Forwarded from Cool GitHub repositories
InsightFace: 2D and 3D Face Analysis Project
Good implementation for face recognition, and landmark detection
ArcFace, CosFace, SubCenter-ArcFace, VPL, Partial-FC
https://github.com/deepinsight/insightface
Good implementation for face recognition, and landmark detection
ArcFace, CosFace, SubCenter-ArcFace, VPL, Partial-FC
https://github.com/deepinsight/insightface
GitHub
GitHub - deepinsight/insightface: State-of-the-art 2D and 3D Face Analysis Project
State-of-the-art 2D and 3D Face Analysis Project. Contribute to deepinsight/insightface development by creating an account on GitHub.
CS109 Data Science
By Harvard University
⌛️ 12 weeks
✅ Video lectures
✅ Slides
✅ Lab exercises
🔗 http://cs109.github.io/2015/pages/videos.html
Note: i have issues with first video link but others are fine.
#datascience #pyton #harvard
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By Harvard University
⌛️ 12 weeks
✅ Video lectures
✅ Slides
✅ Lab exercises
🔗 http://cs109.github.io/2015/pages/videos.html
Note: i have issues with first video link but others are fine.
#datascience #pyton #harvard
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cs109.github.io
Class Material
Forwarded from Graph Machine Learning
Graph ML in Industry Workshop
When I wrote top applications of GNNs at the beginning of this year, I had a feeling that graph ML community is mature enough to start being used in industrial companies. Nine months ahead we decided to gather researchers, engineers, and industry professionals to talk about applications of graphs in the companies. Please, join us on 23rd Sept, 17-00 Paris time (free, online, ~3 hours) by registering at the link.
When I wrote top applications of GNNs at the beginning of this year, I had a feeling that graph ML community is mature enough to start being used in industrial companies. Nine months ahead we decided to gather researchers, engineers, and industry professionals to talk about applications of graphs in the companies. Please, join us on 23rd Sept, 17-00 Paris time (free, online, ~3 hours) by registering at the link.
Google
Graph Machine Learning in Industry
Criteo AI Lab is excited to be presenting Graph Machine Learning in Industry. Please join us on Thursday, September 23rd, at 17:00 Paris time. This page will be updated with video links after the workshop.
Learning From Data
Free course by Caltech - California Institute of Technology
✅ 23 sections with pdf slides and video lessons
https://work.caltech.edu/library/
👉 Join @datascience_bds and @bigdataspecialist for more
Free course by Caltech - California Institute of Technology
✅ 23 sections with pdf slides and video lessons
https://work.caltech.edu/library/
👉 Join @datascience_bds and @bigdataspecialist for more
Four Deep Learning Papers to Read in September 2021
‘Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning’
Authors: Feurer et al. (2021)
📝 Paper
🤖 Code
‘How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers’
Authors: Steiner et al. (2021)
📝 Paper
🤖 Code
‘Catastrophic Fisher Explosion: Early Phase Fisher Matrix Impacts Generalization’
Authors: Jastrzebski et al. (2021)
📝 Paper
‘Do Vision Transformers See Like Convolutional Neural Networks?’
Authors: Raghu et al. (2021)
📝 Paper
Source: Medium
‘Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning’
Authors: Feurer et al. (2021)
📝 Paper
🤖 Code
‘How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers’
Authors: Steiner et al. (2021)
📝 Paper
🤖 Code
‘Catastrophic Fisher Explosion: Early Phase Fisher Matrix Impacts Generalization’
Authors: Jastrzebski et al. (2021)
📝 Paper
‘Do Vision Transformers See Like Convolutional Neural Networks?’
