Neural Networks and Deep Learning, a free online book.

The book will teach you about:

* Neural networks, a beautiful biologically-inspired programming paradigm which enables a computer to learn from observational data
* Deep learning, a powerful set of techniques for learning in neural networks

http://neuralnetworksanddeeplearning.com/index.html

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

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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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.

Learning From Data
Free course by Caltech - California Institute of Technology

✅ 23 sections with pdf slides and video lessons

https://work.caltech.edu/library/

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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

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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Deep learning at Oxford 2015

🎬 16 lessons
⏰ 15 hours

https://www.youtube.com/playlist?list=PLE6Wd9FR--EfW8dtjAuPoTuPcqmOV53Fu

#deeplearning #oxford
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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/

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

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

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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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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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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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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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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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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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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