It consists of various links to help understand concepts, algorithms etc.

• FormNet: Beyond Sequential Modeling for Form-Based Document Understanding https://ai.googleblog.com/2022/04/formnet-beyond-sequential-modeling-for.html

  • paper link - https://arxiv.org/pdf/2203.08411.pdf

• A Free And Powerful Labelling Tool Every Data Scientist Should Know https://towardsdatascience.com/a-free-and-powerful-labelling-tool-every-data-scientist-should-know-ce66473c7557

• Microsoft’s releases an Extreme model Compression library. It’s all-in-one. 32x reductions in size on BERT and GPT-3

  • GitHub: https://lnkd.in/eA5c5WAx
  • Papers:
    • https://lnkd.in/eTCMZGNW
    • https://lnkd.in/ezFcGWnG

• Find statistical significance while dealing with proportions (2-sample binomial test):

  • https://select-statistics.co.uk/calculators/sample-size-calculator-two-proportions/
    Note that the above test is valid if the question you are asking have just two valid answers (e.g. yes or no)

• All about NLP (Resources) https://github.com/ivan-bilan/The-NLP-Pandect

  • https://www.youtube.com/playlist?app=desktop&list=PLAMHV77MSKJ4Z4OXqao1gRdfQK7VQYAXb

• Pytorch : a curated list of tutorials, papers, projects, communities and more relating to PyTorch

  • https://github.com/ritchieng/the-incredible-pytorch

• Lightning ⚡ fast forecasting with statistical and econometric models

  • https://github.com/Nixtla/statsforecast

• Explainability for 🤗 Transformers models in 2 lines (For HF models)

  • https://github.com/cdpierse/transformers-interpret
  • 3 ways of explaining 🤗(HF) models - https://www.linkedin.com/posts/rajistics_how-to-explain-predictions-from-transformer-activity-6965755799847530496-qPN9?utm_source=linkedin_share&utm_medium=ios_app
  • 𝐌𝐋 𝐄𝐱𝐩𝐥𝐚𝐢𝐧𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐒𝐭𝐚𝐧𝐟𝐨𝐫𝐝 𝐰𝐨𝐫𝐤𝐬𝐡𝐨𝐩 - https://www.linkedin.com/posts/rami-krispin_machinelearning-ml-deeplearning-activity-7001184169494032384-k15M?utm_source=share&utm_medium=member_ios
  • A guide for making black box model explainable - https://www.linkedin.com/posts/christoph-molnar_interpretable-machine-learning-activity-7021025855199961088-r_gs?utm_source=share&utm_medium=member_ios
  • Explainable AI in Swiggy - https://www.linkedin.com/posts/soumyajyoti-banerjee-034aa153_we-hate-black-boxespart-i-activity-7045302991452520448-OkkG?utm_source=share&utm_medium=member_desktop
  • Explainerdashboard is a Python library that helps make machine learning models more understandable - https://www.linkedin.com/posts/smritimishra_datascience-machinelearning-artificialintelligence-activity-7065680064155140096-Tz2s?utm_source=share&utm_medium=member_ios

• Learn how to explain any black-box model to non-technical people with Bex T's post.

  • https://www.linkedin.com/posts/towards-data-science_a-complete-shap-tutorial-how-to-explain-activity-6988524164403908608-8n2l?utm_source=share&utm_medium=member_ios

• SHAP (SHapley Additive exPlanations)

  • https://www.linkedin.com/posts/damienbenveniste_machinelearning-datascience-artificialintelligence-activity-7037457215653388288-Dh4h?utm_source=share&utm_medium=member_ios

• LIME (Local Interpretable Model-agnostic Explanations)

  • https://www.linkedin.com/posts/damienbenveniste_machinelearning-datascience-artificialintelligence-activity-7037101999552684032-KZcn?utm_source=share&utm_medium=member_ios

• A python library for producing fanciful test data

  • https://github.com/cube-drone/silly

• Awesome relation extraction : https://www.linkedin.com/posts/daniel-vila-suero-484b6b45_github-roomyleeawesome-relation-extraction-activity-6975030618577326080-FoET?utm_source=share&utm_medium=member_ios

• Focal loss for imbalance data

  • https://www.linkedin.com/posts/asvieira_why-do-tree-based-models-still-outperform-activity-6974682973765812224-stCq?utm_source=share&utm_medium=member_ios

• BART PAPER explained

  • https://www.linkedin.com/posts/prakhar21_bart-denoising-sequence-to-sequence-pre-training-activity-6976511079942139904-dhFd?utm_source=share&utm_medium=member_ios
  • Watch paper summary at https://www.youtube.com/watch?v=BGWpNQHIcs4

• SetFit paper summary

  • https://m.youtube.com/watch?v=6WBK7XSXJM8
  • https://www.youtube.com/watch?v=8h27lV8v8BU (Efficient Few-Shot Learning with Sentence Transformers and different similar model comparison)
  • SetFit few shot classification: https://www.philschmid.de/getting-started-setfit
  • Compress models with knowledge distillation
    https://www.linkedin.com/posts/huggingface_join-researchers-from-hugging-face-and-intel-activity-7005912068369301504-0IRp?utm_source=share&utm_medium=member_ios

• Multi-headed attention

  • https://www.linkedin.com/posts/sudalairajkumar_deeplearning-activity-6977875090705133568-VCFl?utm_source=share&utm_medium=member_ios

• Transforms PDF, documents and images into enriched structured data

  • https://github.com/axa-group/Parsr

• Model calibration

  • https://www.linkedin.com/posts/prithivirajdamodaran_model-calibration-activity-6976362248097128448-1Vjo?utm_source=share&utm_medium=member_ios

• Python Fire is a library for automatically generating command line interfaces (CLIs) from absolutely any Python object

  • https://github.com/google/python-fire

• HF zero shot learning

  • https://m.youtube.com/watch?v=PH_eb1udpew&feature=youtu.be
  • https://github.com/huggingface/setfit
  • https://www.linkedin.com/posts/prithivirajdamodaran_setfit-now-anyone-can-build-a-few-shot-classifier-activity-6980746111414067200-30rZ?utm_source=share&utm_medium=member_ios

• Zero shot IBM

  • https://ibm.github.io/zshot/

• Timeseries forecasting library - sktime!

