ML Time

Improving the Aftermath Management of an Earthquake
Improving the Aftermath Management of an Earthquake
Omdena | Building AI Solutions for Real-World Problems
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Happy Valentine's Day from MATLAB ❤️ Code ⬇️
MathWorks on LinkedIn: Happy Valentine's Day from MATLAB ❤️ Code ⬇️   [y,x,z] = ndgrid(linspace | 98 comments
linkedin
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50+ Statistics Interview Questions and Answers for Data Scientists for 2021
https://towardsdatascience.com/50-statistics-interview-questions-and-answers-for-data-scientists-for-2021-24f886221271
towardsdatascience.com
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DIY- Multi-Level Dendrogram
Siddharth Pawar on LinkedIn: #vismydata #vismydata #tableau
linkedin
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How to use ML to perform RF modulation recognition?
James Heaton on LinkedIn: Xilinx/Vitis-Tutorials
linkedin
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Tools for building robust, state-of-the-art machine learning models
Tools for building robust, state-of-the-art machine learning models
The Data Exchange
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English Audio Speech-to-Text Transcript with Hugging Face | Python NLP
​https://www.linkedin.com/posts/amrrs_english-audio-speech-to-text-transcript-with-activity-6766025732201287680-1DSy/​
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Neural Re-rendering for Full-frame Video Stabilization
Hybrid Neural Fusion for Full-frame Video Stabilization
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AI-ML Cheat Sheets
https://www.linkedin.com/feed/update/urn:li:activity:6765855868929089537/
www.linkedin.com
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Q4 2020 Food Delivery & Rideshare Sales Report
Q4 2020 Food Delivery & Rideshare Sales Report
Medium
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AI PAPER SUMMARY
AI Paper Summary Archives
MarkTechPost
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Microservices Architecture at Netflix!
Srivathsan Vijayaraghavan on LinkedIn: Check out the Microservices Architecture at Netflix! 1. Client sends
linkedin
10 Awesome Data Science Courses to make you an Awesome Data Scientist
https://www.linkedin.com/feed/update/urn:li:activity:6765214718861938688/
www.linkedin.com
Yann LeCun’s Deep Learning Course
Arpit Singh on LinkedIn: #DataScience #ML #machinelearning | 20 comments
linkedin
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Analyzing seasonality with Fourier transforms using Python & SciPy
Theophano Mitsa Ph.D. on LinkedIn: Analyzing seasonality with Fourier transforms using Python & SciPy
linkedin
An open-source text annotation tool for humans
Steve Nouri on LinkedIn: #innovation #artificialintelligence #machinelearning | 58 comments
linkedin
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PORORO: Platform Of neuRal mOdels for natuRal language prOcessing 
Vincent Boucher on LinkedIn: #MachineLearning #NaturalLanguageProcessing #NLP
linkedin
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Indian Flag with Turtle using Python
 This Republic day I tried to do something creative and made an Indian Flag with Turtle using Python. Turtle is a pre-installed Python library. It enables users to create pictures and shapes by providing them with a virtual canvas. 
​https://www.linkedin.com/posts/ayushi7rawat_python3-githubcli-python-ugcPost-6759845965777125376-Il15​
You can also refer to the YouTube video tutorial for better understanding: https://lnkd.in/dUrctgE 
GitHub Repo: https://lnkd.in/disintQ 
Blog link: https://lnkd.in/dcUuTgg​
The Shiny AWS Book
The Shiny AWS Book
bizscienc
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10 Most Stable Linux Distros In 2021
10 Most Stable Linux Distros In 2021
Analytics India Magazine
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Python’s Pandas vs. R’s dplyr
Python’s Pandas vs. R’s dplyr – Which Is The Best Data Analysis Library | R-bloggers
R-bloggers
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Top Ten Kaggle Notebooks For Data Science Enthusiasts In 2021
Top Ten Kaggle Notebooks For Data Science Enthusiasts In 2021
Analytics India Magazine
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An R package to build your resume or CV
Ludovic BOCKEN, PhDs (c) on LinkedIn: jaredlander/resumer
linkedin
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Applied Data Science: Free E-Book
Machine Learning India on LinkedIn: Applied Data Science: Free E-Book | 14 comments
linkedin
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 "The Data Engineering Cookbook" by Andreas Kretz!!
https://www.linkedin.com/posts/imarpit_the-data-engineering-cookbook-by-andreas-activity-6763295036466626560-4itW/
www.linkedin.com
Data Cleaning
uday kiran reddy kondreddy on LinkedIn: data cleaning
linkedin
Wind Forecast
Florian Roscheck on LinkedIn: flrs/caiso_wind_forecast
linkedin
Fundamentals of Python Programming
Arpit Singh on LinkedIn: Fundamentals of Python Programming by Richard Halterman | 19 comments
linkedin
Mistakes to avoid as a Data Scientist
Learn.MachineLearning on LinkedIn: mistakes to avoid | 11 comments
linkedin
Fake AI generated blog
MIT Technology Review on LinkedIn: A college kid created a fake, AI-generated blog. It reached #1 on  | 16 comments
linkedin
ML Interview Questions
Arpit Singh on LinkedIn: 41 Essential Machine Learning Interview QnAs
linkedin
BudgetML
GitHub - ebhy/budgetml: Deploy a ML inference service on a budget in less than 10 lines of code.
