More links added under Python Performance, Cloud, Julia-Python-R, Mat…

…hs&Stats and NLP
bikashdaga · Jul 16, 2021 · 75155f9 · 75155f9
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diff --git a/Python-Performance.md b/Python-Performance.md
@@ -18,6 +18,7 @@
   - [From Python to Numpy](https://www.labri.fr/perso/nrougier/from-python-to-numpy/)
   - [How do you speed up your numerical calculations in Numpy and Pandas? Using a small library called NumExpr with symbolic expression and other cool tricks.](https://www.linkedin.com/posts/ajitjaokar_speed-up-your-numpy-and-pandas-with-numexpr-activity-6680574379069440000-kyQ3)
   - [Speeding up python code using numpy](https://www.kdnuggets.com/2019/06/speeding-up-python-code-numpy.html)
+  - [Speed Up Your #Python and #Pandas with NumExpr via T. Scott Clendaniel](https://bit.ly/3f3QdUR)
   - [Using Cython Nuitka Numba ShedSkin Pythran Transonic](https://twitter.com/ianozsvald/status/1226436048428900353)
   - [using Dask / Vaex / Modin to speed up Pandas-like operations](https://twitter.com/ianozsvald/status/1225748724363780096)
 - Pandas GroupBy speedup
@@ -88,6 +89,7 @@
   - [Making Pandas Fly (PyDataUK 2020)](https://speakerdeck.com/ianozsvald/pydatauk-making-pandas-fly) | [Blog](https://ianozsvald.com/2020/04/27/flying-pandas-and-making-pandas-fly-virtual-talks-this-weekend-on-faster-data-processing-with-pandas-modin-dask-and-vaex/)
   - [Making Pandas Fly (PyDataBudapest 2020)](https://speakerdeck.com/ianozsvald/making-pandas-fly) | [Blog](https://ianozsvald.com/2020/04/27/flying-pandas-and-making-pandas-fly-virtual-talks-this-weekend-on-faster-data-processing-with-pandas-modin-dask-and-vaex/)
   - [Flying Pandas - Dask, Modin and Vaex (Remote Pizza Python 2020)](https://speakerdeck.com/ianozsvald/flying-pandas-modin-dask-and-vaex) | [Blog](https://ianozsvald.com/2020/04/27/flying-pandas-and-making-pandas-fly-virtual-talks-this-weekend-on-faster-data-processing-with-pandas-modin-dask-and-vaex/)
+  - Process 120 million taxi trips and explore in real-time with Dash, Plotly and Vaex: [Interactive Dashboard](https://dash.vaex.io/) | [Blogpost](https://lnkd.in/gYWTtRX) | [Code](https://lnkd.in/gFJg2GE) | [Tutorial for data processing](https://lnkd.in/gF_EEeN)
   - [Tools for Higher Performance python (ODSC 2019)](https://speakerdeck.com/ianozsvald/higher-performance-python-odsc-2019) | [Blog](https://ianozsvald.com/2019/11/22/higher-performance-python-odsc-2019/)
   - [Tools for Higher Performance python (PyDataCambridge 2019)](https://speakerdeck.com/ianozsvald/higher-performance-python) | [Blog](https://ianozsvald.com/2019/11/16/higher-performance-python-at-pydatacambridge-2019/)
   - [Sprinting Pandas](https://speakerdeck.com/ianozsvald/sprinting-pandas-at-odsc-2020)

diff --git a/README-details.md b/README-details.md
@@ -33,6 +33,7 @@
 - [Demystification of the key concepts of Artificial Intelligence and Machine Learning](https://github.com/virgili0/Virgilio/blob/master/serving/paradiso/demystification-ai-ml-dl/demystification-ai-ml-dl.md)
 - [12 thought leaders on LinkedIn who are creating original content to learn Artificial Intelligence and Machine Learning](https://www.linkedin.com/posts/ajitjaokar_12-thought-leaders-on-linkedin-who-are-creating-activity-6627455043429814272-MK_n)
 - [AI Repository by Goku Mohandas](https://www.linkedin.com/posts/asif-bhat_datascience-data-dataanalysis-activity-6643083915873615872-je6g)
+- [Digital Twins: Bringing artificial intelligence to Engineering](https://www.datasciencecentral.com/profiles/blogs/digital-twins-brining-artificial-intelligence-to-engineering)
 - See [Artificial Intelligence](./details/artificial-intelligence.md)
 