  • https://www.linkedin.com/posts/dipanzan_data-machinelearning-datascience-activity-6978023559562485760-gmbr?utm_source=share&utm_medium=member_ios
  • Holy Grail of forecasting in 30 lines of code - https://www.linkedin.com/posts/activity-7033912189333733376-J3PI?utm_source=share&utm_medium=member_ios
  • tutorial for multi-variate forecasting - https://www.linkedin.com/posts/niels-rogge-a3b7a3127_multivariate-probabilistic-time-series-forecasting-activity-7041093807597056000-Yuj0?utm_source=share&utm_medium=member_ios

• Timeseries approaches

  • https://www.linkedin.com/posts/danny-butvinik_machinelearning-timeseries-activity-7020985985861586944-O9Rf?utm_source=share&utm_medium=member_ios

• Price elasticity model

  • https://www.linkedin.com/posts/dat-tran-a1602320_building-and-integration-of-a-simple-ml-price-activity-6983665162738139136-MZEK?utm_source=share&utm_medium=member_ios
  • https://medium.com/priceloop-tech-blog/building-and-integration-of-a-simple-ml-price-optimization-model-into-priceloop-nocode-3f6e444d6207

• Data science datasets

  • https://datasciencedojo.com/blog/datasets-data-science-skills/?utm_campaign=DSD%20blogs%202022&utm_content=223545408&utm_medium=social&utm_source=linkedin&hss_channel=lcp-3740012
  • 5 datasets (and starter notebooks) for image classification - https://www.linkedin.com/posts/harpreetsahota204_deeplearning-computervision-activity-7033543031341400064-qyDI?utm_source=share&utm_medium=member_ios

• ML writer ✍️

  • https://medium.com/mlearning-ai/generating-sentences-from-keywords-using-transformers-in-nlp-e89f4de5cf6b

• GPT-3 armed with a Python interpreter

  • https://www.linkedin.com/posts/eric-vyacheslav-156273169_this-is-incredible-gpt-3-armed-with-a-python-activity-7000105380659773441-3vJF?utm_source=share&utm_medium=member_ios

• Document AI

  • https://www.linkedin.com/posts/niels-rogge-a3b7a3127_ai-microsoft-artificialintelligence-activity-6986627777626054656-vEJ8?utm_source=share&utm_medium=member_ios
  • LiLT a Language independent Layout Transformer, which can be used for multilingual document processing https://www.linkedin.com/feed/update/urn:li:activity:7000873972640686081?utm_source=share&utm_medium=member_ios

• TabPFN, a new tabular data classification method (with current limitation of 1000 records)

  • https://www.linkedin.com/posts/frank-hutter-9190b24b_datascience-greenautoml-neurips-activity-6989198881657913345-ww3j?utm_source=share&utm_medium=member_ios

• Tabular Deep Learning model train 119x faster

  • https://www.linkedin.com/posts/radek-osmulski-6b935794_can-you-make-your-tabular-deep-learning-model-activity-7000268682316701696-874j?utm_source=share&utm_medium=member_ios

• An open-source, diagnostic tool for analyzing Deep Neural Networks (DNN), without needing access to training or even test data - https://www.linkedin.com/posts/prithivirajdamodaran_dont-blindly-reuse-pre-trained-models-from-activity-7082591506200457217-hxEm?utm_source=share&utm_medium=member_ios

• Graph machine learning Airbnb

  • https://www.linkedin.com/posts/juliawallin_graph-machine-learning-at-airbnb-activity-6986686098471071744-0k2x?utm_source=share&utm_medium=member_ios
  • Link Predictions = Binary Classification of node pairs
    https://www.linkedin.com/posts/philipp-brunenberg_link-predictions-with-neo4j-gds-explained-activity-6990309425802526720-I72R?utm_source=share&utm_medium=member_ios

• Python Notebook with simple question answering with Knowledge Graph and Transformer.

  • https://www.linkedin.com/posts/nikita-iserson_naturallanguageprocessing-llm-chatgpt-activity-7032269766832615424-nc9C?utm_source=share&utm_medium=member_ios

• Graphs lift Document classification from 59.1% → 81% acc (code inside) - https://www.linkedin.com/posts/prithivirajdamodaran_llms-are-so-good-because-they-are-secretly-activity-7081497074080325634-Ozi_?utm_source=share&utm_medium=member_ios

• A very interesting project from Meta Research - xFormers, a modular and field agnostic library to flexibly generate transformer architectures from interoperable and optimized building blocks

  • https://www.linkedin.com/posts/tunguz_datascience-machinelearning-artificialintelligence-activity-6990288659639930880-zfMg?utm_source=share&utm_medium=member_ios

• SPEED up Sentence Transformers upto 8x and save $ via VoltaML

  • https://www.linkedin.com/posts/prithivirajdamodaran_speed-up-sentence-transformers-upto-8x-and-activity-6991646033948639232-3qkX?utm_source=share&utm_medium=member_ios
  • https://github.com/VoltaML/voltaML

• Detecting data drift (How Amazon SageMaker can assist you in detecting data drift using a simple design?)

