching for improvements. Even before COVID-19, 45% of radiologists experienced burnout at one point in their career. They felt overwhelmed with the administrative burden and the large number of images they had to check manually, which could reachContinue reading “Artificial Intelligence in RadiologyAdvantages Use Cases & Trends”

6 reasons why learning using kaggle competitions does not train you for the real world data science problems. Continue reading on Becoming Human: Artificial Intelligence Magazine » Via https://becominghuman.ai/why-kaggle-competitions-move-you-away-from-real-world-data-science-problems-b6a2c46d0e7b?source=rss—-5e5bef33608a—4 source https://365datascience.weebly.com/the-best-data-science-blog-2020/why-kaggle-competitions-move-you-away-from-real-world-data-science-problems

If you are preparing to make a career as a Data Scientist or are looking for opportunities to skill-up in your current role, this analysis of in-demand skills for 2021, based on over 15,000 Data Scientist job postings, should offer you a good idea as to which programming languages and software tools are increasing andContinue reading “The Most In-Demand Skills for Data Scientists in 2021”

Today, organizations are increasingly implementing cloud ETL tools to handle large data sets. With data sets becoming larger by the day, unified ETL tools have become crucial for data integration needs of enterprises. Originally from KDnuggets https://ift.tt/2RxcovL source https://365datascience.weebly.com/the-best-data-science-blog-2020/etl-in-the-cloud-transforming-big-data-analytics-with-data-warehouse-automation

WeightWatcher is based on theoretical research (done injoint with UC Berkeley) into Why Deep Learning Works, based on our Theory of Heavy Tailed Self-Regularization (HT-SR). It uses ideas from Random Matrix Theory (RMT), Statistical Mechanics, and Strongly Correlated Systems. Originally from KDnuggets https://ift.tt/3x5yiXw source https://365datascience.weebly.com/the-best-data-science-blog-2020/is-your-model-overtained

Data Labeling Service — How to Get Good Training Data for ML Project? Four Customer Pain Points in Getting No Bias Training Data With the commercialization of AI products, auto-driving, face recognition, security, and other fields have become popular scenarios, and AI companies begin to focus on scenario-based landing capability. As the basis of the AI industry, high-quality trainingContinue reading “How to Get Good Training Data for ML Project?”

The impact of conversational AI is becoming more noticeable, and we can see it from the growth of the conversational AI market, which will reach AUD 22.6 billion or USD 17 billion by 2024: Source: Deloitte We already know how significant the role of conversational AI is in healthcare, media, and financial services. The business worldContinue reading “5 Ways Conversational AI is Shaping the Future of Learning”

Tldr: Poor processes and culture can derail the success of many an exceptional AI team. In part 1, I introduced a four-pronged framework for analysing the principal factors underlying the failure of corporate AI projects: Intuition (Why) Process/Culture (How) People (Who) Systems (What) and addressed in detail how a lack of organizational intuition for AI canContinue reading “Why Corporate AI Projects Fail? Part 2/4”