 ### Automation
@@ -156,6 +157,7 @@ See [Mathematics, Statistics, Probability & Probabilistic programming](./details
   + [Towards Deeper Graph Neural Networks • Deep Adaptive Graph Neural Network (DAGNN) can be used to learn graph node representations from larger receptive fields.](https://www.linkedin.com/posts/philipvollet_datascience-machinelearning-pytorch-activity-6691433713458397186-5QLz)
   + [Graph-Powered Machine Learning • Free eBook Excerpt (Chapter: 3, 4, 7)](https://www.linkedin.com/posts/philipvollet_machinelearning-datascience-neo4j-activity-6689239970785431553-pMv3)
   + [Cytoscape interactive network visualization in Python and Dash. A graph visualization component for creating easily customizable, high-performance, interactive, and web-based networks. ](https://www.linkedin.com/posts/philipvollet_datascience-jupyter-notebook-activity-6696126245572362240-cO4v)
+  + [This dash app allows you to annotate automatically segmented brain regions](https://www.linkedin.com/posts/philipvollet_datascience-biomedical-plotly-activity-6732033689368231936-c1NJ)
   + [Notes on graph theory — Centrality measures by Anas AIT AOMAR](https://www.linkedin.com/posts/towards-data-science_notes-on-graph-theorycentrality-measures-activity-6696705294426263552-JViA)
   + [COOKIE: A Dataset for Conversational Recommendation over Knowledge Graphs in E-commerce - A new dataset for conversational recommendation over knowledge graphs in e-commerce platforms.](https://www.linkedin.com/posts/philipvollet_nlp-innovation-datascience-activity-6703571968739946496-Z0zy)
   + [@plotlygraphs We’ve explored  @OpenAI’s new #GPT3 API, and we are super impressed with its capabilities!](https://twitter.com/plotlygraphs/status/1286079929982095360)

diff --git a/cloud-devops-infra/README.md b/cloud-devops-infra/README.md
@@ -109,11 +109,9 @@ reproducible research
       - [Intel® Advisor](https://software.intel.com/content/www/us/en/develop/tools/advisor.html)
       - [Intel® Advisor Cookbook](https://www.intel.com/content/www/us/en/develop/documentation/advisor-cookbook/top.html)
     - [Intel® DevCloud for oneAPI](https://devcloud.intel.com/oneapi/)
+    - [Tuning applications for multiple architectures](https://techdecoded.intel.io/big-picture/tuning-applications-for-multiple-architectures/)
     - Also see [Intel](../courses.md#intel) in [Courses](../courses.md#courses)
     - [TVM is an open deep learning compiler stack for CPUs, GPUs, and specialized accelerators. It aims to close the gap between the productivity-focused deep learning frameworks, and the performance- or efficiency-oriented hardware backends](https://tvm.apache.org/docs/index.html)
-    - 
-
-
 
  _Thanks to the great minds on the [mechanical sympathy](https://groups.google.com/forum/#!forum/mechanical-sympathy) mailing list for their responses to my queries on CPU probing._
 
@@ -196,6 +194,7 @@ reproducible research
 ## Misc
 
 - [👉 Docker CLI & Dockerfile Cheat Sheet 👈](https://www.linkedin.com/posts/asif-bhat_docker-quick-reference-activity-6622407319550562304-xxdj)
+- [Edge projects with code from Naveen Kumar](https://www.hackster.io/naveenbskumar/projects)
 
 # Contributing
 

diff --git a/details/julia-python-and-r.md b/details/julia-python-and-r.md
@@ -37,6 +37,7 @@
      - 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
   - [Webinar: AI Analytics PART 1: Optimize End-to-End Data Science and Machine Learning Acceleration](https://event.on24.com/event/25/25/92/3/rt/1/documents/resourceList1596477265666/s_webinarslides1596477264742.pdf](https://software.intel.com/content/www/us/en/develop/documentation/get-started-with-ai-linux/top.html](https://github.com/intel/AiKit-code-samples)
+  - [Supply Chain Optimization (question)](https://www.datasciencecentral.com/forum/topics/optimization-question)
 
   ### Jupyter and notebook related
 
@@ -54,7 +55,8 @@
 
   - [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)
-
+  - [Mayank's presentation: __MLT__ Pre-NeurIPS Paper Reading Session: Semi-Supervised Learning](https://docs.google.com/presentation/d/1hj4-MQCxORoyED9oBw7uWdErnKgddKzQLQ1YrY5-8Hs/edit)
+
   ## Unsupervised
 
   - [⭐ CHEAT SHEET : Supervised and Unsupervised Learning ⭐](https://www.linkedin.com/posts/asif-bhat_cheat-sheet-supervised-and-unsupervised-ugcPost-6606216862718099457-3VrA)
@@ -92,6 +94,7 @@
   - [Spektral is a Python library for graph deep learning, based on the Keras API and TensorFlow 2](https://github.com/danielegrattarola/spektral/)
   - [PyGLN Gated Linear Network (GLN implementations for NumPy, PyTorch, TensorFlow and JAX: A new family of neural networks introduced by DeepMind](https://www.linkedin.com/posts/philipvollet_machinelearning-neuralnetwork-network-activity-6693036479427563520-AYtm)
   - [Understand the Impact of Learning Rate on Neural Network Performance](https://machinelearningmastery.com/understand-the-dynamics-of-learning-rate-on-deep-learning-neural-networks/)
+  - [Neural Networks are Function Approximation Algorithms](https://machinelearningmastery.com/neural-networks-are-function-approximators/)
 