Authors: Raghu et al. (2021)
📝 Paper
Source: Medium
Reinforcement Learning Lecture Series 2021
🎬 13 lessons
⏰ 14 hours
Taught by DeepMind researchers, this series was created in collaboration with University College London (UCL) to offer students a comprehensive introduction to modern reinforcement learning.
https://deepmind.com/learning-resources/reinforcement-learning-series-2021
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🎬 13 lessons
⏰ 14 hours
Taught by DeepMind researchers, this series was created in collaboration with University College London (UCL) to offer students a comprehensive introduction to modern reinforcement learning.
https://deepmind.com/learning-resources/reinforcement-learning-series-2021
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Google DeepMind
Artificial intelligence could be one of humanity’s most useful inventions. We research and build safe artificial intelligence systems. We're committed to solving intelligence, to advance science...
Deep learning at Oxford 2015
🎬 16 lessons
⏰ 15 hours
https://www.youtube.com/playlist?list=PLE6Wd9FR--EfW8dtjAuPoTuPcqmOV53Fu
#deeplearning #oxford
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🎬 16 lessons
⏰ 15 hours
https://www.youtube.com/playlist?list=PLE6Wd9FR--EfW8dtjAuPoTuPcqmOV53Fu
#deeplearning #oxford
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YouTube
Deep learning at Oxford 2015
A course I taught in 2015 at Oxford University with the help of Brendan Shillingford. More information here: http://www.cs.ox.ac.uk/teaching/courses/2014-201...
The People + AI Guidebook by Google
The People + AI Guidebook is a set of methods, best practices and examples for designing with AI.
https://pair.withgoogle.com/guidebook/
The People + AI Guidebook is a set of methods, best practices and examples for designing with AI.
https://pair.withgoogle.com/guidebook/
Withgoogle
People + AI Guidebook
A toolkit for teams building human-centered AI products.
Get ready for second annual #NLPSummit by John Snow Labs.
Week One comes with 50+ unique sessions with a special track on #NLP in #Healthcare.
Week Two - beginner to advanced training workshops with certification.
Hear from industry leaders at NASA, Vonage, Zillow, Merck, Amazon, Walmart Labs, Booz Allen Hamilton, Morgan Stanley, Salesforce, Roku, Zillow and many more!
Free registration: https://www.nlpsummit.org/2021-events/
#ML #AI #digitalhealthcare #dataengineer #deeplearning
Week One comes with 50+ unique sessions with a special track on #NLP in #Healthcare.
Week Two - beginner to advanced training workshops with certification.
Hear from industry leaders at NASA, Vonage, Zillow, Merck, Amazon, Walmart Labs, Booz Allen Hamilton, Morgan Stanley, Salesforce, Roku, Zillow and many more!
Free registration: https://www.nlpsummit.org/2021-events/
#ML #AI #digitalhealthcare #dataengineer #deeplearning
Mathematics for Machine Learning
Published by Cambridge University Press (published April 2020)
https://mml-book.com
PDF: https://mml-book.github.io/book/mml-book.pdf
Published by Cambridge University Press (published April 2020)
https://mml-book.com
PDF: https://mml-book.github.io/book/mml-book.pdf
Forwarded from Free programming books
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Introduction to Machine Learning (Fall 2020)
By Massachusetts Institute of Technology, MIT
Length: 13 weeks
🔗 Course link
#ml #machinelearning #datascience #MIT
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By Massachusetts Institute of Technology, MIT
Length: 13 weeks
🔗 Course link
#ml #machinelearning #datascience #MIT
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Introduction to Data Science by University of Washington
🎬 95 video sessions
⏰ Duration: 16h
👨🏫 Instructor: Bill Howe, PhD
✅ Completely free
🔗 Course link
#datascience #ds #ml #washingtonuniversity
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🎬 95 video sessions
⏰ Duration: 16h
👨🏫 Instructor: Bill Howe, PhD
✅ Completely free
🔗 Course link
#datascience #ds #ml #washingtonuniversity
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Data Science: Python for Data Analysis 2022 Full Bootcamp
Rating ⭐️: 4.3 out of 5
Students 👨🏫: 104,287
Created by: Ahmed Ibrahim and SDE OCTOPUS | AI
🔗 Course link
Note: Free coupon is inserted in URL. Number of free spots is limited to 1000. Once this number is reached, coupon won't be valid anymore.
