​Black Hole Machine Learning

🔗 Black Hole Machine Learning
Neural Networks vie Theoretical Physics

​Самые интересные применения машинного обучения в социальных сетях, маркетинге и другом в 2019 году.

https://www.geeksforgeeks.org/top-machine-learning-applications-in-2019/

🔗 Top Machine Learning Applications in 2019 - GeeksforGeeks
Suppose you want to search Machine Learning on Google. Well, the results you will see are carefully curated and ranked by Google using Machine Learning!!!… Read More »

🎥 Fields in Data Science | What are the different fields in data science?
👁 1 раз ⏳ 1140 сек.
In this video, you will understand the #Data #Science #Fields such as Mathematics, statistics, Machine Learning, Cluster Analysis, Data Mining, Big data Analytics, Data Visualization, Artificial Intelligence, Neural Networks, Deep Learning, Deep Active Learning, Cognitive Computing.

Get Data Science Training: https://www.besanttechnologies.com/training-courses/data-warehousing-training/datascience-training-institute-in-chennai

For Best Training and Certifications Contact Us Now!
📞 Classroom : +91 8099 770

​Advanced Google Skills for Data Science

🔗 Advanced Google Skills for Data Science
Optimize your programming by searching like a professional

​Multiple Linear Regression-Beginner’s Guide

🔗 Multiple Linear Regression-Beginner’s Guide
In this article i will be focusing on making a multiple linear regression model from scratch in python for beginners.

🎥 Fall 2019 Robotics Colloquium: Debadeepta Dey (Microsoft Research)
👁 1 раз ⏳ 3380 сек.
Lecture title: Imitation-Learning with Indirect Oracles

We present Vision-based Navigation with Language-based Assistance (VNLA), a grounded vision-language task where an agent with visual perception is guided via language to find objects in photorealistic indoor environments. The task emulates a real-world scenario in that (a) the requester may not know how to navigate to the target objects and thus makes requests by only specifying high-level endgoals, and (b) the agent is capable of sensing when it is l

🎥 The Future of Artificial Intelligence: Crash Course AI #20
👁 1 раз ⏳ 660 сек.
Today, in our final episode of Crash Course AI, we're going to look towards the future. We've spent much of this series explaining how and why we don't have the Artificial General Intelligence (or AGI) that we see in the movies like Bladerunner, Her, or Ex Machina. Siri frequently doesn't understand us, we probably shouldn't sleep in our self-driving cars, and those recommended videos on YouTube and Netflix often aren't what we really want to watch next. So let's talk about what we do know, how we got here,

​What is Going on Inside the Brain When We Listen to Music? - New World : Artificial Intelligence

🔗 What is Going on Inside the Brain When We Listen to Music? - New World : Artificial Intelligence
When you listen to music, multiple areas of your brain become engaged and active. But when you actually play an instrument, that activity becomes more like a full-body brain workout. What's going on?

​Streamlit dashboard to run SQL queries on BigQuery.

Blog post: https://imadelhanafi.com/posts/bigquery_dashboard/

Live version: https://bigquery.imadelhanafi.com

Github repo: https://github.com/imadelh/Bigquery-Streamlit

🔗 BigQuery dashboard with Streamlit :: Imad El Hanafi — Portfolio & Blog
Introduction Live version: https://bigquery.imadelhanafi.com Github repo: https://github.com/imadelh/Bigquery-Streamlit Storing and querying large datasets is an important step for data analysis and predictive modeling. BigQuery is a serverless data warehouse that allows storing data (up to Terabytes) and runs fast SQL queries without worrying about the computing power. In this post, we will discover how to interact with BigQuery and render results in an interactive dashboard built using Streamlit.

​Analyzing and Improving the Image Quality of StyleGAN

https://github.com/NVlabs/stylegan2

Paper : https://arxiv.org/abs/1912.04958v1

https://paperswithcode.com/paper/analyzing-and-improving-the-image-quality-of

🔗 NVlabs/stylegan2
StyleGAN2 - Official TensorFlow Implementation. Contribute to NVlabs/stylegan2 development by creating an account on GitHub.