  • https://www.linkedin.com/posts/smritimishra_technology-dataarchitecture-datascience-activity-6992127674655617024-UwaZ?utm_source=share&utm_medium=member_ios
  • https://www.linkedin.com/posts/pranjalyadav_understanding-handling-data-drift-with-ugcPost-7001263463796813824-EbGR?utm_source=share&utm_medium=member_ios

• Production model - drifts

  • https://www.linkedin.com/feed/update/urn:li:activity:7028677829483491328?utm_source=share&utm_medium=member_ios
  • 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐌𝐨𝐝𝐞𝐥 𝐃𝐫𝐢𝐟𝐭 𝐢𝐧 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠? - https://www.linkedin.com/feed/update/urn:li:activity:7046468651683643392?utm_source=share&utm_medium=member_desktop

• Logistic regression

  • https://www.linkedin.com/posts/mdarshad_logistic-regression-ugcPost-6990974615275638784-B4h-?utm_source=share&utm_medium=member_ios

• Fraud detection using graph

  • https://www.linkedin.com/posts/activity-6992785538931957760-hPEg?utm_source=share&utm_medium=member_ios
  • https://www.linkedin.com/posts/activity-6992186970827333633-vDKh?utm_source=share&utm_medium=member_ios

• Acceleration: Planet of SPARSITY, speed up AND save training/inference $

  • https://www.linkedin.com/posts/prithivirajdamodaran_planet-of-sparsity-speed-up-and-save-training-activity-6993086747928592384-8ikS?utm_source=share&utm_medium=member_ios
  • different techniques to speed up the inference: https://www.linkedin.com/posts/sebastianraschka_i-talked-about-different-techniques-to-speed-activity-6993192240953114624-loNs?utm_source=share&utm_medium=member_ios

• MLOps resources

  • https://www.linkedin.com/posts/damienbenveniste_machinelearning-mlops-datascience-activity-6993598745867157506-90qh?utm_source=share&utm_medium=member_ios
  • MLOps engineers and full stack data scientists! - https://www.linkedin.com/posts/bechir-trabelsi-652a3914a_github-googlecloudplatformml[…]165138919424-BwNK?utm_source=share&utm_medium=member_desktop
  • Data engineering or MLOps - https://www.linkedin.com/posts/benjaminrogojan_are-you-trying-to-learn-about-data-engineering-activity-7065536405120159744-AlLj?utm_source=share&utm_medium=member_ios
  • Complete Jenkins Course - https://www.linkedin.com/posts/ann-afamefuna_like-share-devop-activity-7064181148456747009-sFtx?utm_source=share&utm_medium=member_ios
  • how to design, implement, and deploy an ML system using MLOps good practices - https://www.linkedin.com/feed/update/urn:li:activity:7059421414784720897?utm_source=share&utm_medium=member_ios
  • Top 7 MLOps GitHub Repository - https://www.linkedin.com/posts/youssef-hosni-b2960b135_top-7-mlops-github-repository-mlops-zoomcamp-activity-7089559914351661056-7cVe?utm_source=share&utm_medium=member_ios
  • MLOps 8 week course - https://www.linkedin.com/posts/deshwalmahesh_mlops-deployment-serving-activity-7091635477732061184-_tQL?utm_source=share&utm_medium=member_ios
  • Seldon-core microservice: open source, cloud agnostic, handles scaling, quick to get started, helpful documentation and active community

• Pre-training with quality data

  • https://www.linkedin.com/posts/hanspeterzorn_pretraining-language-models-quality-over-activity-6993843947735252992-thxD?utm_source=share&utm_medium=member_ios

• Fine-tuning Transformers is UNSTABLE. Why and How to fix it?

  • https://www.linkedin.com/posts/prithivirajdamodaran_artificialintelligence-machinelearning-transformers-activity-6997079480745762816-1L0U?utm_source=share&utm_medium=member_ios

• Notebooks and colab alternatives

  • https://www.linkedin.com/posts/ashishpatel2604_data-datascience-machinelearning-activity-6997071480664559616-doXm?utm_source=share&utm_medium=member_ios

• Image, text, and tabular classifications using Ludwig

  • https://www.linkedin.com/posts/dalianaliu_machinelearning-deeplearning-activity-6998737762879246336-nBSg?utm_source=share&utm_medium=member_ios

• Free access to machine learning books that give you practical knowledge

  • https://www.linkedin.com/posts/ashishpatel2604_datascientists-machinelearning-artificialintelligence-activity-6999235602076299265--Um5?utm_source=share&utm_medium=member_ios
  • 𝗖𝗵𝗲𝗰𝗸 𝗢𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗕𝗲𝘀𝘁 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗕𝗼𝗼𝗸𝘀! - https://www.linkedin.com/posts/giannis-tolios_datascience-python-machinelearning-activity-7060620878262280192-VKfq?utm_source=share&utm_medium=member_ios
  • Best YouTube Channels for Learning Data Science for Free in 2023 - https://www.linkedin.com/posts/youssef-hosni-b2960b135_best-youtube-channels-for-learning-data-science-activity-7053654804144828416-JFAn?utm_source=share&utm_medium=member_ios
  • https://www.linkedin.com/posts/rahul-bharambe-rsb-560982257_learning-science-programming-activity-7036886697313210368-CPSt?utm_source=share&utm_medium=member_ios

• PivotTableJS Python

  • https://www.linkedin.com/posts/avi-chawla_python-datascience-dataanalysis-ugcPost-7000056013911404544-PigM?utm_source=share&utm_medium=member_ios

• BERTopic to explore arxiv articles

  • https://www.linkedin.com/posts/jalammar_code-demo-of-bertopic-bertopic-for-topic-activity-7000449472182767616-TLaR?utm_source=share&utm_medium=member_ios

• Classification tool for modeling uplift - the incremental impact of a treatment

