From dd6f01ea40ee189fb6d267b7211273adab2ec0c7 Mon Sep 17 00:00:00 2001 From: mani Date: Tue, 21 Jan 2020 21:41:18 +0000 Subject: [PATCH] Added more links for python, data, maths, stats, probability, etc... --- Programming-in-Python.md | 8 +++ README-details.md | 12 ++++ cloud-devops-infra/README.md | 5 ++ competitions.md | 2 + data/README.md | 9 +++ data/data-exploratory-analysis.md | 6 ++ data/feature-engineering.md | 4 +- data/feature-selection.md | 2 +- ...-analysis-interpretation-explainability.md | 14 +++++ details/java-jvm.md | 8 +++ details/julia-python-and-r.md | 27 +++++++++ details/maths-stats-probability.md | 56 +++++++++++++++---- details/misc.md | 1 - 13 files changed, 140 insertions(+), 14 deletions(-) diff --git a/Programming-in-Python.md b/Programming-in-Python.md index 468cd256..64145ae8 100644 --- a/Programming-in-Python.md +++ b/Programming-in-Python.md @@ -37,11 +37,14 @@ - [Regex resources by Chris Albon](https://chrisalbon.com/#regex) - [WTF Python repo](https://github.com/satwikkansal/wtfpython) - [Introduction to Python](https://github.com/Imperial-College-Data-Science-Society/Lecture-1-Intro-to-Python) +- [Introduction of Python Programming](https://www.linkedin.com/posts/nabihbawazir_python-self-study-activity-6605392829378367488-Q6Uz) +- [Writing your first program in Python (2019) - Brown University](https://www.linkedin.com/posts/nabihbawazir_introductions-to-python-activity-6606104345299771392-t7f1) ## Intermediate / Advanced Python - [Scientific Python](https://github.com/Imperial-College-Data-Science-Society/Lecture-2-Scientific-Python) - [Neural Networks Matrices exploration - Under the Hood Mathematical Operations](https://github.com/souravs17031999/NeuralNets-Pure-Python) +- [Understand the use of *args and **kwargs](https://morioh.com/p/252b73e0be0a?f=5c21f93bc16e2556b555ab2f&fbclid=IwAR2P_D8kr9Gf2gCjd2pf57ugkuv0qBfG0JEuAijGgl3JE2o_N1_MVk7u8CM) ## Courses @@ -81,6 +84,7 @@ See **Python: Best practices** and **Python: Testing** under [Courses](./courses * [pytype](https://github.com/google/pytype) - A static type analyzer for Python code. * [Review of Python Static Analysis Tools](https://www.codacy.com/blog/review-of-python-static-analysis-tools/) * [Python Static Analysis Tools ](https://luminousmen.com/post/python-static-analysis-tools) +* [PANDAS 👉 Reading and Writing Data 👈](https://www.linkedin.com/posts/asif-bhat_pandas-activity-6610445263372947456-oxup) * See [awesome-static-analysis for Python](https://github.com/mre/awesome-static-analysis/blob/master/README.md#python) ### Python wrappers @@ -119,6 +123,10 @@ with nothing but Python - [Security](https://anvil.works/docs/security) - [Forum](https://anvil.works/forum/) - [Assembly](https://mardix.github.io/assembly/) - A Pythonic Object-Oriented Web Framework built on Flask +- [A curated list of awesome Python frameworks, libraries, software and resources](https://github.com/vinta/awesome-python) +- [Explanation of most popular Data Science Library (in Python)](https://www.linkedin.com/posts/nabihbawazir_artificialintellegence-datascience-machinelearning-activity-6617700382200164352-RDhs) +- [50 most popular Python libraries and frameworks used in data science](https://www.linkedin.com/posts/nabihbawazir_python-datascience-dataanalysis-activity-6604589510447722496-AvyE) +- [Python for 9 Purposes: The graphics miss Scikit-Learn and of course "Pandas"](https://www.linkedin.com/posts/nabihbawazir_python-for-9-purposesthe-graphics-miss-scikit-learn-activity-6605860742774259712-Was0) ## Best practices diff --git a/README-details.md b/README-details.md index 4eab639a..174e4901 100644 --- a/README-details.md +++ b/README-details.md @@ -91,6 +91,18 @@ See [Mathematics, Statistics, Probability & Probabilistic programming](./details - [Learn Data Science by bitgrit](https://github.com/bitgrit-official/learndatascience) - **[and other related topics: Stats, Visualisations, Cheatsheets, etc...](data/README.md#data)** +### Data Scientist + +- [How can I become a data scientist?](https://www.linkedin.com/posts/data-science-central_how-can-i-become-a-data-scientist-activity-6611453301030473728-0weA) +- [Being a Data Science Contractor - UK: How to find work?](https://www.linkedin.com/posts/data-science-central_being-a-data-science-contractor-uk-how-activity-6618156469516849153-2WD-) +- [How to switch career from Automation Testing to Data Science? Here is a simple guide.](https://careerhops.wixsite.com/mirrorneuron/post/automation-testing-to-data-science?fbclid=IwAR3HPbfiQ6Qmq4tJPbnI7SZA59QTuL9-IPObpgEIJ_q32O_xN9ZnNaG046M) +- [9 Mistakes to avoid when