​Measuring Dataset Granularity.

http://arxiv.org/abs/1912.10154

🔗 Measuring Dataset Granularity
Despite the increasing visibility of fine-grained recognition in our field, "fine-grained'' has thus far lacked a precise definition. In this work, building upon clustering theory, we pursue a framework for measuring dataset granularity. We argue that dataset granularity should depend not only on the data samples and their labels, but also on the distance function we choose. We propose an axiomatic framework to capture desired properties for a dataset granularity measure and provide examples of measures that satisfy these properties. We assess each measure via experiments on datasets with hierarchical labels of varying granularity. When measuring granularity in commonly used datasets with our measure, we find that certain datasets that are widely considered fine-grained in fact contain subsets of considerable size that are substantially more coarse-grained than datasets generally regarded as coarse-grained. We also investigate the interplay between dataset granularity with a variety of factors an

​Facebook has a neural network that can do advanced math
https://www.technologyreview.com/s/614929/facebook-has-a-neural-network-that-can-do-advanced-math/#

🔗 Facebook has a neural network that can do advanced math
Other neural nets haven’t progressed beyond simple addition and multiplication, but this one calculates integrals and solves differential equations.

​Visual Domain Adaptation Challenge

http://ai.bu.edu/visda-2019/?fbclid=IwAR1duIuADj053gRA5nPPG73K6wi1eh9DQfFUXSjVNzKeGNOleVpkPX6FywE

🔗 VisDA2019: Visual Domain Adaptation Challenge
We are pleased to announce the 2017 Visual Domain Adaptation (VisDA2017) Challenge! The VisDA challenge aims to test domain adaptation methods’ ability to transfer source knowledge and adapt it to novel target domains. The goal is to develop a method of unsupervised syntetic-to-real domain adaptation

​Here's the original article if you want to know what it actually says. https://www.nature.com/articles/s41537-019-0077-9

🔗 A machine learning approach to predicting psychosis using semantic density and latent content analysis
A machine learning approach to predicting psychosis using semantic density and latent content analysis

​“AI: Monte Carlo Tree Search (MCTS)”


https://rsci.app.link/LczXR6YnO2_p=c11731dc9a0660eee31c8de3e9b6b9

🔗 Medium – Get smarter about what matters to you.
Medium is not like any other platform on the internet. Our sole purpose is to help you find compelling ideas, knowledge, and perspectives. We don’t serve ads—we serve you, the curious reader who loves to learn new things. Medium is home to thousands of independent voices, and we combine humans and technology to find the best reading for you—and filter out the rest.

​CS 188 : Introduction to Artificial Intelligence

https://inst.eecs.berkeley.edu/~cs188/fa18/

🔗 CS 188: Introduction to Artificial Intelligence, Fall 2018

​Long Short Term Memory and Gated Recurrent Unit’s Explained — ELI5 Way

🔗 Long Short Term Memory and Gated Recurrent Unit’s Explained — ELI5 Way
In this post, we will learn the intuition behind the working of LSTM and GRU.

​Can You Become a Data Scientist Without a Quantitative Degree?

🔗 Can You Become a Data Scientist Without a Quantitative Degree?
A story and some insights

🎥 Data Science For Beginners with Python 6 - If Else and Looping Constructs in Pandas Part 1
👁 1 раз ⏳ 723 сек.
Data Science For Beginners with Python 6 - Using If Else and Looping Constructs to modify the data in Pandas dataframe Part 1

Welcome to this course on Data Science For Beginners With Python. In video provides an Introduction to Data Science with Python- Copying, Selecting, Indexing data from pandas dataframes and different Attributes of Data in Pandas. What is Data Science?

Link to notebook and dataset: https://github.com/gshanbhag525/Programming-Knowledge-
Numpy tutorials: https://www.youtube.com/wa

🎥 Untitled
👁 4 раз ⏳ 2891 сек.