  • https://www.linkedin.com/posts/parulpandeyindia_upliftrandomforest-python-r-activity-7003351970648387584-Tsif?utm_source=share&utm_medium=member_ios

• Transformers for forecasting

  • https://www.linkedin.com/posts/activity-7004146392323526656-1fOt?utm_source=share&utm_medium=member_ios

• Why Graph Algorithms & Graph Neural Networks for RecSys

  • https://www.linkedin.com/posts/activity-7005832760263077888-VlpP?utm_source=share&utm_medium=member_ios

• Bandit-approach for our recommender system

  • https://www.linkedin.com/posts/mirza-klimenta-phd-823398249_multi-armed-bandits-in-python-epsilon-greedy-activity-7009909807558074368-5m_a?utm_source=share&utm_medium=member_ios

• Hashing function in recommender system

  • https://www.linkedin.com/posts/damienbenveniste_machinelearning-datascience-artificialintelligence-activity-7039275598988361728-M08S?utm_source=share&utm_medium=member_ios

• Ranking problem RecSys

  • https://www.linkedin.com/posts/amarchin_how-to-optimise-rankings-with-cascade-bandits-activity-7009268040764653568-ctRb?utm_source=share&utm_medium=member_ios

• Real time search or recommender system design look like?

  • https://www.linkedin.com/posts/aurimas-griciunas_mlops-machinelearning-dataengineering-activity-7009454191613984768-FvsI?utm_source=share&utm_medium=member_ios

• Finding similar users - Approximate Nearest Neighbor Search

  • https://www.linkedin.com/posts/mungoliabhishek81_datascience-machinelearning-statistics-activity-7010446713039597568-zC8W?utm_source=share&utm_medium=member_ios

• Recommender Systems

  • https://www.linkedin.com/posts/damienbenveniste_machinelearning-datascience-artificialintelligence-activity-7055200272851103744-LM5p?utm_source=share&utm_medium=member_ios
  • Recommender Systems · Evaluation · Metrics · and Loss - https://www.linkedin.com/posts/vinija_recommender-systems-evaluation-metrics-activity-7067002662285676544-aFON?utm_source=share&utm_medium=member_ios
  • Representing Users and Items in Large Language Models based Recommender Systems - https://www.linkedin.com/posts/reachsumit_representing-users-and-items-in-large-language-activity-7076565350162583553-kCm6?utm_source=share&utm_medium=member_ios

• TOP 5 Linear Regression Models

  • https://www.linkedin.com/feed/update/urn:li:activity:7010588147357036544?utm_source=share&utm_medium=member_ios

• Math for Machine Learning and Data Science

  • https://www.linkedin.com/posts/andrewyng_mathematics-for-machine-learning-and-data-activity-7008888767176474624-t002?utm_source=share&utm_medium=member_ios

• Awesome Machine Learning cheatsheets from Stanford's CS 229

  • https://www.linkedin.com/posts/stevenouri_artificialintelligence-datascience-machinelearning-activity-7009452208509304832-i013?utm_source=share&utm_medium=member_ios

• What is a Feature Store and why is it such an important element in 𝗠𝗟𝗢𝗽𝘀 𝗦𝘁𝗮𝗰𝗸?

  • https://www.linkedin.com/posts/aurimas-griciunas_mlops-machinelearning-dataengineering-activity-7011598540757966848-Ue3C?utm_source=share&utm_medium=member_ios

• Positional embedding

  • https://www.linkedin.com/posts/hanspeterzorn_positional-encoding-everything-you-need-activity-7015573995265613824-sbwT?utm_source=share&utm_medium=member_ios

• ManimML - python library to visualize the working of neural networks.

  • https://www.linkedin.com/feed/update/urn:li:activity:7017080242016014336?utm_source=share&utm_medium=member_ios

• The v0.13 release of BERTopic! 🎉

  • https://www.linkedin.com/feed/update/urn:li:activity:7016424071647121408?utm_source=share&utm_medium=member_ios

• Top2Vec

  • https://www.linkedin.com/posts/eric-vyacheslav-156273169_top2vec-is-an-awesome-algorithm-and-repo-activity-7018169591772147712-awnU?utm_source=share&utm_medium=member_ios

• Lasso & Ridge

  • https://www.linkedin.com/posts/venkat-raman-analytics_lasso-ridge-everything-you-wanted-to-activity-7018446743298088960-3TkL?utm_source=share&utm_medium=member_ios

• 5 FREE courses

  • https://www.linkedin.com/posts/danleedata_stanford-cs229-machine-learning-course-activity-7018238830587572224-y2C2?utm_source=share&utm_medium=member_ios

• List of resources (ML/DL)

  • https://www.linkedin.com/posts/thinam-tamang_machinelearning-deeplearning-mlops-activity-7017873926982701057-J_5S?utm_source=share&utm_medium=member_ios

• Research studying the effect of pruning on the generalization performance

  • https://www.linkedin.com/posts/sebastianraschka_research-deeplearningai-machinelearning-activity-7019325352917295107-PfTc?utm_source=share&utm_medium=member_ios

• Let's build GPT from scratch

  • https://www.linkedin.com/posts/sudalairajkumar_lets-build-gpt-from-scratch-in-code-spelled-activity-7021322858660335619-pFoX?utm_source=share&utm_medium=member_ios
  • How to train ChatGPT - https://www.linkedin.com/feed/update/urn:li:activity:7024476262949625857?utm_source=share&utm_medium=member_ios
  • Code Self attention from scratch - https://www.linkedin.com/posts/sebastianraschka_understanding-the-self-attention-mechanism-activity-7029471702736637952-q2nf?utm_source=share&utm_medium=member_ios
  • Create your own chatbot using the OpenAI library - https://www.linkedin.com/feed/update/urn:li:activity:7034529832700899328?utm_source=share&utm_medium=member_ios
  • An open source implementation for LLaMA based ChatGPT training process using RHLF - https://www.linkedin.com/posts/sudalairajkumar_llm-foundationalmodels-chatgpt-activity-7036576670476099584-LcVI?utm_source=share&utm_medium=member_ios
  • :robot_face: Large Language Model (LLM) Primers | ChatGPT, Prompt Engineering, RLHF - https://www.linkedin.com/posts/amanc_artificialintelligence-machinelearning-ai-activity-7083312744220803072-yjFB?utm_source=share&utm_medium=member_ios

• Why does Deep Learning fail on tabular data?