starting your career in Data Science](https://www.linkedin.com/posts/nabihbawazir_datascience-machinelearning-artificialintelligence-activity-6620904711958687744-UiIm/) +- [How can I become a data scientist?](https://www.linkedin.com/posts/data-science-central_how-can-i-become-a-data-scientist-activity-6611453301030473728-0weA) +- [8 essential tools for data scientists](https://www.linkedin.com/posts/data-science-central_8-essential-tools-for-data-scientists-activity-6623350694613184512-esn2) +- [Data Scientist is not One-Man-Army, but should know some tech concept, not mandatory to master (depend on the company), this is what I choose](https://www.linkedin.com/posts/nabihbawazir_data-scientist-is-not-one-man-army-but-should-activity-6602851212972912640-t828) +- [The Ultimate Learning Path to Become a Data Scientist and Master Machine Learning](https://www.analyticsvidhya.com/blog/2019/01/learning-path-data-scientist-machine-learning-2019/) +- [♦️MUST READ ARTICLES FOR DATA SCIENCE ENTHUSIAST♦️](https://www.linkedin.com/posts/asif-bhat_datascience-neverstoplearning-datanalytics-activity-6608609171401166848-U8Do) + ### Graphs - [A number of interesting links on Graph Networks by Yaz](https://github.com/yazdotai/graph-networks) - [Graph databases](./data/README.md#databases) diff --git a/cloud-devops-infra/README.md b/cloud-devops-infra/README.md index bc5cfd95..b85a55c1 100644 --- a/cloud-devops-infra/README.md +++ b/cloud-devops-infra/README.md @@ -85,6 +85,10 @@ - [GPU Server 1 of 2](./gpus/GPU-Server-side-1-of-2.jpg) | [GPU Server 2 of 2](./gpus/GPU-Server-side-2-of-2.jpg) | [Applications of GPU servers](./gpus/Applications-of-GPU-Server.jpg) - [checkout the manufacturers](http://manli.com/en/) - [Embedded Vision Solutions for NVIDIA Jetson Series](https://www.avermedia.com/professional/category/nvidia_jetson_solutions) | [Embedded Vision Family Brochure](http://storage.avermedia.com/web_release_www/Solutions/Embedded_Vision_Solutions_brochure_20190429.pdf) - Avermedia Box PC & Carrier (works with NVIDIA Jetson): [1](./gpus/Avermedia-Box-PC-and-Carrier-1-of-2-works-with-NVidia-Jetson.jpg) | [2](./gpus/Avermedia-Box-PC-and-Carrier-2-of-2-works-with-NVidia-Jetson.jpg) + - [Accelerating Wide & Deep Recommender Inference on GPUs](https://www.linkedin.com/posts/miguelusque_accelerating-wide-deep-recommender-inference-activity-6614061742936870913-oG2v) + - [Create GPU Arrays and Move to DL Frameworks with DLPack](https://www.linkedin.com/posts/activity-6625024900585316352-PucI) + - [GPU Accelerated data viz tools](https://www.linkedin.com/posts/murraydata_data-todashboard-activity-6623659330199781376-YIUQ) + - [This tool is nice to monitor not only RAPIDS but also deep learning workloads](https://www.linkedin.com/posts/miguelusque_gpu-dashboards-in-jupyter-lab-activity-6611570222585401344-n1Qe) - See [NVIDIA's RAPIDS](./gpus/rapids.md) ## TPU @@ -112,6 +116,7 @@ ## IPU - [GraphCore](http://graphcore.ai) | Videos: [Simon Knowles - More complex models and more powerful machines](https://www.youtube.com/watch?v=dLvkF_TmyAc&feature=youtu.be) | [Graphcore tech Concept](https://youtu.be/cSXbhEsUUGo?t=916) | [A new kind of hardware designed for machine intelligence - GraphCore Presentations](http://www.bristol.bcs.org.uk/2017/graphcore.pdf) | [V‍ID‌EO‌‍: SCA‌LING‌‍ THRO‌UG‍HP‌‍U‌T P‍R‌O‍C‍ESSO‌‍RS FO‌‍R‍ MAC‌HINE INTELLIG‌ENC‌‍E](https://www.graphcore.ai/posts/video-scaling-throughput-processors-for-machine-intelligence) + - [What makes the IPU's architecture so efficient](https://www.linkedin.com/posts/graphcore_if-youd-like-to-know-what-makes-the-ipus-activity-6617716840384778240-PUS0) ## Performance diff --git a/competitions.md b/competitions.md index 572776a4..8012ea01 100644 --- a/competitions.md +++ b/competitions.md @@ -33,6 +33,8 @@ - [KDD Data mining and Knowledge Discovery cup](http://www.kdd.org/kdd-cup) - [VizDoom AI competition](http://vizdoom.cs.put.edu.pl/competition-cig-2017) [deadlink] - [Numerai](https://numer.ai/) - data science tournaments +- [10 Data Science Competitions for you to hone your skills for 2020](https://towardsdatascience.com/10-data-science-competitions-for-you-to-hone-your-skills-for-2020-32d87ee19cc9) +- [Kaggle Kernels Guide for Beginners — Step by Step Tutorial](https://towardsdatascience.com/kaggle-kernels-for-beginners-a-step-by-step-guide-3db6b1cd7606) ## Coding challenges diff --git a/data/README.md b/data/README.md index f6afea9d..1cc06065 100644 --- a/data/README.md +++ b/data/README.md @@ -18,6 +18,7 @@ The question to ask ourselves: _Do we know our data...?