GitHub
Hamza Tahir on LinkedIn: ebhy/budgetml
linkedin
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A Comprehensive guide to Fine-tuning Deep Learning Models in Keras (Part II) | Felix Yu
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Canada’s AI Corridor is Maturing: The Canadian AI Ecosystem in 2018 - jfgagne
jfgagne
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Canadian AI Ecosystem 2018 - jfgagne
jfgagne
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Why we switched from Flask to FastAPI for production machine learning
Medium
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Steve Nouri on LinkedIn: #artificialintelligence #datascience #deeplearning | 40 comments
linkedin
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A Flask API for serving scikit-learn models
Medium
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Philip Vollet on LinkedIn: #nlp #neuralnetworks #knowledgegraphs
linkedin
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Modeling Global and Local Node Contexts for Text Generation from...
arXiv.org
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GitHub - UKPLab/kg2text: Modeling Global and Local Node Contexts for Text Generation from Knowledge Graphs (authors' implementation for the TACL20 paper)
GitHub
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Steve Nouri on LinkedIn: #augmentedreality #technology #datascience | 108 comments
linkedin
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Philip Vollet on LinkedIn: #GNN #machinelearning #deeplearning | 15 comments
linkedin
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Ontario schools will now teach first graders financial literacy and coding to better prepare students for jobs of the future
CNN
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Morphological Processing for தமிழ் — The Unsupervised Way
Medium
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PyTorch
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GitHub - ChoudharyChanchal/game_control
GitHub
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R-CNN object detection with Keras, TensorFlow, and Deep Learning - PyImageSearch
PyImageSearch
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https://medium.com/towards-artificial-intelligence/main-types-of-neural-networks-and-its-applications-tutorial-734480d7ec8e
medium.com
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You Don’t Understand Neural Networks Until You Understand the Universal Approximation Theorem
Medium
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1-Introduction to Time Series and Forecasting https://lnkd.in/gqZbe7p​
2-Statistics and Analysis of Scientific Data https://lnkd.in/g-hbjWZ​
3-Linear Algebra Done Right https://lnkd.in/gqCT-6g​
4-Linear Algebra https://lnkd.in/gJSPXQd​
5-Algebra https://lnkd.in/gc-H3Kk​
6-Understanding Analysis https://lnkd.in/g_RXeVk​
7-Understanding Statistics Using R https://lnkd.in/g27NnMy​
8-An Introduction to Statistical Learning https://lnkd.in/gyjbQ-A​
9-Statistical Learning from a Regression Perspective https://lnkd.in/g2gFWfi​
10-Robotics https://lnkd.in/gJDEgec​
11-Regression Modeling Strategies https://lnkd.in/gR3Mufh​
12-A Modern Introduction to Probability and Statistics https://lnkd.in/gCgbX6m​
13-The Python Workbook https://lnkd.in/gw4tAf7​
14-Machine Learning in Medicine — a Complete Overview https://lnkd.in/g43uZiT​
15-Introduction to Data Science https://lnkd.in/gSvtBCs​
16-Applied Predictive Modeling https://lnkd.in/gcHus2e​
1.Data Science Process https://pos.li/2fki4i​
2.Data Visualization in Business https://pos.li/2fki4j​
3.Know Machine Learning Key Terminology https://pos.li/2fki4k​
4.Understand Machine Learning Implementation https://pos.li/2fki4l​
5.Machine Learning Applications on Marketing https://pos.li/2fki4m and Retail https://pos.li/2fki4f​
Is Canada still in the lead in AI?