  • https://www.linkedin.com/posts/dusan-stamenkovic-457074179_datascience-data-neuralnetworks-activity-7021473058628452352-ayFJ?utm_source=share&utm_medium=member_ios

• System design goldmine

  • https://www.linkedin.com/posts/madhavvarjuntthukral-b41564183_systemdesign-design-hld-activity-7022468592549904384-uYfQ?utm_source=share&utm_medium=member_ios

• [Python] 30 Libraries to Boost Your Data Science Productivity 🚀

  • https://www.linkedin.com/posts/avi-chawla_boost-your-data-science-productivity-activity-7020700472416026624-_PHM?utm_source=share&utm_medium=member_ios
  • Python and its versatility (Please find the full list in the comments) - https://www.linkedin.com/posts/avi-chawla_i-reviewed-1000-python-libraries-and-discovered-activity-7026499417461469185-JhVs?utm_source=share&utm_medium=member_ios
  • One of the Best Python Notes For Interview Preparation by University of Idaho - https://www.linkedin.com/posts/ashishpatel2604_python-notes-ugcPost-7027944399510605824-yZLF?utm_source=share&utm_medium=member_ios
  • Sourcery, it's an automated refactoring tool that makes your code elegant, concise, and Pythonic in no time - https://www.linkedin.com/posts/avi-chawla_python-activity-7031942494841942016-yRu0?utm_source=share&utm_medium=member_ios
  • Top 25 pandas trickshttps : https://youtu.be/RlIiVeig3hc
  • Pandas AI is a Python library that integrates generative artificial intelligence capabilities into Pandas, making dataframes conversational - https://www.linkedin.com/posts/tunguz_datascience-artificialintelligence-generativeai-activity-7061317742380945408-GBih?utm_source=share&utm_medium=member_ios
  • Python Code Optimization - https://www.kaggle.com/code/youssef19/python-code-optimization-for-data-scientists/notebook
  • Jupyter notebook data frame - https://www.linkedin.com/posts/avi-chawla_pandas-datascience-activity-7070701001980223488-t62x?utm_source=share&utm_medium=member_ios
  • leverage interactive controls using IPywidgets - https://www.linkedin.com/feed/update/urn:li:activity:7078795858707673088?utm_source=share&utm_medium=member_ios
  • Drawdata is an open-source library that allows you to draw a 2D dataset in a notebook - https://www.linkedin.com/feed/update/urn:li:activity:7076993142960386049?utm_source=share&utm_medium=member_ios
  • 🚀 Top 7 GitHub repositories that will help you Master Python 🐍 - https://www.linkedin.com/posts/ginacostag_data-python-artificialintelligence-activity-7037053521413689347-obRZ?utm_source=share&utm_medium=member_ios

• Upskill yourself in 2023 with these high-quality FREE courses!

  • https://www.linkedin.com/posts/stevenouri_artificialintelligence-deeplearning-datasci[…]700518588416-KA2E?utm_source=share&utm_medium=member_desktop
  • Top Free Courses of Large Language Models by World Biggest University - https://www.linkedin.com/posts/ashishpatel2604_largelanguagemodels-artificialintelligence-activity-7054669918599790592-rZ42?utm_source=share&utm_medium=member_ios
  • Curated list of top-tier AI courses - https://www.linkedin.com/posts/sebastianraschka_ai-llm-deeplearning-activity-7066750909925511168-scw2?utm_source=share&utm_medium=member_ios
  • Top 5 courses - https://www.linkedin.com/posts/rahulagwl_youtube-activity-7090533831664885760-wIY9?utm_source=share&utm_medium=member_ios

• Microsoft recommender using transformer

  • https://github.com/microsoft/recommenders

• Learn to DEPLOY your Machine Learning models!

  • https://www.linkedin.com/posts/damienbenveniste_machinelearning-datascience-artificialintelligence-activity-7026228560910573569-70AS?utm_source=share&utm_medium=member_ios

• Prompt Engineering

  • ChatGPT Prompt Engineering for Developers Andrew NG - https://www.linkedin.com/posts/andrewyng_thrilled-to-announce-our-new-course-chatgpt-ugcPost-7057374304153333760-su5F?utm_source=share&utm_medium=member_ios
  • https://www.linkedin.com/posts/sudalairajkumar_promptengineering-activity-7029420526498447360-rQ3q?utm_source=share&utm_medium=member_ios
  • Overview - https://www.linkedin.com/posts/omarsar_machinelearning-deeplearning-ai-activity-7033122973544763392-D1dK?utm_source=share&utm_medium=member_ios
  • Blind prompting =:woman_shrugging: Prompt engineering =👩‍🔬 If you plan to use LLMs in production, this is a must-read:interrobang: :rocket: https://www.linkedin.com/posts/philipp-schmid-a6a2bb196_blind-prompting-prompt-engineering-activity-7055942829604270081-stj3?utm_source=share&utm_medium=member_ios

• Promptify, a library that allows for the use of #LLMs to solve #NLP problems, including #NamedEntity Recognition, #Binary Classification, #MultiLabel Classification, and Question-Answering and return a python object