_ - [Feature Selection](./README.md#feature-selection) - [Feature Engineering](./README.md#feature-engineering) - [Post model-creation analysis, ML interpretation/explainability](./README.md#post-model-creation-analysis-ml-interpretationexplainability) +- [Model deployment](./README.md#model-deployment) - [Statistics](./README.md#statistics) - [Visualisation](./README.md#visualisation) - [Common mistakes when training models (data related)](./README.md#common-mistakes-when-training-models-data-related) @@ -81,6 +82,14 @@ See [Feature engineering](./feature-engineering.md) See [Post model-creation analysis, ML interpretation/explainability](./model-analysis-interpretation-explainability.md) +## Model deployment + +- [Model Deployment Methods and Techniques - Part 1](https://lnkd.in/ghaTe_d) +- [Model Deployment Methods and Techniques - Part 2](https://lnkd.in/gk3cpzH) +- [Model Deployment Methods and Techniques - Part 3](https://lnkd.in/gV_cQJ2) +- [Model Deployment Methods and Techniques - Part 4](https://lnkd.in/g5zCV6w) +- [Model Deployment Methods and Techniques - Part 5](https://www.linkedin.com/posts/srivatsan-srinivasan-b8131b_datascience-ml-machinelearning-activity-6615447096793407488-sN1y) + ## Statistics See [Statistics.md](statistics.md#statistics) diff --git a/data/data-exploratory-analysis.md b/data/data-exploratory-analysis.md index 2206959e..78ab1e2e 100644 --- a/data/data-exploratory-analysis.md +++ b/data/data-exploratory-analysis.md @@ -31,6 +31,7 @@ aka *_Exploratory Data Analysis_* - [Associations and Correlations - The Essential Elements](https://www.linkedin.com/posts/data-science-central_associations-and-correlations-the-essential-activity-6609987641754570752-61fE) - [13 Great Articles and Tutorials about Correlation](https://www.linkedin.com/posts/data-science-central_13-great-articles-and-tutorials-about-correlation-activity-6622173938812280832-Fa4a) - [Testing for Normality using Skewness and Kurtosis](https://www.linkedin.com/posts/ashishpatel2604_artificialintelligence-deeplearning-datascience-activity-6603851612719026176-zx0u) +- [Variable Reduction: An art as well as Science](https://www.linkedin.com/posts/data-science-central_variable-reduction-an-art-as-well-as-science-activity-6607678425375342592-xrSp) ### Outliers @@ -50,6 +51,11 @@ aka *_Exploratory Data Analysis_* - [P-Value Explained in One Picture](https://www.linkedin.com/posts/data-science-central_p-value-explained-in-one-picture-activity-6617794081072443392-BG7_) - [p-value and level of significance explained](https://www.linkedin.com/posts/data-science-central_p-value-and-level-of-significance-explained-activity-6622189036842864640-lR81) +### Regression Analysis + +- [Intro to Full Regression Analysis](https://www.youtube.com/watch?v=W4w1XX4fCu0&feature=share&fbclid=IwAR0nJRz8v4MZhTa8AL-1XlIit7neb_vfD0JzdhKooLKD4pX-U9kl6bB6Hro) +- [Regression Analysis](https://bit.ly/2JzcOcb) + # Contributing Contributions are very welcome, please share back with the wider community (and get credited for it)! diff --git a/data/feature-engineering.md b/data/feature-engineering.md index a6d32157..11e0e6d9 100644 --- a/data/feature-engineering.md +++ b/data/feature-engineering.md @@ -12,9 +12,11 @@ - [Feature engineering and Dimensionality reduction](https://towardsdatascience.com/dimensionality-reduction-for-machine-learning-80a46c2ebb7e) - [Seven Techniques for Data Dimensionality Reduction](https://www.kdnuggets.com/2015/05/7-methods-data-dimensionality-reduction.html) - [Feature Engineering and Feature Selection](https://media.licdn.com/dms/document/C511FAQF45u2wk4WYKQ/feedshare-document-pdf-analyzed/0?e=1570834800&v=beta&t=lNVqtm3JJYvvPHpsl0uc6mZJjVGWgJ8Toz29tNJA4GI) [deadlink] +- [Hands-on Guide to Automated Feature Engineering - Prateek Joshi](https://www.linkedin.com/posts/vipulppatel_hands-on-guide-to-automated-feature-engineering-ugcPost-6612564773705924608-Utyb) +- [Feature Engineering and Selection](https://www.linkedin.com/posts/nabihbawazir_feature-engineering-and-selection-ugcPost-6603534412548280320-XTIX) +- [What is feature engineering and why do we need it?](https://www.linkedin.com/posts/srivatsan-srinivasan-b8131b_datascience-machinelearning-ml-activity-6623556433189363712-O7c4) - [ML topics expanded by Chris Albon](https://chrisalbon.com/#machine_learning) - look for topics: Feature Engineering • Feature Selection - # Contributing Contributions are very welcome, please share back with the wider community (and get credited for it)! diff --git a/data/feature-selection.md b/data/feature-selection.md index 26a7adfe..069e78d9 100644 --- a/data/feature-selection.md +++ b/data/feature-selection.md @@ -4,7 +4,7 @@ - Forward Feature selection: [Blog on Towards DS](https://towardsdatascience.com/feature-importance-and-forward-feature-selection-752638849962) | [Scikit learn](https://mikulskibartosz.name/forward-feature-selection-in-scikit-learn-f6476e474ddd) - [What is dimensionality reduction? What is the difference between feature selection and extraction?](https://datascience.stackexchange.com/questions/130/what-is-dimensionality-reduction-what-is-the-difference-between-feature-selecti) - [Feature Engineering and Feature Selection](https://media.licdn.com/dms/document/C511FAQF45u2wk4WYKQ/feedshare-document-pdf-analyzed/0?e=1570834800&v=beta&t=lNVqtm3JJYvvPHpsl0uc6mZJjVGWgJ8Toz29tNJA4GI) [deadlink] - +- [Feature Selection Techniques in Machine Learning with Python - Raheel Shaikh](https://www.linkedin.com/posts/vipulppatel_feature-selection-techniques-in-ml-with-python-ugcPost-6603482535081062400-3ZH9) # Contributing diff --git a/data/model-analysis-interpretation-explainability.md b/data/model-analysis-interpretation-explainability.md index 8d464faf..5a80ffb6 100644 --- a/data/model-analysis-interpretation-explainability.md +++ b/data/model-analysis-interpretation-explainability.md @@ -38,6 +38,20 @@ - [Machine Learning Project End to End with Python Code (data science focussed)](https://www.youtube.com/watch?v=ekV9QO5KHUY&list=PLcQCwsZDEzFkP9WMe6xvLrd_ZNGqoXOQY&fbclid=IwAR1z7XBl762FLyo-gVvdBDU1iCVqz89K1yfmJS1cbC4rZyEfF-jO30ZsYeY) - [Machine learning model explainability through Shapley values](https://faculty.ai/blog/machine-learning-model-explainability-through-shapley-values/) by [Christiane Ahlheim](https://www.linkedin.com/in/christiane-ahlheim-498263b2/) & [Markus Kunesch](https://www.linkedin.com/in/markus-kunesch/) - [Research on AI Safety](https://faculty.ai/research/) by [faculty.ai](https://faculty.ai) +- [Explaining the Explainer: A First Theoretical Analysis of LIME](https://www.linkedin.com/posts/montrealai_artificialintelligence-deeplearning-machinelearning-activity-6622678147433316352-iu72) +- [Explainable AI: The Royal Society](https://www.linkedin.com/posts/nabihbawazir_explainable-ai-the-royal-society-activity-6610121083649695744-SXrL) +- [Important Model Evaluation Metrics for Machine Learning Everyone should know - Tavish Srivastava](https://www.linkedin.com/posts/vipulppatel_important-model-evaluation-metrics-everyone-ugcPost-6607395953429266432-cDiV) +- [Read Pavel Pscheidl’s latest blog for a step by step on how to import, inspect, and score with MOJO models inside #H2O](https://lnkd.in/gbNtfMn) | [LinkedIn](https://www.linkedin.com/posts/pavel-pscheidl-19b15990_h2o-ai-ml-activity-6606566698516656128-Bk93) + +## Calibration + +- [Probability Calibration by Cambridge Spark](https://blog.cambridgespark.com/probability-calibration-c7252ac123f) +- [We can build ensemble Models with Xgboosted trees. Individual trees are stacked for better predictions but they are prone to overfitting. These trees are difficult to interprete and may be inexplicable - Kevin Lemagnen](https://github.com/klemag/odsc2018-ensemble-demystified) + +## Overfitting + +- [A Quick Refresher on Overfitting and its removal Techniques](https://www.linkedin.com/posts/ashutoshtripathi1_what-is-overfitting-and-its-removal-techniques-ugcPost-6605478376671150080-fsVI) +- [We can build ensemble Models with Xgboosted trees. Individual trees are stacked for better predictions but they are prone to overfitting. These trees are difficult to interprete and may be inexplicable - Kevin Lemagnen](https://github.com/klemag/odsc2018-ensemble-demystified) # Contributing diff --git a/details/java-jvm.md b/details/java-jvm.md index 1a7be14a..eaeec353 100644 --- a/details/java-jvm.md +++ b/details/java-jvm.md @@ -131,6 +131,14 @@ MLPMNist using DL4J: [![MLPMNist using DL4J](https://img.shields.io/docker/pulls - See [Cloud/DevOps/Infra > Performance](../cloud-devops-infra/README.md#performance) - to find various ML performance benchmarking suites - Also see [Post model-creation analysis, ML interpretation/explainability](../data/README.md#post-model-creation-analysis-ml-interpretationexplainability) +### Supervised + + - [⭐ CHEAT SHEET : Supervised and Unsupervised Learning ⭐](https://www.linkedin.com/posts/asif-bhat_cheat-sheet-supervised-and-unsupervised-ugcPost-6606216862718099457-3VrA) + +### Unsupervised + + - [⭐ CHEAT SHEET : Supervised and Unsupervised Learning ⭐](https://www.linkedin.com/posts/asif-bhat_cheat-sheet-supervised-and-unsupervised-ugcPost-6606216862718099457-3VrA) + ### Deep learning - [Deep Learning