Mathematics_for_Machine_Learning_by_Marc_Peter_Deisenroth,_A_Aldo.pdf
16.6 MB
🧠 Topic: Mathematics for Machine Learning by Marc Peter Deisenroth, A Aldo Faisal, Cheng Soon Ong
💠 Link: https://mml-book.github.io/
💡 Tags: #mathematics #machine_learning #book
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💠 Link: https://mml-book.github.io/
💡 Tags: #mathematics #machine_learning #book
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Machine_Learning_A_Probabilistic_Perspective_by_Kevin_P_Murphy_z.pdf
25.7 MB
🧠 Topic: Machine Learning: A Probabilistic Perspective by Kevin P. Murphy
💠 Link: https://probml.github.io/pml-book/
💡 Tags: #machine_learning #book
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💠 Link: https://probml.github.io/pml-book/
💡 Tags: #machine_learning #book
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🧠 Topic: Neural Rendering Course (SIGGGRAPH 2021)
💠 Link: https://youtu.be/otly9jcZ0Jg
💡 Tags: #deep_learning #GANs #artificial_intelligence #computer_graphics #neural_rendering
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💠 Link: https://youtu.be/otly9jcZ0Jg
💡 Tags: #deep_learning #GANs #artificial_intelligence #computer_graphics #neural_rendering
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YouTube
Advances in Neural Rendering (SIGGRAPH 2021 Course) Part 1 of 2
This is an updated version of our CVPR 2020 tutorial
(https://www.youtube.com/watch?v=LCTYRqW-ne8).
Much have changed in a year!
For more details, visit https://www.neuralrender.com/
Introduction
0:00:00 Intro & Fundamentals
Generative Adversarial Networks…
(https://www.youtube.com/watch?v=LCTYRqW-ne8).
Much have changed in a year!
For more details, visit https://www.neuralrender.com/
Introduction
0:00:00 Intro & Fundamentals
Generative Adversarial Networks…
🧠 Topic: Task2Vec
💠 Link: https://openaccess.thecvf.com/content_ICCV_2019/papers/Achille_Task2Vec_Task_Embedding_for_Meta-Learning_ICCV_2019_paper.pdf
💡 Tags: #meta_learning #deep_learning
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💠 Link: https://openaccess.thecvf.com/content_ICCV_2019/papers/Achille_Task2Vec_Task_Embedding_for_Meta-Learning_ICCV_2019_paper.pdf
💡 Tags: #meta_learning #deep_learning
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🧠 Topic: Middlesex University Dubai MSc Data Science course
💠 Link: https://github.com/IvanReznikov/mdx-msc-data-science
💡 Tags: #machine_learning #deep_learning
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💠 Link: https://github.com/IvanReznikov/mdx-msc-data-science
💡 Tags: #machine_learning #deep_learning
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GitHub
GitHub - IvanReznikov/mdx-msc-data-science: Middlesex University Dubai: MSc Data Science. Modelling, Regression and Machine Learning…
Middlesex University Dubai: MSc Data Science. Modelling, Regression and Machine Learning track. Instructor: Dr. Ivan Reznikov - IvanReznikov/mdx-msc-data-science
🧠 Topic: Data Science 4 Life Science: Chemistry lectures
💠 Link: https://github.com/IvanReznikov/ds4ls-public
💡 Tags: #machine_learning #deep_learning #chemistry
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💠 Link: https://github.com/IvanReznikov/ds4ls-public
💡 Tags: #machine_learning #deep_learning #chemistry
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GitHub
GitHub - IvanReznikov/ds4ls-public
Contribute to IvanReznikov/ds4ls-public development by creating an account on GitHub.
🧠 Topic: Deep Learning Nanodegree Foundation
💠 Link: https://github.com/IvanReznikov/deep-learning
💡 Tags: #machine_learning #deep_learning ➖➖➖➖➖➖➖
🧩 More: @Aitive
💠 Link: https://github.com/IvanReznikov/deep-learning
💡 Tags: #machine_learning #deep_learning ➖➖➖➖➖➖➖
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10 Techniques to deal with Imbalanced Data.pdf
369.7 KB
🧠 Topic: 10 Techniques to deal with Imbalanced Data
💡 Tags: #machine_learning #deep_learning
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💡 Tags: #machine_learning #deep_learning
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🧠 Topic: TensorFlow on Databricks
💠 Link: https://databricks.com/tensorflow/getting-started-with-tensorflow-on-databricks
💡 Tags: #tensor_flow #deep_learning
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💠 Link: https://databricks.com/tensorflow/getting-started-with-tensorflow-on-databricks
💡 Tags: #tensor_flow #deep_learning
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Databricks
The Data and AI Company — Databricks
The Databricks Platform is the world’s first data intelligence platform powered by generative AI. Infuse AI into every facet of your business.