  • https://www.linkedin.com/posts/aadityaura_prompt-gpt-opensource-activity-7024485145256591360-etIo?utm_source=share&utm_medium=member_ios
  • NER - https://www.linkedin.com/posts/praveenr2998_named-entity-recognitionner-using-chatgpt-activity-7031614774664724480-e4Bd?utm_source=share&utm_medium=member_ios

• A/B testing

  • https://www.linkedin.com/posts/danleedata_ab-testing-in-data-science-interviews-by-activity-7027674504340373504-Y6Hz?utm_source=share&utm_medium=member_ios
  • leAB library AB test - https://www.linkedin.com/posts/khuyen-tran-1401_python-testing-abtesting-activity-7075484308370591744-LA3p?utm_source=share&utm_medium=member_ios

• SHAP-hypetune is a python package for simultaneous Hyperparameters Tuning and Features Selection for Gradient Boosting Models!

  • https://www.linkedin.com/posts/eric-vyacheslav-156273169_shap-hypetune-is-a-python-package-for-simultaneous-activity-7028014478805876736-EcHJ?utm_source=share&utm_medium=member_ios

• Machine Learning model LOSS functions

  • https://www.linkedin.com/posts/damienbenveniste_machinelearning-datascience-artificialintelligence-activity-7028762585705451520-iqtz?utm_source=share&utm_medium=member_ios

• [Data engineering] Inefficient Python programs will degrade your data pipeline performance

  • https://www.linkedin.com/posts/sarah-floris_dataengineering-dataengineer-python-activity-7033462088442253312-K8ZX?utm_source=share&utm_medium=member_ios
  • Deploying your ML model - https://www.linkedin.com/posts/khuyen-tran-1401_automate-machine-learning-deployment-with-activity-7058088609752510466-uEt_?utm_source=share&utm_medium=member_ios
  • Docker for data science - https://www.linkedin.com/feed/update/urn:li:activity:7071712311178051584?utm_source=share&utm_medium=member_ios

• Outlining techniques for improving the training performance of your PyTorch model without compromising its accuracy.

  • https://www.linkedin.com/posts/sebastianraschka_some-techniques-to-make-your-pytorch-models-activity-7034565471005802496-V9Ck?utm_source=share&utm_medium=member_ios

• Reinforcement Learning: An Introduction

  • https://www.linkedin.com/feed/update/urn:li:activity:7036534106515468289?utm_source=share&utm_medium=member_ios
  • 𝐑𝐞𝐢𝐧𝐟𝐨𝐫𝐜𝐢𝐧𝐠 𝐑𝐞𝐢𝐧𝐟𝐨𝐫𝐜𝐞𝐦𝐞𝐧𝐭 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠: 𝐓𝐨𝐩 𝟐𝟎 𝐏𝐲𝐭𝐡𝐨𝐧 𝐋𝐢𝐛𝐫𝐚𝐫𝐢𝐞𝐬 𝐭𝐨 𝐌𝐚𝐱𝐢𝐦𝐢𝐳𝐞 𝐘𝐨𝐮𝐫 𝐑𝐞𝐢𝐧𝐟𝐨𝐫𝐜𝐞𝐦𝐞𝐧𝐭 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐏𝐨𝐭𝐞𝐧𝐭𝐢𝐚𝐥 - https://www.linkedin.com/feed/update/urn:li:activity:7066679327395876864?utm_source=share&utm_medium=member_ios

• t-SNE

  • https://www.linkedin.com/posts/damienbenveniste_machinelearning-datascience-artificialintelligence-activity-7038913883998220289-tv39?utm_source=share&utm_medium=member_ios

• Awesome open data-centric AI

  • https://github.com/Renumics/awesome-open-data-centric-ai

• The Precision-Recall AUC is a much more actionable metric for highly imbalanced data

  • https://www.linkedin.com/posts/damienbenveniste_machinelearning-datascience-artificialintelligence-activity-7039638012204384256-YHSZ?utm_source=share&utm_medium=member_ios

• For generative models and active learning, Amazon Science has a fantastic video- https://youtu.be/2s_GtmofbyU

• GPT tips

  • https://www.linkedin.com/posts/stevenouri_gpt4-activity-7042775117508055040-MzmF?utm_source=share&utm_medium=member_desktop
  • GPT4 will be supercharging ChatGPT but how to leverage its full potential? - https://www.linkedin.com/posts/stevenouri_gpt4-ugcPost-7042382267515621376-nT25?utm_source=share&utm_medium=member_desktop
  • So far the best and the cheapest #chatgpt competitor local implementation, very fast output on a 2020 M1 Macbook Air - https://www.linkedin.com/posts/liuhongliang_chatgpt-activity-7042212001267269632-iaSL?utm_source=share&utm_medium=member_desktop (edited)

• Today, most Large Language Models are aligned to human expectations with Reinforcement Learning from Human Feedback (RLHF).

  • https://www.linkedin.com/posts/damienbenveniste_machinelearning-datascience-artificialintelligence-activity-7046869965580210176-hnaj?utm_source=share&utm_medium=member_ios

• FREE HuggingFace Course - (https://lnkd.in/exdqcZCE) This course is for anyone who wants to learn Natural Language Processing (NLP)

  • https://www.linkedin.com/posts/svpino_here-is-a-free-course-you-cant-miss-the-activity-7040652670092599296-cqz2?utm_source=share&utm_medium=member_ios

• 𝐌𝐚𝐬𝐭𝐞𝐫𝐢𝐧𝐠 𝐔𝐧𝐬𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐞𝐝 𝐀𝐧𝐨𝐦𝐚𝐥𝐲 𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧: 𝟐𝟎 𝐓𝐨𝐩 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬, 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧 𝐌𝐞𝐭𝐫𝐢𝐜𝐬 𝐚𝐧𝐝 𝐏𝐲𝐭𝐡𝐨𝐧 𝐋𝐢𝐛𝐫𝐚𝐫𝐢𝐞𝐬