Theory](https://github.com/virgili0/Virgilio/blob/master/serving/purgatorio/select-and-train-machine-learning-models/deep-learning-theory/deep-learning-theory.md) diff --git a/details/julia-python-and-r.md b/details/julia-python-and-r.md index 335a51f5..8ee8c17c 100644 --- a/details/julia-python-and-r.md +++ b/details/julia-python-and-r.md @@ -36,6 +36,31 @@ - Part B: https://www.youtube.com/watch?v=j4IgXflsZtg&list=PLcQCwsZDEzFkQj3tOV2NDrjJ43iuNY5yC&index=9 - Part C: https://www.youtube.com/watch?v=kHZmFVDm0QQ&list=PLcQCwsZDEzFkQj3tOV2NDrjJ43iuNY5yC&index=10 + ## Supervised + + - [A Semi-Supervised Classification Algorithm using Markov Chain and Random Walk in R](https://www.linkedin.com/posts/data-science-central_a-semi-supervised-classification-algorithm-activity-6614306095047462912-7rjG) + - [⭐ CHEAT SHEET : Supervised and Unsupervised Learning ⭐](https://www.linkedin.com/posts/asif-bhat_cheat-sheet-supervised-and-unsupervised-ugcPost-6606216862718099457-3VrA) + + ## Unsupervised + + - [⭐ CHEAT SHEET : Supervised and Unsupervised Learning ⭐](https://www.linkedin.com/posts/asif-bhat_cheat-sheet-supervised-and-unsupervised-ugcPost-6606216862718099457-3VrA) + - [Have You Heard About Unsupervised Decision Trees](https://www.linkedin.com/posts/data-science-central_have-you-heard-about-unsupervised-decision-activity-6612027078051196928-vOj0) + - [Detecting Money Laundering with Unsupervised ML](https://www.linkedin.com/posts/data-science-central_detecting-money-laundering-with-unsupervised-activity-6624105668414619648-EMNy) + - [k-nearest neighbor algorithm using Python](https://www.linkedin.com/posts/data-science-central_k-nearest-neighbor-algorithm-using-python-activity-6606937543143415808-zUvO) + - [KMeans and Elbow discussion](https://www.facebook.com/groups/DataScienceWithPython/permalink/754348561720027/) + - [Clustering with non numeric data](https://www.linkedin.com/posts/data-science-central_clustering-with-non-numeric-data-activity-6607783116335534080-aWRV) + - [Have u ever heard about Bounded Clustering?](https://www.linkedin.com/posts/ashishpatel2604_bounded-clustering-activity-6604231470691217408-Fhyn) + + ## Active Learning + + - [ViewAL: Active Learning with Viewpoint Entropy for Semantic Segmentation](https://www.linkedin.com/posts/hamed-zitoun-54428658_machinelearning-deeplearning-datascience-activity-6606497055265431552-Grcr) + + ## Neural Networks + + - [How To Train Interpretable Neural Networks That Accurately Extrapolate From Small Data By Christopher Rackauckas](https://lnkd.in/e7hsfBc) +| [LinkedIn](https://www.linkedin.com/posts/montrealai_artificialintelligence-machinelearning-neuralnetworks-activity-6623714880987951105-VYU9) + - [How to generate neural network confidence intervals with Keras](https://www.linkedin.com/posts/kranthi-kumar9_how-to-generate-neural-network-confidence-activity-6606228692798672896-ClI9) + ## Generative Adversarial Network (GAN) - [A Beginner's Guide to Generative Adversarial Networks (GANs)](https://skymind.ai/wiki/generative-adversarial-network-gan) @@ -118,6 +143,7 @@ - [Top ML repos](https://github.com/yazdotai/top-machine-learning) - [Hands on ML](https://github.com/ageron/handson-ml) - [The "Python Machine Learning (1st edition)" book code repository and info resource](https://github.com/rasbt/python-machine-learning-book) + - [𝗣𝘆𝘁𝗵𝗼𝗻 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀](https://www.linkedin.com/posts/martinroberts_python-machine-learning-projects-activity-6620692910499295232-B8Gq) - [ML for Software Engineers](https://github.com/ZuzooVn/machine-learning-for-software-engineers) - [PredictionIO, a machine learning server for developers and ML engineers. Built on Apache Spark, HBase and Spray](https://github.com/apache/predictionio) - [Dive into Machine Learning with Python Jupyter notebook and scikit-learn!](https://github.com/hangtwenty/dive-into-machine-learning) @@ -172,6 +198,7 @@ - [Starting deep learning hands-on: image classification on CIFAR-10](https://blog.deepsense.ai/deep-learning-hands-on-image-classification/) - [Checkout 'Deep Learning with Tensorflow 2.0 (MNIST)' Notebooks](../notebooks/README.md#notebooks) - [Deep learning for 3D printing manufacturing](https://www.youtube.com/watch?v=jAQSM2dhDV4) by [Benjamin Schrauwen](https://www.linkedin.com/in/benjaminschrauwen) + - [Mathematical Understanding of CNN: course notes of Andrew Ng](https://www.linkedin.com/posts/ashishpatel2604_amazing-cnn-notes-ugcPost-6602853333562687488-PG8e) - [DL topics expanded by Chris Albon](https://chrisalbon.com/#deep_learning) - topics covered: Keras - See [Deep