🧠 Topic: Visualizing A Neural Machine Translation Model (Mechanics of Seq2seq Models With Attention)
💠 Link: https://jalammar.github.io/visualizing-neural-machine-translation-mechanics-of-seq2seq-models-with-attention/
💡 Tags: #seq2seq #deep_learning
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💠 Link: https://jalammar.github.io/visualizing-neural-machine-translation-mechanics-of-seq2seq-models-with-attention/
💡 Tags: #seq2seq #deep_learning
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jalammar.github.io
Visualizing A Neural Machine Translation Model (Mechanics of Seq2seq Models With Attention)
Translations: Chinese (Simplified), French, Japanese, Korean, Persian, Russian, Turkish, Uzbek
Watch: MIT’s Deep Learning State of the Art lecture referencing this post
May 25th update: New graphics (RNN animation, word embedding graph), color coding, elaborated…
Watch: MIT’s Deep Learning State of the Art lecture referencing this post
May 25th update: New graphics (RNN animation, word embedding graph), color coding, elaborated…
🧠 Topic: Applied Machine Learning in Python
💠 Link: https://amueller.github.io/aml/00-introduction/00-introduction.html
💡 Tags: #machine_learning
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💠 Link: https://amueller.github.io/aml/00-introduction/00-introduction.html
💡 Tags: #machine_learning
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🧠 Topic: WRNs — Wide Residual Networks
💠 Link: https://towardsdatascience.com/review-wrns-wide-residual-networks-image-classification-d3feb3fb2004
💡 Tags: #machine_learning #computer_vision
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💠 Link: https://towardsdatascience.com/review-wrns-wide-residual-networks-image-classification-d3feb3fb2004
💡 Tags: #machine_learning #computer_vision
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Medium
Review: WRNs — Wide Residual Networks (Image Classification)
This time, WRNs (Wide Residual Networks) is presented. By widening Residual Network (ResNet), the network can be shallower with the same…
🧠 Topic: Open-Access Neuroscience Electronic Textbook
💠 Link: https://nba.uth.tmc.edu/neuroscience/
💡 Tags: #neuroscience
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💠 Link: https://nba.uth.tmc.edu/neuroscience/
💡 Tags: #neuroscience
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🧠 Topic: BAYESIAN META-LEARNING IS ALL YOU NEED
💠 Link: https://jameskle.com/writes/bayesian-meta-learning-is-all-you-need
💡 Tags: #deep_learning #meta_learning
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💠 Link: https://jameskle.com/writes/bayesian-meta-learning-is-all-you-need
💡 Tags: #deep_learning #meta_learning
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James Le
Bayesian Meta-Learning Is All You Need — James Le
This blog post is my attempt to demystify the probabilistic view of meta-learning and answer these key questions: Why is the deterministic view of meta-learning not sufficient? What is the variational inference? How can we design neural-based Bayesian…
🧠 Topic: Optimization and Online Learning
💠 Link: https://www.youtube.com/watch?v=ee-HYD6kKqM&list=PLXsmhnDvpjORzPelSDs0LSDrfJcqyLlZc
💡 Tags: #deep_learning #machine_learning #optimization
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💠 Link: https://www.youtube.com/watch?v=ee-HYD6kKqM&list=PLXsmhnDvpjORzPelSDs0LSDrfJcqyLlZc
💡 Tags: #deep_learning #machine_learning #optimization
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YouTube
1.1 Introduction to Optimization and to Me
These lectures are from material taught as a second graduate course in Optimization, at The University of Texas at Austin, in Spring 2020. The first half of the course was recorded in the LAITS studio as part of an online MS program in Computer Science. The…
🧠 Topic: Introduction to Causality in Machine Learning
💠 Link: https://towardsdatascience.com/introduction-to-causality-in-machine-learning-4cee9467f06f
💡 Tags: #machine_learning #causal_inference
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💠 Link: https://towardsdatascience.com/introduction-to-causality-in-machine-learning-4cee9467f06f
💡 Tags: #machine_learning #causal_inference
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Medium
Introduction to Causality in Machine Learning
Why we need causality in Machine Learning (From a business perspective)
🧠 Topic: Quick Start to Gaussian Process Regression
💠 Link: https://towardsdatascience.com/quick-start-to-gaussian-process-regression-36d838810319
💡 Tags: #machine_learning #Bayesian
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💠 Link: https://towardsdatascience.com/quick-start-to-gaussian-process-regression-36d838810319
💡 Tags: #machine_learning #Bayesian
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Medium
Quick Start to Gaussian Process Regression
A quick guide to understanding Gaussian process regression (GPR) and using scikit-learn’s GPR package
🧠 Topic: Causal Reasoning: Fundamentals and Machine Learning Applications
💠 Link: https://causalinference.gitlab.io/causal-reasoning-book-chapter1/
💡 Tags: #machine_learning #causal_inference
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💠 Link: https://causalinference.gitlab.io/causal-reasoning-book-chapter1/
💡 Tags: #machine_learning #causal_inference
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Getting Started with Causal Inference
Chapter 1: Causal Reasoning Book
Introduction
🧠 Topic: Purdue Krannert-Statistics Causal Machine Learning for Novel Settings Boot Camp
💠 Link: https://www.stat.purdue.edu/~sabbaghi/teaching/causalMLNovelSettings/Introduction%20-%20Causal%20Inference%20and%20ML.html
💡 Tags: #machine_learning #causal_inference
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💠 Link: https://www.stat.purdue.edu/~sabbaghi/teaching/causalMLNovelSettings/Introduction%20-%20Causal%20Inference%20and%20ML.html
💡 Tags: #machine_learning #causal_inference
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🧠 Topic: Log Softmax Vs Softmax
💠 Link: https://deepdatascience.wordpress.com/2020/02/27/log-softmax-vs-softmax/
💡 Tags: #deep_learning
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💠 Link: https://deepdatascience.wordpress.com/2020/02/27/log-softmax-vs-softmax/
💡 Tags: #deep_learning
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Data Science & Deep Learning
Log Softmax Vs Softmax
Question: I recently came across log softmax for loss calculation. Does it matter and Which one should I choose ?Answer: Log Softmax is advantageous over softmax for numerical stability, optimisati…