  • https://www.linkedin.com/posts/maryammiradi_machinelearning-artificialintelligence-ai-activity-7044261452110516224-kbMf?utm_source=share&utm_medium=member_ios
  • 𝐏𝐞𝐫𝐟𝐨𝐫𝐦 𝐦𝐨𝐫𝐞 𝐫𝐨𝐛𝐮𝐬𝐭 𝐎𝐮𝐭𝐥𝐢𝐞𝐫 𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧 𝐢𝐧 𝐏𝐲𝐭𝐡𝐨𝐧 - https://www.linkedin.com/posts/banias_python-datascience-machinelearning-activity-7077314236946735104-rGlZ?utm_source=share&utm_medium=member_ios

• Missing values imputations

  • https://www.linkedin.com/feed/update/urn:li:activity:7061274161238663168?utm_source=share&utm_medium=member_ios

• MRMR (Minimum-Redundancy-Maximum-Relevance) is an efficient feature selection method that proved to work extremely well for automatic feature selection at scale.

  • https://www.linkedin.com/posts/samuelemazzanti_smazzantimrmr-activity-7045303295879356416-MdM8?utm_source=share&utm_medium=member_desktop

• Finetune large language models on single consumer GPUs

  • https://www.linkedin.com/posts/sebastianraschka_languagemodels-deeplearning-ai-activity-7046837051211583488-wkcE?utm_source=share&utm_medium=member_ios
  • Train a 7B model from scratch on a Single GPU! :exploding_head - https://www.linkedin.com/posts/sanyambhutani_we-can-now-train-a-7b-model-from-scratch-activity-7051736310427918336-2YD1?utm_source=share&utm_medium=member_ios
  • LLMs to build a simple assistant - https://www.linkedin.com/posts/eugeneyan_experimenting-with-llms-to-research-reflect-[…]836878950400-lcDT?utm_source=share&utm_medium=member_desktop
  • Truly open-source and the world's first truly open instruction-tuned LLM!- https://www.linkedin.com/posts/maziyarpanahi_say-hello-to-dolly-v2-truly-open-source-ugcPost-7052922568273465344-C5E6?utm_source=share&utm_medium=member_ios
  • Another LLM that can be used commercially - https://github.com/stability-AI/stableLM/
  • You can now train and run you own LLM locally! GPT4All-J - https://www.linkedin.com/posts/liorsinclair_you-can-now-train-and-run-you-own-llm-locally-activity-7054846251082625024-_Xt3?utm_source=share&utm_medium=member_ios
  • Try this for prototype which provides a central interface to connect your LLM's with external data - https://github.com/jerryjliu/llama_index
  • The secret recipe for making Chat based models work 👩‍🍳 - https://www.linkedin.com/posts/sanyambhutani_the-secret-recipe-for-making-chat-based-models-activity-7060254012935139328-pPfP?utm_source=share&utm_medium=member_ios
  • Outperforming LLMs with 2000x smaller models! 🚀 - https://www.linkedin.com/posts/sanyambhutani_outperforming-llms-with-2000x-smaller-models-activity-7060977553104134144-1gRH?utm_source=share&utm_medium=member_ios

• Challenges of LLM deployment on premises in seldon: (Seldon - LLMOps for Enterprise: Key Challenges when Deploying for Production)

  • https://www.seldon.io/webinar/llmops-for-enterprise-key-challenges-when-deploying-for-production-3

• Explores the exciting use cases of #LangChain and #ChatGPT

  • https://www.linkedin.com/posts/pradipnichite_building-chatbot-using-langchain-and-chatgpt-activity-7058312972267380736-r1i3?utm_source=share&utm_medium=member_ios
  • LangChain - https://www.linkedin.com/posts/sonali-pattnaik_generativeai-ai-activity-7063160223967973376-3K0P?utm_source=share&utm_medium=member_ios
  • LangChain is really changing the game when it comes to building applications using LLMs - https://www.linkedin.com/feed/update/urn:li:activity:7066486064898560000?utm_source=share&utm_medium=member_desktop
  • Free generative AI courses - https://www.linkedin.com/feed/update/urn:li:activity:7069751645164765185?utm_source=share&utm_medium=member_ios
  • A prompting tool : https://github.com/microsoft/guidance

• Sklearn Meets Large Language Models. It allows you to integrate language models like ChatGPT into scikit-learn for text analysis tasks - https://www.linkedin.com/posts/liorsinclair_just-found-out-about-scikit-llm-sklearn-activity-7066464504666013696-l-rc?utm_source=share&utm_medium=member_desktop

• Curated list of papers about large language models, especially relating to ChatGPT

  • https://github.com/Hannibal046/Awesome-LLM?s=31

• StarCoder ⭐, the biggest open-source Code-LLM :robot_face:.