Learning](../courses.md#deep-learning) in [Courses](../courses.md#courses) diff --git a/details/maths-stats-probability.md b/details/maths-stats-probability.md index 9cf9fde6..efd98735 100644 --- a/details/maths-stats-probability.md +++ b/details/maths-stats-probability.md @@ -7,6 +7,13 @@ - [Topic-wise notes: maths & stats](https://www.ctanujit.org/lecture-notes.html) - [Number Representation Systems Explained in One Picture](https://www.datasciencecentral.com/profiles/blogs/number-representation-systems-explained-in-one-picture) - Abductive Learning: Towards Bridging Machine Learning and Logical Reasoning: [Slides](http://daiwz.net/org/slides/ABL-meetup.html) | [Video](https://www.youtube.com/watch?v=ETHrFxiFIUM) | [GitHub](https://github.com/AbductiveLearning/ABL-HED) +- [A Simple Introduction to Complex Stochastic Processes](https://www.linkedin.com/posts/data-science-central_a-simple-introduction-to-complex-stochastic-activity-6615015773003935744-wfUL) +- [Quick math references](https://www.linkedin.com/posts/asif-bhat_mathematics-quick-reference-activity-6621165265302458368-XRFB) +- [Mathematics for Machine Learning](https://lnkd.in/edgvceK) +- [Patrick Landreman: A Crash Course in Applied Linear Algebra | PyData New York 2019](https://www.youtube.com/watch?v=wkxgZirbCr4) +- [👏Linear Algebra👏 by Jim Hefferon](https://www.linkedin.com/posts/asif-bhat_linear-algebra-activity-6621491653905608704-8gkg) +- [#Tensor #Calculus for Deep learning which is used in Google #Tensorflow. Designed by Prof. Dr. Cornelis P. Dullemond](https://www.linkedin.com/posts/ashishpatel2604_tensor-calculus-for-deep-learning-activity-6602889964453756928-Y7Nk) +- [Mathematical Understanding of CNN: course notes of Andrew Ng](https://www.linkedin.com/posts/ashishpatel2604_amazing-cnn-notes-ugcPost-6602853333562687488-PG8e) ## Statistics @@ -26,37 +33,64 @@ - [Learning from Data: the art of statistics](http://www.lse.ac.uk/Events/2019/03/20190327t1830vHKT/Learning-from-Data) | [The Art of Statistics: Learning from Data by David Spiegelhalter](https://www.amazon.com/Art-Statistics-Learning-Pelican-Books-ebook/dp/B07HQDJD99) - [Statistical Rethinking](https://issuu.com/biwugrok17/docs/pdf_download_online_pdf_statistical) [deadlink] - Abductive Learning: Towards Bridging Machine Learning and Logical Reasoning: [Slides](http://daiwz.net/org/slides/ABL-meetup.html) | [Video](https://www.youtube.com/watch?v=ETHrFxiFIUM) | [GitHub](https://github.com/AbductiveLearning/ABL-HED) +- [32 Type of Statisical Distribution, by Rasmus Baath](https://www.linkedin.com/posts/nabihbawazir_32-type-of-statisical-distribution-by-rasmus-activity-6610812725444612098-l1n-) +- [Book: Statistics for Non-Statisticians](https://www.linkedin.com/posts/data-science-central_book-statistics-for-non-statisticians-activity-6610350028454141952-gJ_l) +- [👉 Statistics Quick Reference 👈](https://www.linkedin.com/posts/asif-bhat_statistics-activity-6620801636182917120-Z32y) +- [A Semi-Supervised Classification Algorithm using Markov Chain and Random Walk in R](https://www.linkedin.com/posts/data-science-central_a-semi-supervised-classification-algorithm-activity-6614306095047462912-7rjG) +- [Interesting Problem: Self-correcting Random Walks](https://www.linkedin.com/posts/data-science-central_interesting-problem-self-correcting-random-activity-6622308830137114624-9D2L) +- 24 Uses of Statistical Modeling: [Part I](https://www.linkedin.com/posts/data-science-central_24-uses-of-statistical-modeling-part-i-activity-6616738123302924288-fPqG) | [Part II](https://www.linkedin.com/posts/data-science-central_24-uses-of-statistical-modeling-part-ii-activity-6606560053312970752-6X1H) +- [Encyclopedia of Statistics by Data Science Central](https://www.linkedin.com/posts/ashishpatel2604_encyclopediastatistics-activity-6606068070370902016-TA04) +- [Statistical Inquiry Cycle](https://www.linkedin.com/posts/nabihbawazir_datascience-machinelearning-artificialintelligence-activity-6624989612928536576-Z7NE) +- [Your Guide to Master Hypothesis Testing in Statistics](https://www.linkedin.com/posts/data-science-central_your-guide-to-master-hypothesis-testing-in-activity-6624332159144509441-HVq_) +- [🎯 Most #important #statistics concept for a #Datascientist in one #image and its an extension](https://www.linkedin.com/posts/ashishpatel2604_important-statistics-datascientist-activity-6625408616923004929-TC4A) +- [Basic Understanding 🤔 of Statistics🕸️ Notes 📔 Best Statistics courses on Internet](https://www.linkedin.com/posts/ashishpatel2604_basics-of-statistic-by-udacity-ugcPost-6603602906786693120-VLBR) +- [Statistics