LinkedInEditors
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TOP 10 SQL Concepts for Job Interview
1.Aggregate Functions (SUM/AVG)
2.Group By and Order By
3.JOINs (Inner/Left/Right)
4.Union and Union All
5.Date and Time processing
6.String processing
7.Window Functions (Partition by)
8.Subquery
9.View and Index
10.Common Table Expression (CTE)
TOP 10 Statistics Concepts for Job Interview
1.Sampling
2.Experiments (A/B tests)
3.Descriptive Statistics
4.p-value
5.Probability Distributions
6.t-test
7.ANOVA
8.Correlation
9.Linear Regression
10.Logistics Regression
TOP 10 Python Concepts for Job Interview
1.Reading data from file/table
2.Writing data to file/table
3.Data Types
4.Function
5.Data Preprocessing (numpy/pandas)
6.Data Visualisation (Matplotlib/seaborn/bokeh)
7.Machine Learning (sklearn)
8.Deep Learning (Tensorflow/Keras/PyTorch)
9.Distributed Processing (PySpark)
10.Functional and Object Oriented Programming
Certificate Program in Quantum Engineering and Technology
University of Chicago Professional Education
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Royal Bank of Canada, Borealis AI partner with Red Hat, NVIDIA on new AI computing platform | BetaKit
BetaKit
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GitHub - adityamhatre/GameCarControl-CV
GitHub
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Car controller for games using OpenCV
Medium
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Gallery — pydeck 0.6.1 documentation
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GitHub - dheerajiiitv/T5-paraphrase-generation
GitHub
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10 COOL GPT-3 DEMOS
MLT | MACHINE LEARNING TOKYO
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Data Science Topics: CRISP – DM - Project Management Methodology Exploratory Data Analytics (EDA) / Descriptive Analytics Statistical Data Business Intelligence and Data Visualization Plots & Inferential Statistics Probability Distributions (Continuous & Discrete) Hypothesis Testing - The ‘4’ Must Know Hypothesis Tests Data Mining Supervised Learning – Linear Regression, OLS Predictive Modelling – Multiple Linear Regression Lasso and Ridge Regressions Logistic Regression – Binary Value Prediction, MLE Multinomial Regression Advanced Regression for Count Data Data Mining Unsupervised Learning - Clustering Data Mining Unsupervised Learning - Dimension Reduction (PCA) Data Mining Unsupervised Learning - Association Rules Recommendation Engine Network Analytics Machine Learning - k - NN Classifier Decision Tree & Random Forest Ensemble Techniques - Bagging and Boosting AdaBoost & Extreme Gradient Boosting Text Mining & Natural Language Processing (NLP) Machine Learning Classifier Technique - Naive Bayes Introduction to Perceptron, Multilayer Perceptron Building Blocks of Neural Network Deep Learning Black Box Technique - Neural Network Deep Learning Black Box Technique - SVM Survival Analytics Forecasting/Time Series – Model Driven Algorithms Forecasting/Time Series – Data Driven Algorithms
BLURB Leaderboard
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Domain-Specific Language Model Pretraining for Biomedical Natural...
arXiv.org
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Step-by-step guide to contributing on GitHub
Data School
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GitHub - tiangolo/full-stack-fastapi-postgresql: Full stack, modern web application generator. Using FastAPI, PostgreSQL as database, Docker, automatic HTTPS and more.
GitHub
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Launch Your Career in Data Science
EliteDataScience
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Writer Identification Using Microblogging Texts for Social Media Forensics
arXiv.org
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TinyML is giving hardware new life
TechCrunch
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GitHub - goru001/inltk: Natural Language Toolkit for Indic Languages aims to provide out of the box support for various NLP tasks that an application developer might need
GitHub
 
Natural Language Toolkit for Indic Languages — iNLTK latest documentation
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GitHub - naiveHobo/InvoiceNet: Deep neural network to extract intelligent information from invoice documents.
GitHub
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Group Normalization
Committed towards better future
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Aman Arora on LinkedIn: Group Normalization
linkedin
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Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data
Uber Engineering Blog
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Getting Started in NLP/ML Research - Victor Zhong
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GitHub - TDAmeritrade/stumpy: STUMPY is a powerful and scalable Python library for modern time series analysis
GitHub
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STUMPY documentation — stumpy 1.10.2 documentation
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STUMPY
Medium
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GitHub - Farama-Foundation/PettingZoo: Gym for multi-agent reinforcement learning
GitHub
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GitHub - jim-schwoebel/allie: 🤖 A machine learning framework for audio, text, image, video, or .CSV files (50+ featurizers and 15+ model trainers).
GitHub
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Twitter icon
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​http://gvv.mpi-inf.mpg.de/projects/NHRR/ http://gvv.mpi-inf.mpg.de/projects/NHRR/data/1415.pdf​
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Machine Learning with Graphs, Leskovec 
http://snap.stanford.edu/class/cs224w-videos-2019/
snap.stanford.edu
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China has started a grand experiment in AI education. It could reshape how the world learns.