#python #datanalysis #datascience
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Rating ⭐️: 4.3 out of 5
Students 👨🏫: 104,287
Created by: Ahmed Ibrahim and SDE OCTOPUS | AI
🔗 Course link
Note: Free coupon is inserted in URL. Number of free spots is limited to 1000. Once this number is reached, coupon won't be valid anymore.
#python #datanalysis #datascience
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Udemy
Data Science: Python for Data Analysis Full Bootcamp
Learn and build your Python Programming skills from the ground up in addition to Python Data Science libraries and tools
Free 10-Hour Machine Learning Course
by freecodecamp
Section 1: Basics of Machine Learning
Section 2: Linear Regression & Regularization
Section 3: Logistic Regression & Performance Metrics
Section 4: Support Vector Machine
Section 5: PCA
Section 6: Learning Theory
Section 7: Decision Trees & Random Forest
Section 7.5: Learning more algorithms and building more projects
Section 8: Unsupervised Learning Algorithms
Section 9: Building Applications
🔗 Course link: https://www.freecodecamp.org/news/free-machine-learning-course-10-hourse/
10-hour youtube video: https://www.youtube.com/watch?v=NWONeJKn6kc
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by freecodecamp
Section 1: Basics of Machine Learning
Section 2: Linear Regression & Regularization
Section 3: Logistic Regression & Performance Metrics
Section 4: Support Vector Machine
Section 5: PCA
Section 6: Learning Theory
Section 7: Decision Trees & Random Forest
Section 7.5: Learning more algorithms and building more projects
Section 8: Unsupervised Learning Algorithms
Section 9: Building Applications
🔗 Course link: https://www.freecodecamp.org/news/free-machine-learning-course-10-hourse/
10-hour youtube video: https://www.youtube.com/watch?v=NWONeJKn6kc
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freeCodeCamp.org
Free 10-Hour Machine Learning Course
Every day more and more use cases are found for machine learning. It is a great field to get into. We just released a 10-hour machine learning course for beginners on the freeCodeCamp.org YouTube channel. Ayush Singh developed this course. He is a yo...
ML and NLP Research Highlights of 2021
by Sebastian Ruder
This post summarizes progress across multiple impactful areas in ML and NLP in 2021.
Contents:
Universal Models
Massive Multi-task Learning
Beyond the Transformer
Prompting
Efficient Methods
Benchmarking
Conditional Image Generation
ML for Science
Program Synthesis
Bias
Retrieval Augmentation
Token-free Models
Temporal Adaptation
The Importance of Data
Meta-learning
https://ruder.io/ml-highlights-2021/
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by Sebastian Ruder
This post summarizes progress across multiple impactful areas in ML and NLP in 2021.
Contents:
Universal Models
Massive Multi-task Learning
Beyond the Transformer
Prompting
Efficient Methods
Benchmarking
Conditional Image Generation
ML for Science
Program Synthesis
Bias
Retrieval Augmentation
Token-free Models
Temporal Adaptation
The Importance of Data
Meta-learning
https://ruder.io/ml-highlights-2021/
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ruder.io
ML and NLP Research Highlights of 2021
This post summarizes progress across multiple impactful areas in ML and NLP in 2021.
Machine Learning for Healthcare (Spring 2019)
By Massachusetts Institute of Technology (MIT)
🎬 25 video lessons
⏰ 33 hours
👨🏫 Prof. Peter Szolovits
👨🏫 Prof. David Sontag
https://www.classcentral.com/course/mit-opencourseware-machine-learning-for-healthcare-spring-2019-40955/classroom
#ml #machinelearning #healthcare #MIT
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By Massachusetts Institute of Technology (MIT)
🎬 25 video lessons
⏰ 33 hours
👨🏫 Prof. Peter Szolovits
👨🏫 Prof. David Sontag
https://www.classcentral.com/course/mit-opencourseware-machine-learning-for-healthcare-spring-2019-40955/classroom
#ml #machinelearning #healthcare #MIT
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Rules of Machine Learning:
Best Practices for ML Engineering
Author: Martin Zinkevich
This document is intended to help those with a basic knowledge of machine learning get thebenefit of best practices in machine learning from around Google.
