Data Science Central
Machine Learning with Applications in One Picture
· Interesting picture summarizing several types of techniques used in machine learning, contrasting unsupervised learning with unsupervised learning and reinforc… · Shared by 146, including Yves Mulkers, James Gingerich #B2B #Technology #Influencer, Howard Getson, Dr. Craig Brown, Ronald van Loon, Matthias Lampe, Jennifer Stirrup #MBA Topics: #AI #Data #Strategy, Kirk Borne, 🇺🇦Evan Kirstel #B2B #TechFluencer, Dr. Ganapathi Pulipaka 🇺🇸
Analytics Insight
Top 10 Python Libraries that Every Data Scientist Must Know
3 min · · Python libraries are the basics that every data science professional must know to build accurate algorithms and code as per the project requirements. Read the top 10 Python Libraries for Data… · Shared by 76, including Daniel Lawniczak, Carla Gentry 🎶, Evan (he/him), Yves Mulkers
Singularity Hub
This AI Could Bring Us Computers That Can Write Their Own Software
4 min · · A new machine learning program called MISIM can figure out what a snippet of code is meant to do and offer up new code to make it faster or more efficient. · Shared by 143, including Nicolas Babin, l. ones, Helen Yu, Vinod Sharma, John Hagel, Ronald van Loon, Katja Evertz, Theodora (Theo) Lau - 劉䂀曼 🌻, Marktechpost AI Research News ⚡, Andreas Staub, Joanna (J.F.) Penn, Stephan Woodtli, Harold Sinnott
towardsai.net
Main Types of Neural Networks and its Applications — Tutorial
20+ min · · A tutorial on the main types of neural networks and its applications to real-world challenges. · Shared by 87, including Carla Gentry 🎶, abhishek yadav, Patrick Scheuerer, ΛLΞX TRΞJʘ, Dr. Ganapathi Pulipaka 🇺🇸
MIT Technology Review
A radical new neural network design could overcome big challenges in AI
10 min · · Researchers borrowed equations from calculus to redesign the core machinery of deep learning so it can model continuous processes like changes in health. · Shared by 172, including Philipp Laurim, Daniel Witte, Jonathan Frye, Oleg Baskov, Dominik Grolimund, Javi Cantón @javicanton@mas.to, Donneker, Matthew Turland, Massimiliano Aroffo, @AlgoCompSynth@ravenation.club by znmeb, Vikram Dutt, Thomas Power, 📁𝐦𝐫𝐯𝐧, Clemens, Nico Müller 🇺🇦, Marco Unternaehrer, Philippe Surber, Thomas Spreng, Niklaus Gerber
TensorFlow
Introducing TensorFlow Videos for a Global Audience: Spanish — The TensorFlow Blog
2 min · · https://blog.tensorflow.org/2020/04/introducing-tensorflow-videos-for-global-audience-spanish.html https://i.ytimg.com/vi/LcXOMKE7d7A/hqdefault.jpg April 13, 2020 — Posted by the TensorFlow Team When… · Shared by 17, including Yves Mulkers
VentureBeat
Kubernetes: The key ingredient IT needs to accelerate today’s data science
3 min · · Kubernetes has increasingly becomel part of the fabric that data scientists need to run optimal applications in the most powerful ways. · Shared by 19, including Christopher S. Penn, 🇺🇦Evan Kirstel #B2B #TechFluencer
Machine Learning Mastery
14 Different Types of Learning in Machine Learning
20+ min · · Machine learning is a large field of study that overlaps with and inherits ideas from many related fields such as artificial intelligence. The focus of the field is learning, that is, acquiring skills… · Shared by 235, including Matthias Lampe, Birgit Schefer, Kirk Borne, Nige Roberts-Willson, Francesco Corea, Carla Gentry 🎶, Antonio Vieira Santos, Bob E. Hayes, Thomas Billeter, Ines Bieler
Forbes
AI Bias Adds Complexity To AI Systems
7 min · · When AI systems start to make judgments about a person’s quality of life independently without checks and balances such as in the case of AI systems that monitor students' emotions in classrooms to… · Shared by 41, including Yves Mulkers, Marc R Gagné MAPP, Ronald van Loon, Gil Press, Andreas Staub, Theodora (Theo) Lau - 劉䂀曼 🌻, Tamara McCleary
KDnuggets
Data Preparation for Machine learning 101: Why it’s important and how to do it
6 min · · As data scientists who are the brains behind the AI-based innovations, you need to understand the significance of data preparation to achieve the desired level of cognitive capability for your models.… · Shared by 36, including Prof. Arun Kumar वाघचौरे, Bob E. Hayes, ipfconline in vacation mode :) will be back Sept.4, Evan Sinar, PhD, Kirk Borne, Terence Mills, Craig Brown, PhD
medium.com
AI in 2019: A Year in Review
18+ min · · The Growing Pushback Against Harmful AI · Shared by 105, including Merkstatt@troet.cafe 📯, Kate Crawford, Thomas Power, Andra Keay 🐳 @robotlaunch@mstdn.social, 🇺🇦Evan Kirstel #B2B #TechFluencer, Yann “不停” Heurtaux ⏚ @shalf@mastodon.social, Gerd Leonhard, Stephan Woodtli
Machine Learning Mastery
Probability for Machine Learning
20+ min · · Probability for Machine Learning Discover How To Harness Uncertainty With Python Machine Learning DOES NOT MAKE SENSE Without Probability What is Probability?…it’s about handling uncertainty… · Shared by 23, including Kirk Borne
The New Yorker
Sunday Reading: The Rise of Artificial Intelligence
2 min · · From The New Yorker’s archive: views of an expanding and ever-changing technological frontier. · Shared by 122, including Martin Ford, Elinor Stutz, Iain Brown, PhD, Thomas Billeter, Bob E. Hayes, Josep M. Ganyet, Ronald van Loon, Nige Roberts-Willson, Tamara McCleary, 🇺🇦Evan Kirstel #B2B #TechFluencer, Oscar MacDonald
The Gradient
The State of Machine Learning Frameworks in 2019
11+ min · · Since deep learning regained prominence in 2012, many machine learning frameworks have clamored to become the new favorite among researchers and industry practitioners. From the early academic outputs… · Shared by 275, including Massimiliano Aroffo, Dr. Ganapathi Pulipaka 🇺🇸, Thomas Billeter, AGI.Eth | MTL.Eth, Marc Gasser, hardmaru, Francesco Corea, Ferit (at 🏠) 🌙, Bob E. Hayes, Jamie Burke ⛺️, Mike Tamir, PhD, Sebastian Raschka, Balaji, Lluís Codina, Oleg Baskov, Kirk Borne, Pete Skomoroch, Nando de Freitas 🏳️🌈, Oliver Ewinger, 🇺🇦Evan Kirstel #B2B #TechFluencer
The StartUp
AI for Beginners
12+ min · · The basics of how AI works, and how it can be used · Shared by 127, including Florian Graillot, Damir Kusar, Thomas Power, 🇺🇦Evan Kirstel #B2B #TechFluencer, R.NFT R “Ray” Wang 王瑞光 #1A #AI, Women Who Code, Merkstatt@troet.cafe 📯
Machine Learning Mastery
A Gentle Introduction to Bayes Theorem for Machine Learning
9 min · · Bayes Theorem provides a principled way for calculating a conditional probability. It is a deceptively simple calculation, although it can be used to easily calculate the conditional probability of… · Shared by 52, including Tom Connor