   ## Generative Adversarial Network (GAN)
 

diff --git a/details/julia-python-and-r/reinforcement-learning.md b/details/julia-python-and-r/reinforcement-learning.md
@@ -29,6 +29,7 @@
   - [Introduction to Reinforcement Learning](https://www.linkedin.com/posts/montrealai_artificialintelligence-deeplearning-reinforcementlearning-activity-6620788745669005312--A2A)
   - [Introduction to Reinforcement Learning by David Silver](https://www.linkedin.com/posts/nabihbawazir_artificialintelligence-deeplearning-machinelearning-activity-6612596599749259264-DlSO)
   - [Unsupervised Curricula for Visual Meta-Reinforcement Learning](https://www.linkedin.com/posts/montrealai_artificialintelligence-machinelearning-reinforcementlearning-activity-6628750214540861440-jKB0)
+  - [Meta learning: How To Learn Deep Learning And Thrive In The Digital World](https://gumroad.com/l/learn_deep_learning)
   - [Reinforcement Learning](../courses.md#reinforcement-learning) in [Courses](../courses.md#courses)reinforcement-learning.md
 
 

diff --git a/details/maths-stats-probability.md b/details/maths-stats-probability.md
@@ -34,6 +34,7 @@
 - [How the Mathematics of Fractals Can Help Predict Stock Markets�Shifts�+](https://www.linkedin.com/posts/vincentg_how-the-mathematics-of-fractals-can-help-activity-6730167289112510464-H3eX)
 - [Introduction to Linear Algebra for Applied Machine Learning with Python](https://pabloinsente.github.io/intro-linear-algebra)
 - [#AI #fourier on partial differential equations and navier stokes](https://www.technologyreview.com/2020/10/30/1011435/ai-fourier-neural-network-cracks-navier-stokes-and-partial-differential-equations/?)
+- [Fourier Transforms With scipy.fft: Python Signal Processing](https://realpython.com/python-scipy-fft/)
 - [Manim is an engine for precise programatic animations, designed for creating explanatory math videos](https://github.com/3b1b/manim)
 - Hessian matrix approximation: [Khan Academy](https://www.khanacademy.org/math/multivariable-calculus/applications-of-multivariable-derivatives/quadratic-approximations/a/the-hessian) | [Uni. of Buffalo | Chapter 5 Hessian](https://cedar.buffalo.edu/~srihari/CSE574/Chap5/Chap5.4-Hessian.pdf) | [Math Lectures: Hessian Example](https://www.iith.ac.in/~ashok/Maths_Lectures/TutorialB/Hessian_Examples.pdf)
 

diff --git a/details/pytorch.md b/details/pytorch.md
@@ -75,6 +75,7 @@ On https://pytorch.org/, just under **"Quick Start Locally"** find **"C++/Java"*
 - [The Incredible Pytorch • This is a curated list of tutorials, projects, libraries, videos, papers, books and anything related to the incredible PyTorch](https://www.linkedin.com/posts/philipvollet_nlp-machinelearning-deeplearning-activity-6676333739670335489-R31y)
 - [Active Learning with PyTorch](https://medium.com/pytorch/https-medium-com-robert-munro-active-learning-with-pytorch-2f3ee8ebec)
 - Example of Retina Face: [online app](https://retinaface.herokuapp.com) | [GitHub](https://github.com/ternaus/retinaface_demo) | [Author](https://ternaus.github.io)
+- [Effective Pytorch](https://www.linkedin.com/posts/philipvollet_machinelearning-nlp-pytorch-activity-6731643907819614208-xMXu)
 
 # Contributing
 

diff --git a/natural-language-processing/course-tutorial-learning-resources.md b/natural-language-processing/course-tutorial-learning-resources.md
@@ -9,6 +9,7 @@
 - [How Rossum is using deep learning to extract data from any document](https://www.linkedin.com/posts/eric-feuilleaubois-ph-d-43ab0925_how-rossum-is-using-deep-learning-to-extract-activity-6605832802078347264-ZsW8)
 - [Everything you need to know about Named Entity Recognition!!](https://github.com/neomatrix369/awesome-ai-ml-dl/blob/master/natural-language-processing/ner.md)
 - [NLP and Python books](https://www.linkedin.com/posts/inna-vogel-nlp_100daysofnlp-activity-6685064904925310976-KU-d)
+- [Citation Needed: A Taxonomy and Algorithmic Assessment of Wikipedia's Verifiability](https://arxiv.org/abs/1902.11116)
 - See [Natural Language Processing (NLP)](../courses.md#naturallanguageprocessing-nlp) in [Courses](../courses.md#courses)
 
 # Contributing