👉 43 ML Rules to follow
🔗 http://martin.zinkevich.org/rules_of_ml/rules_of_ml.pdf
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Best Practices for ML Engineering
Author: Martin Zinkevich
This document is intended to help those with a basic knowledge of machine learning get thebenefit of best practices in machine learning from around Google.
👉 43 ML Rules to follow
🔗 http://martin.zinkevich.org/rules_of_ml/rules_of_ml.pdf
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Graph ML and deep learning courses
This is another post on your request. Other courses you requested will be shared in following days.
Geometric Deep learning course
AMMI21
👨🏫 Teachers: Michael M. Bronstein, Joan Bruna, Taco Cohen, Petar Veličković
📚12 lectures, 2 tutorials, and 4 seminars
This course follows GDL BOOK
🔗 Course link: https://geometricdeeplearning.com/lectures/
Machine Learning for Graphs and Sequential Data (MLGS)
by Stephan Günnemann
Awesome course covering in depth generative models, robustness, sequential data, clustering, label propagation, GNNs, and more
🔗 Course link: https://www.in.tum.de/daml/teaching/mlgs/
Stanford CS224W course on graph ML
A legendary Stanford CS224W course on graph ML now releases videos on YouTube for 2021
🎬 60 Videos
⏰ 30h
🔗 Course link
Python For Data Science (Udemy)
This course specifically created for Data Science / AI / ML / DL. It covers BASICS PYTHON ONLY
Rating ⭐️: 4.1 out of 5
Students 👨🎓: 65,523 students
Duration ⏰: 3hr 55min of on-demand video
🔗 Course link
Deep Learning Prerequisites: The Numpy Stack in Python V2 (Udemy)
Rating ⭐️: 4.6 out of 5
Students 👨🎓: 34,785
Duration ⏰: 1hr 59min of on-demand video
🔗 Course link
There is also this cool blogpost by Gordić Aleksa:
How to get started with Graph Machine Learning
And one early access version book:
Graph Powered Machine Learning
by: Allesandro Negro
🔗 Book link
#graphML #ML #machinelearning #deeplearning #python
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This is another post on your request. Other courses you requested will be shared in following days.
Geometric Deep learning course
AMMI21
👨🏫 Teachers: Michael M. Bronstein, Joan Bruna, Taco Cohen, Petar Veličković
📚12 lectures, 2 tutorials, and 4 seminars
This course follows GDL BOOK
🔗 Course link: https://geometricdeeplearning.com/lectures/
Machine Learning for Graphs and Sequential Data (MLGS)
by Stephan Günnemann
Awesome course covering in depth generative models, robustness, sequential data, clustering, label propagation, GNNs, and more
🔗 Course link: https://www.in.tum.de/daml/teaching/mlgs/
Stanford CS224W course on graph ML
A legendary Stanford CS224W course on graph ML now releases videos on YouTube for 2021
🎬 60 Videos
⏰ 30h
🔗 Course link
Python For Data Science (Udemy)
This course specifically created for Data Science / AI / ML / DL. It covers BASICS PYTHON ONLY
Rating ⭐️: 4.1 out of 5
Students 👨🎓: 65,523 students
Duration ⏰: 3hr 55min of on-demand video
🔗 Course link
Deep Learning Prerequisites: The Numpy Stack in Python V2 (Udemy)
Rating ⭐️: 4.6 out of 5
Students 👨🎓: 34,785
Duration ⏰: 1hr 59min of on-demand video
🔗 Course link
There is also this cool blogpost by Gordić Aleksa:
How to get started with Graph Machine Learning
And one early access version book:
Graph Powered Machine Learning
by: Allesandro Negro
🔗 Book link
#graphML #ML #machinelearning #deeplearning #python
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Geometricdeeplearning
GDL Course
Grids, Groups, Graphs, Geodesics, and Gauges