How to Visualize Filters and Feature Maps in Convolutional Neural Networks
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@Machine_learn
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https://machinelearningmastery.com/how-to-visualize-filters-and-feature-maps-in-convolutional-neural-networks/
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@Machine_learn
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https://machinelearningmastery.com/how-to-visualize-filters-and-feature-maps-in-convolutional-neural-networks/
Introducing TensorFlow Graphics: Computer Graphics Meets Deep Learning
Github : https://github.com/tensorflow/graphics
Article: https://medium.com/tensorflow/introducing-tensorflow-graphics-computer-graphics-meets-deep-learning-c8e3877b7668
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@Machine_learn
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Github : https://github.com/tensorflow/graphics
Article: https://medium.com/tensorflow/introducing-tensorflow-graphics-computer-graphics-meets-deep-learning-c8e3877b7668
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@Machine_learn
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GitHub
GitHub - tensorflow/graphics: TensorFlow Graphics: Differentiable Graphics Layers for TensorFlow
TensorFlow Graphics: Differentiable Graphics Layers for TensorFlow - tensorflow/graphics
Announcing Open Images V5 and the ICCV 2019 Open Images Challenge
#Challenge
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@Machine_learn
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http://ai.googleblog.com/2019/05/announcing-open-images-v5-and-iccv-2019.html
#Challenge
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@Machine_learn
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http://ai.googleblog.com/2019/05/announcing-open-images-v5-and-iccv-2019.html
research.google
Announcing Open Images V5 and the ICCV 2019 Open Images Challenge
Posted by Vittorio Ferrari, Research Scientist, Machine Perception In 2016, we introduced Open Images, a collaborative release of ~9 million imag...
Computer Age Statistical Inference - Algorithms, Evidence, & Data Science
Table of Content:
Part I Classic Statistical Inference
1 Algorithms and Inference
2 Frequentist Inference
3 Bayesian Inference
4 Fisherian Inference and Maximum Likelihood Estimation
5 Parametric Models and Exponential Families
Part II Early Computer-Age Methods
6 Empirical Bayes
7 James–Stein Estimation and Ridge Regression
8 Generalized Linear Models and Regression Trees
9 Survival Analysis and the EM Algorithm
10 The Jackknife and the Bootstrap
11 Bootstrap Confidence Intervals
12 Cross-Validation and Cp Estimates of Prediction Error
13 Objective Bayes Inference and MCMC
14 Postwar Statistical Inference and Methodology
Part III Twenty-First-Century Topics
15 Large-Scale Hypothesis Testing and FDRs
16 Sparse Modeling and the Lasso
17 Random Forests and Boosting
18 Neural Networks and Deep Learning
19 Support-Vector Machines and Kernel Methods
20 Inference After Model Selection
21 Empirical Bayes Estimation
#book
@Machine_learn
Table of Content:
Part I Classic Statistical Inference
1 Algorithms and Inference
2 Frequentist Inference
3 Bayesian Inference
4 Fisherian Inference and Maximum Likelihood Estimation
5 Parametric Models and Exponential Families
Part II Early Computer-Age Methods
6 Empirical Bayes
7 James–Stein Estimation and Ridge Regression
8 Generalized Linear Models and Regression Trees
9 Survival Analysis and the EM Algorithm
10 The Jackknife and the Bootstrap
11 Bootstrap Confidence Intervals
12 Cross-Validation and Cp Estimates of Prediction Error
13 Objective Bayes Inference and MCMC
14 Postwar Statistical Inference and Methodology
Part III Twenty-First-Century Topics
15 Large-Scale Hypothesis Testing and FDRs
16 Sparse Modeling and the Lasso
17 Random Forests and Boosting
18 Neural Networks and Deep Learning
19 Support-Vector Machines and Kernel Methods
20 Inference After Model Selection
21 Empirical Bayes Estimation
#book
@Machine_learn
2_5395841253042553746.pdf
8.1 MB
Computer Age Statistical Inference - Algorithms, Evidence, & Data Science
Table of Content:
Part I Classic Statistical Inference
1 Algorithms and Inference
2 Frequentist Inference
3 Bayesian Inference
4 Fisherian Inference and Maximum Likelihood Estimation
5 Parametric Models and Exponential Families
Part II Early Computer-Age Methods
6 Empirical Bayes
7 James–Stein Estimation and Ridge Regression
8 Generalized Linear Models and Regression Trees
9 Survival Analysis and the EM Algorithm
10 The Jackknife and the Bootstrap
11 Bootstrap Confidence Intervals
12 Cross-Validation and Cp Estimates of Prediction Error