MIT Technology Review
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1-The Elements of Statistical Learning https://lnkd.in/gd-PpCa​
2-Introductory Time Series with R https://lnkd.in/gemsHzv​
3-A Beginner’s Guide to R https://lnkd.in/gwQ6m_B​
4-Data Structures and Algorithms with Python https://lnkd.in/g9_djwr​
5-Introduction to Statistics and Data Analysis https://lnkd.in/gUUJMCj​
6-Principles of Data Mining https://lnkd.in/gMgmc-P​
7-Computer Vision https://lnkd.in/gftqxf9​
8-Data Mining https://lnkd.in/gJ6iugA​
9-Robotics, Vision and Control https://lnkd.in/gq-q9i5​
10-Statistical Analysis and Data Display https://lnkd.in/grCNSYq​
11-Statistics and Data Analysis for Financial Engineering https://lnkd.in/gRn8zWd​
12-Stochastic Processes and Calculus https://lnkd.in/gnWaWW6​
13Statistical Analysis of Clinical Data on a Pocket Calculator https://lnkd.in/gM7VCgG​
14-Clinical Data Analysis on a Pocket Calculator https://lnkd.in/gvxBvg6​
15-The Data Science Design Manual https://lnkd.in/gKxgaRD​
16-An Introduction to Machine Learning https://lnkd.in/gykPC2B​
On-device, Real-time Body Pose Tracking with MediaPipe BlazePose
Google AI Blog
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Slideio: a new python library for reading medical images.
Medium
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Keras documentation: CycleGAN
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Text Generation with Bi-LSTM in PyTorch
Medium
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Amazon's Machine Learning University is making its online courses available to the public
Amazon Science
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The untold story of GPT-3 is the transformation of OpenAI
TechTalks
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GitHub - HenriquesLab/ZeroCostDL4Mic: ZeroCostDL4Mic: A Google Colab based no-cost toolbox to explore Deep-Learning in Microscopy
GitHub
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Home · HenriquesLab/ZeroCostDL4Mic Wiki
GitHub
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ZeroCostDL4Mic: an open platform to use Deep-Learning in Microscopy
bioRxiv
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Too many AI researchers think real-world problems are not relevant
MIT Technology Review
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OCR with Keras, TensorFlow, and Deep Learning - PyImageSearch
PyImageSearch
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The Laplace Transform: A Generalized Fourier Transform 
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Facebook and NYU use artificial intelligence to make MRI scans four times faster
The Verge
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Microsoft leads $16M round into AI data science startup Pachyderm
SiliconANGLE
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GitHub - abhimishra91/insight: Repository for Project Insight: NLP as a Service
GitHub
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GitHub - DevashishPrasad/CascadeTabNet: This repository contains the code and implementation details of the CascadeTabNet paper "CascadeTabNet: An approach for end to end table detection and structure recognition from image-based documents"
GitHub
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CVPR2020 Workshop on Text and Documents in the Deep Learning Era
CVPR2020 Workshop on Text and Documents in the Deep Learning Era
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Google Colaboratory
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GitHub - linkedin/detext: DeText: A Deep Neural Text Understanding Framework for Ranking and Classification Tasks
GitHub
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https://dl.acm.org/doi/10.1145/3331184.3331381
dl.acm.org
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The Ultimate guide to AI, Data Science & Machine Learning, Articles, Cheatsheets and Tutorials ALL in one place
LinkedInEditors
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https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.161.3556&rep=rep1&type=pdf
’Introduction to Statistics for Data Science, Exploratory Data Analysis in Python‘
Distributions
Probability Mass Functions
Cumulative distribution functions
Modeling distributions
Probability density functions
Relationships between variables
Estimation
Hypothesis testing
Linear least squares
Regression
Time series analysis
Survival analysis
Analytic methods
Credit: Allen B. Downey
Forward from the 'Deep Learning for Coders' Book
fastdotai
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fastbook/01_intro.ipynb at master · fastai/fastbook
GitHub
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GitHub - fastai/fastbook: The fastai book, published as Jupyter Notebooks
GitHub
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XGBoost Tutorials: 
​https://lnkd.in/g3dSRxz 
1-Introduction to Boosted Trees 
2-Distributed XGBoost with AWS YARN 
3-Distributed XGBoost with XGBoost4J-Spark 
4-DART booster 
5-Monotonic Constraints 
6-Random Forests in XGBoost 
7-Feature Interaction Constraints 
8-Text Input Format of DMatrix 
9-Notes on Parameter Tuning 
10-Using XGBoost External Memory Version (beta)
XGBoost Algorithm | XGBoost In Machine Learning
Analytics Vidhya
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CatBoost vs. Light GBM vs. XGBoost - KDnuggets
KDnuggets
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GitHub - INF800/realtime-forex-api
GitHub
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​https://github.com/rakesh4real/fastapi-template https://realtime-forex.herokuapp.com/​
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​https://www.mit.edu/~amidi/teaching/data-science-tools/ https://github.com/shervinea/mit-15-003-data-science-tools​
​https://www.python-machinelearning.com/​
​https://guide.allennlp.org/overview​
GitHub - DevashishPrasad/CascadeTabNet: This repository contains the code and implementation details of the CascadeTabNet paper "CascadeTabNet: An approach for end to end table detection and structure recognition from image-based documents"
GitHub
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