Cheatsheet](https://www.linkedin.com/posts/nabihbawazir_statistics-cheatsheet-activity-6605755821471166464--U80) +- [Didn't Learn Statistics Yet?](https://www.linkedin.com/posts/iamsivab_42-open-problems-in-mathematics-ugcPost-6604724523625472000-TieN) +- [7 Traps to Avoid Being Fooled by Statistical Randomness](https://www.linkedin.com/posts/data-science-central_7-traps-to-avoid-being-fooled-by-statistical-activity-6607693525188427777-ZEsL) - [Statistics by Chris Albon](https://chrisalbon.com/#statistics) - covering Frequentist topics - [See Data > Statistics section more related links](../data/README.md#statistics) ## Probability and Probabilistic programming + +- [Probabilistic Symmetry and Invariant Neural Networks](https://www.youtube.com/watch?v=u8Jt1HkWTn4) by [Benjamin Bloem-Reddy](https://www.stat.ubc.ca/~benbr/) +- [Practical Probabilistic Programming book (pdf)](http://www.unquotebooks.com/download/practical-probabilistic-programming/) +- [Suite of probabilitic programming language repos from Improbable.io](https://github.com/improbable-research) +- [Chris Fonnesbeck’s presentation: PyMC's Big Adventure - Lessons Learned from the Development of Open-source Software for Probabilistic Programming](https://gitpitch.com/fonnesbeck/neurips_2018_talk#/) | [Chris Fonnesbeck](https://twitter.com/fonnesbeck) +- [Amortized Monte Carlo Integration](https://www.youtube.com/watch?v=-oHCqLFLTAI) by [Tom Rainforth](http://www.robots.ox.ac.uk/~twgr/) +- Books + - [Think Stats, 2nd edition](https://greenteapress.com/wp/think-stats-2e/) | [github](https://github.com/AllenDowney/ThinkStats2) - is an introduction to Probability and Statistics for Python programmers +- [Learning & Reasoning in Artificial Intelligence](https://www.youtube.com/watch?v=K_GOHepjY2o) by [Thomas Lukasiewicz](http://www.cs.ox.ac.uk/thomas.lukasiewicz/) +- Abductive Learning: Towards Bridging Machine Learning and Logical Reasoning: [Slides](http://daiwz.net/org/slides/ABL-meetup.html) | [Video](https://www.youtube.com/watch?v=ETHrFxiFIUM) | [GitHub](https://github.com/AbductiveLearning/ABL-HED) +- [Random Number Generation and Sampling Methods](https://www.codeproject.com/Articles/1190459/Random-Number-Generation-and-Sampling-Methods#Weighted_Choice) +- [👉 Introduction to #probability 👈](https://www.linkedin.com/posts/asif-bhat_introduction-to-probability-activity-6612508393510932480-FXN4) +- [#Probability #understanding in one #image](https://www.linkedin.com/posts/ashishpatel2604_probability-understanding-image-activity-6625279322162855936-mrHo) +- [Understanding the applications of Probability in Machine Learning](https://www.linkedin.com/posts/data-science-central_understanding-the-applications-of-probability-activity-6607350877571338241-t30D) +- [How to approach Hypothesis Testing](https://medium.com/@dhruvaggarwal6/how-to-approach-hypothesis-testing-6257d03bcfee) +- [Data Science](../courses.md#data-science) in [Courses](../courses.md#courses) + +### Bayesian - [Bayesian active learning with Gaussian processes](https://bitbucket.org/JohnReid/2019-bayesian-mixer/raw/5439d0bf0be2d01dc2d95ab89407211a875021ae/Bayesian-Mixer.pdf) | [source code](https://github.com/JohnReid/dynlearn) | [John Reid](http://johnreid.github.io/) - [Probabilistic Programming & Bayesian Methods for Hackers - Cam Davidson-Pilon](http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/) -- [Probabilistic Symmetry and Invariant Neural Networks](https://www.youtube.com/watch?v=u8Jt1HkWTn4) by [Benjamin Bloem-Reddy](https://www.stat.ubc.ca/~benbr/) - [Bayesian nonparametric ML through randomized loss functions & posterior bootstraps](https://www.youtube.com/watch?v=y_gI9R4Oe0g) by [Chris Holmes](http://www.stats.ox.ac.uk/~cholmes/) - [A visual guide to Bayesian thinking](https://www.youtube.com/watch?v=BrK7X_XlGB8) -- [Practical Probabilistic Programming book (pdf)](http://www.unquotebooks.com/download/practical-probabilistic-programming/) - [Visualization in Bayesian workflow](https://arxiv.org/abs/1709.01449) -- [Suite of probabilitic programming language repos from Improbable.io](https://github.com/improbable-research) -- [Chris Fonnesbeck’s presentation: PyMC's Big Adventure - Lessons Learned from the Development of Open-source Software for Probabilistic Programming](https://gitpitch.com/fonnesbeck/neurips_2018_talk#/) | [Chris Fonnesbeck](https://twitter.com/fonnesbeck) - [Skillsmatter: Precision Medicine With Mechanistic, Bayesian Models](https://skillsmatter.com/skillscasts/12129-bayesian-mixer-london-june) [deadlink] - [Colin Carroll’s presentation: Tidy and beautiful - Visualizing