Category Archives: Data Science News

comments Source Geographic Information Systems are seeing a new popularity in recent times partly thanks to the COVID-19 Outbreak that has prompted may people to Read more » Originally from KDnuggets https://ift.tt/2SlQrgS source https://365datascience.weebly.com/the-best-data-science-blog-2020/exploring-the-impact-of-geographic-information-systems

Check out these 5 cool Python libraries that the author has come across during an NLP project, and which have made their life easier. Originally from KDnuggets https://ift.tt/2VN5KkS source https://365datascience.weebly.com/the-best-data-science-blog-2020/five-cool-python-libraries-for-data-science

Also: A Concise Course in Statistical Inference: The Free eBook; ML Ops: Machine Learning as an Engineering Discipline; Learning during a crisis (#DataScience 90-day learning challenge) ; Free High-Quality Machine Learning & Data Science Books & Courses: Quarantine Edition Originally from KDnuggets https://ift.tt/2Yj6mk0 source https://365datascience.weebly.com/the-best-data-science-blog-2020/top-kdnuggets-tweets-apr-22-28-24-best-and-free-books-to-understand-machine-learning

A growing consensus of researchers contend that new algorithms are needed to transform narrow AI to AGI. Brain Simulator II is free software for new algorithm development targeted at AGI that you can experiment with and participate in its development. Originally from KDnuggets https://ift.tt/2y2HhPA source https://365datascience.weebly.com/the-best-data-science-blog-2020/introducing-brain-simulator-ii-a-new-platform-for-agi-experimentation

Deep learning generating images This article will talk about implementing Deep learning in R on cifar10 data-set and train a Convolution Neural Network(CNN) model to classify 10,000 test images across 10 classes in R using Keras and Tensorflow packages. For details on the Cifar10 Dataset — https://www.cs.toronto.edu/~kriz/cifar.html But first, let’s talk a bit about R and Python —Continue reading “How to implement Deep Learning in R using Keras and Tensorflow”

The most common question regarding Competitive Programming is where do I start? Let me build this for you step by step: Continue reading on Becoming Human: Artificial Intelligence Magazine » Via https://becominghuman.ai/getting-started-with-competitive-programming-f6f72f4f0228?source=rss—-5e5bef33608a—4 source https://365datascience.weebly.com/the-best-data-science-blog-2020/getting-started-with-competitive-programming

Source Do you think 100% automation is a pipe dream? You’re not alone. But what if you’re wrong? Prior to joining Infrrd, I worked for a leading software vendor that helped Fortune 500 companies optimize their contact centers. We offered all sorts of analytics, customer engagement, and automation solutions, including self-service automation like chatbots. Having aContinue reading “Can you achieve 100% AI automation?”

Also: Should Data Scientists Model COVID19 and other Biological Events; Learning during a crisis (Data Science 90-day learning challenge); Data Transformation: Standardization vs Normalization; DBSCAN Clustering Algorithm in Machine Learning; Find Your Perfect Fit: A Quick Guide for Job Roles in the Data World Originally from KDnuggets https://ift.tt/3aMM4BV source https://365datascience.weebly.com/the-best-data-science-blog-2020/kdnuggets-news-20n17-apr-29-the-super-duper-nlp-repo-free-machine-learning-data-science-books-courses-for-quarantine

The book Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing by Ron Kohavi (Microsoft, Airbnb), Diane Tang (Google) and Ya Xu (LinkedIn) is available for purchase, with the authors proceeds from the book being donated to charity. Originally from KDnuggets https://ift.tt/2VMBl6d source https://365datascience.weebly.com/the-best-data-science-blog-2020/trustworthy-online-controlled-experiments-a-practical-guide-to-ab-testing

The COVID-19 pandemic has affected everything, and building predictions during this time is difficult. Data science teams need to update their models to prepare for the recovery, and know how to properly train 2020 data models to learn from the coronavirus anomaly. Originally from KDnuggets https://ift.tt/2xgpGTP source https://365datascience.weebly.com/the-best-data-science-blog-2020/how-data-scientists-can-train-and-updates-models-to-prepare-for-covid-19-recovery