13 Objective Bayes Inference and MCMC
14 Postwar Statistical Inference and Methodology
Part III Twenty-First-Century Topics
15 Large-Scale Hypothesis Testing and FDRs
16 Sparse Modeling and the Lasso
17 Random Forests and Boosting
18 Neural Networks and Deep Learning
19 Support-Vector Machines and Kernel Methods
20 Inference After Model Selection
21 Empirical Bayes Estimation
#book
@Machine_learn
Table of Content:
Part I Classic Statistical Inference
1 Algorithms and Inference
2 Frequentist Inference
3 Bayesian Inference
4 Fisherian Inference and Maximum Likelihood Estimation
5 Parametric Models and Exponential Families
Part II Early Computer-Age Methods
6 Empirical Bayes
7 James–Stein Estimation and Ridge Regression
8 Generalized Linear Models and Regression Trees
9 Survival Analysis and the EM Algorithm
10 The Jackknife and the Bootstrap
11 Bootstrap Confidence Intervals
12 Cross-Validation and Cp Estimates of Prediction Error
13 Objective Bayes Inference and MCMC
14 Postwar Statistical Inference and Methodology
Part III Twenty-First-Century Topics
15 Large-Scale Hypothesis Testing and FDRs
16 Sparse Modeling and the Lasso
17 Random Forests and Boosting
18 Neural Networks and Deep Learning
19 Support-Vector Machines and Kernel Methods
20 Inference After Model Selection
21 Empirical Bayes Estimation
#book
@Machine_learn
I highly recommend the Cornell University's "Machine Learning for Intelligent Systems (CS4780/ CS5780)" course taught by Associate Professor Kilian Q. Weinberger.
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@Machine_learn
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Youtube Video Lectures: 👇
https://www.youtube.com/playlist?list=PLl8OlHZGYOQ7bkVbuRthEsaLr7bONzbXS
Course Lecture Notes: 👇
http://www.cs.cornell.edu/courses/cs4780/2018fa/lectures/
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@Machine_learn
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Youtube Video Lectures: 👇
https://www.youtube.com/playlist?list=PLl8OlHZGYOQ7bkVbuRthEsaLr7bONzbXS
Course Lecture Notes: 👇
http://www.cs.cornell.edu/courses/cs4780/2018fa/lectures/
YouTube
CORNELL CS4780 "Machine Learning for Intelligent Systems"
Cornell class CS4780. Written lecture notes: http://www.cs.cornell.edu/courses/cs4780/2018fa/lectures/index.html Official class webpage: http://www.cs.cornel...
Rules of Machine Learning:
Best Practices for ML Engineering by Martin Zinkevich best practices in ML from around Google
@Machine_learn
Best Practices for ML Engineering by Martin Zinkevich best practices in ML from around Google
@Machine_learn
2_5427129059001762388.pdf
449.5 KB
Rules of Machine Learning:
Best Practices for ML Engineering by Martin Zinkevich best practices in ML from around Google
@Machine_learn
Best Practices for ML Engineering by Martin Zinkevich best practices in ML from around Google
@Machine_learn
#Food recommender system based on LSTM network and cosine similarity
#Author:@Raminmousa
@Machine_learn
https://github.com/Ramin1Mousa/food-recommendation-system
#Author:@Raminmousa
@Machine_learn
https://github.com/Ramin1Mousa/food-recommendation-system
👍1
Adventures in WhatsApp DB — extracting messages from backups (with code examples)
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@Machine_learn
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https://medium.com/@1522933668924/extracting-whatsapp-messages-from-backups-with-code-examples-49186de94ab4
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@Machine_learn
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https://medium.com/@1522933668924/extracting-whatsapp-messages-from-backups-with-code-examples-49186de94ab4
Medium
Adventures in WhatsApp DB — extracting messages from backups (with code examples)
Getting your messages without giving a third party your credentials and data
#NLP 2018 Highlights
By Elvis Saravia.
Summary of all the biggest NLP stories, state-of-the-art results and new interesting research directions of the year coming from both academia and the industry
@Machine_learn
By Elvis Saravia.
Summary of all the biggest NLP stories, state-of-the-art results and new interesting research directions of the year coming from both academia and the industry
@Machine_learn
2_5291795760491266648.pdf
3 MB
#NLP 2018 Highlights
By Elvis Saravia.
Summary of all the biggest NLP stories, state-of-the-art results and new interesting research directions of the year coming from both academia and the industry
@Machine_learn
By Elvis Saravia.
Summary of all the biggest NLP stories, state-of-the-art results and new interesting research directions of the year coming from both academia and the industry
@Machine_learn