Bayesian models with xarray and ArviZ](https://colcarroll.github.io/arviz_pydata_nyc/#/) | [Colin Carroll](https://twitter.com/colindcarroll) -- [Amortized Monte Carlo Integration](https://www.youtube.com/watch?v=-oHCqLFLTAI) by [Tom Rainforth](http://www.robots.ox.ac.uk/~twgr/) - Books - [Bayesian Data Analysis Third Edition [Gelman]](https://statmodeling.stat.columbia.edu/wp-content/uploads/2013/08/bda3_contents.pdf) | [previous source](https://www.academia.edu/32086149/Bayesian_Data_Analysis_Third_Edition_Gelman_.pdf) [deadlink] - [Think Bayesian](http://greenteapress.com/wp/think-bayes) - - [Think Stats, 2nd edition](https://greenteapress.com/wp/think-stats-2e/) | [github](https://github.com/AllenDowney/ThinkStats2) - is an introduction to Probability and Statistics for Python programmers - [Model Based Machine Learning Book](http://www.mbmlbook.com/) - [Bayesian Analysis with Python: Introduction to statistical modeling and probabilistic programming using PyMC3 and ArviZ](https://www.amazon.com/Bayesian-Analysis-Python-Introduction-probabilistic/dp/1789341655) - [Probability Learning I : Bayes’ Theorem](https://towardsdatascience.com/probability-learning-i-bayes-theorem-708a4c02909a) - [Probability Learning II: How Bayes’ Theorem is applied in Machine Learning](https://towardsdatascience.com/probability-learning-ii-how-bayes-theorem-is-applied-in-machine-learning-bd747a960962) -- [Learning & Reasoning in Artificial Intelligence](https://www.youtube.com/watch?v=K_GOHepjY2o) by [Thomas Lukasiewicz](http://www.cs.ox.ac.uk/thomas.lukasiewicz/) -- Abductive Learning: Towards Bridging Machine Learning and Logical Reasoning: [Slides](http://daiwz.net/org/slides/ABL-meetup.html) | [Video](https://www.youtube.com/watch?v=ETHrFxiFIUM) | [GitHub](https://github.com/AbductiveLearning/ABL-HED) - [Bayesian Framework](https://ekroc.weebly.com/uploads/2/1/6/3/21633182/bayesworkshop1.pdf) - [Implementing the Bayesian Framework](https://ekroc.weebly.com/uploads/2/1/6/3/21633182/bayesworkshop2.pdf) -- [Random Number Generation and Sampling Methods](https://www.codeproject.com/Articles/1190459/Random-Number-Generation-and-Sampling-Methods#Weighted_Choice) -- [Data Science](../courses.md#data-science) in [Courses](../courses.md#courses) -- [How to approach Hypothesis Testing](https://medium.com/@dhruvaggarwal6/how-to-approach-hypothesis-testing-6257d03bcfee) +- [Bayesian Machine Learning](https://www.linkedin.com/posts/data-science-central_bayesian-machine-learning-activity-6623268367648256000-TTcf) +- [A "quick" introduction to PyMC3 and Bayesian models](https://www.linkedin.com/posts/data-science-central_a-quick-introduction-to-pymc3-and-bayesian-activity-6612010972817215488-k8p8) +- [Some Applications of Markov Chain in Python](https://www.linkedin.com/posts/data-science-central_some-applications-of-markov-chain-in-python-activity-6622278631290916864-R36q) +- [A curated list of resources dedicated to bayesian deep learning](https://www.linkedin.com/posts/data-science-central_a-curated-list-of-resources-dedicated-to-activity-6606636571997327360-szZp) +- [Analysis of Perishable Products Sales Using Bayesian Inference](https://www.linkedin.com/posts/data-science-central_analysis-of-perishable-products-sales-using-activity-6623969792770527232-o4q3) +- [Naive Bayes for Dummies; A Simple Explanation](https://www.linkedin.com/posts/data-science-central_naive-bayes-for-dummies-a-simple-explanation-activity-6605579607217364992-fYKP) # Contributing diff --git a/details/misc.md b/details/misc.md index c3f9e57d..40868aaf 100644 --- a/details/misc.md +++ b/details/misc.md @@ -2,7 +2,6 @@ - [Enterprise Data Analytics](https://www.dsta.gov.sg/docs/default-source/dsta-about/dh13201801_enterprise-data-analytics.pdf) - [Data Analytics to Support Total WSH Management](https://www.osha-singapore.com/pdf/Goh-Yang-Miang--Data-Analytics-to-Support-Total-WSH-Management.pdf) - - [The Ultimate Learning Path to Become a Data Scientist and Master Machine Learning](https://www.analyticsvidhya.com/blog/2019/01/learning-path-data-scientist-machine-learning-2019/) - [Learn Machine Learning from Top 50 Articles for the Past Year (v.2019)](https://medium.mybridge.co/learn-machine-learning-from-top-50-articles-for-the-past-year-v-2019-15842d0b82f6) - [Feature-wise Transformations](https://distill.pub/2018/feature-wise-transformations/?utm_source=mybridge&utm_medium=blog&utm_campaign=read_more) - [The 6 most useful Machine Learning projects of the past year (2018)](https://towardsdatascience.com/the-10-most-useful-machine-learning-projects-of-the-past-year-2018-5378bbd4919f)