  • https://www.linkedin.com/posts/philipp-schmid-a6a2bb196_bigcode-chatgpt-copilot-activity-7059941239277678592-hUmn?utm_source=share&utm_medium=member_ios

• Current open-source ChatGPT alternatives

  • https://www.linkedin.com/posts/saattrupdan_chatgpt-generativeai-opensource-activity-7055833276703219713-K8-Q?utm_source=share&utm_medium=member_ios
  • HuggingChat - https://www.linkedin.com/posts/clementdelangue_i-believe-we-need-open-source-alternatives-activity-7056722611371663362-I_Rq?utm_source=share&utm_medium=member_ios
  • 1,000+ AI tools were released in April'2023 - https://www.linkedin.com/feed/update/urn:li:activity:7058073510253858816?utm_source=share&utm_medium=member_ios

• Running GPT on your laptop is now possible thanks to GPT4All

  • https://www.linkedin.com/posts/hajar-mousannif_ml-ai-aiot-activity-7047111968347820033-P75p?utm_source=share&utm_medium=member_ios
  • 100% Private & Local PDF ChatBot (without langchain), Video talks about using falcon series models locally and query it - https://www.youtube.com/watch?v=hSQY4N1u3v0
  • LLAMA2 - The repo to try this locally, good to see it supports MAC os too (the best part you can use GPU's for the quick inferencing) - https://github.com/ggerganov/llama.cpp
  • how to fine-tune LLMs!- https://www.linkedin.com/posts/akshay-pachaar_lit-gpt-ugcPost-7090663892741144576-33Et?utm_source=share&utm_medium=member_ios
  • Fine-tune Llama-2 - https://www.linkedin.com/posts/itamar-g1_fine-tune-llama-2-with-a-few-lines-of-code-activity-7087691271275786240-Q7gS?utm_source=share&utm_medium=member_ios
  • Llama 2 - https://www.linkedin.com/posts/philipp-schmid-a6a2bb196_llama-2-every-resource-you-need-activity-7088184410990092288-nApZ?utm_source=share&utm_medium=member_ios
  • Train LLMs in just 50 lines of code! - https://m.youtube.com/watch?v=JNMVulH7fCo

• Federated Learning!

  • https://www.linkedin.com/posts/damienbenveniste_machinelearning-datascience-artificialintelligence-activity-7061728800211972096-_aIB?utm_source=share&utm_medium=member_ios

• 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐅𝐫𝐨𝐦 𝐒𝐜𝐫𝐚𝐭𝐜𝐡 𝐔𝐬𝐢𝐧𝐠 𝐍𝐮𝐦𝐏𝐲 𝐚𝐧𝐝 𝐏𝐲𝐭𝐡𝐨𝐧

  • https://www.linkedin.com/feed/update/urn:li:activity:7048317846677803009?utm_source=share&utm_medium=member_desktop
  • 🚀 Scikit-Learn Cheatsheet to Machine Learning :robot_face: - https://www.linkedin.com/posts/ginacostag_scikit-learn-cheatsheet-ugcPost-7076565233271463936-BSBE?utm_source=share&utm_medium=member_ios
  • Machine Learning advancements in the leading industry (blogs and links) - https://www.linkedin.com/posts/damienbenveniste_machinelearning-datascience-artificialintelligence-activity-7059914026977247232-v6YY?utm_source=share&utm_medium=member_ios
  • do Feature Selection automatically? Try 𝐦𝐫𝐦𝐫. 𝐦𝐫𝐦𝐫 (minimum-Redundancy-Maximum-Relevance) is a minimal-optimal feature selection algorithm at scale. - https://www.linkedin.com/posts/banias_python-datascience-machinelearning-activity-7078039042574934017-kqSd?utm_source=share&utm_medium=member_ios

• Cosine Similarity for 1 Trillion Pairs of Vectors

  • https://www.linkedin.com/posts/rragundez_cosine-similarity-for-1-trillion-pairs-of-activity-7049218915909935105-PxaS?utm_source=share&utm_medium=member_ios

• How does Uber predict ride ETAs?

  • https://www.linkedin.com/posts/damienbenveniste_machinelearning-datascience-artificia[…]143027195904-U2jR?utm_source=share&utm_medium=member_desktop

• Information Gain (IG)

  • https://www.linkedin.com/posts/danny-butvinik_artificialintelligence-machinelearning-activity-7074966254163247104--M-E?utm_source=share&utm_medium=member_ios
  • Gini index and entropy - https://www.linkedin.com/posts/danny-butvinik_artificialintelligence-patternrecognition-activity-7085122970544664576-XAtn?utm_source=share&utm_medium=member_ios

• How do you deal with imbalanced data?

  • https://www.linkedin.com/posts/damienbenveniste_machinelearning-datascience-artificia[…]098536181760-J3JK?utm_source=share&utm_medium=member_desktop

• Outliers treatment

  • https://www.linkedin.com/feed/update/urn:li:activity:7060917137711726592?utm_source=share&utm_medium=member_ios

• Hyperparameter (HP) tuning is Bayesian Optimization Hyper Band

  • https://www.linkedin.com/posts/damienbenveniste_machinelearning-datascience-artificialintelligence-activity-7056291915050143744-bAX0?utm_source=share&utm_medium=member_ios

• XGBoost

  • https://www.linkedin.com/feed/update/urn:li:activity:7054123649183727616?utm_source=share&utm_medium=member_ios

• Testing code that relies on external services, like a database

  • https://www.linkedin.com/posts/khuyen-tran-1401_python-testing-activity-7061708578537021440-8ae9?utm_source=share&utm_medium=member_ios

• Active learning: package scikit-activeml is a Python library for active learning on top of SciPy and scikit-learn

  • https://github.com/scikit-activeml/scikit-activeml

• Prediction ≠ Estimation

  • https://www.linkedin.com/posts/venkat-raman-analytics_statistics-datascience-econometrics-activity-7084057235567349761-a7_d?utm_source=share&utm_medium=member_ios

• Chebyshev distance

  • https://www.linkedin.com/posts/mehdihamedi_machinelearning-distance-chess-activity-7087454692628119555-1GZY?utm_source=share&utm_medium=member_ios

• Generalized Linear Models

  • https://www.linkedin.com/posts/arnab-biswas-67083b225_statistics-datascience-glm-activity-7087149608069455872-2kI0?utm_source=share&utm_medium=member_ios

• 5-week Software Engineering crash course curriculum designed specifically for Data Scientists and Machine Learning Engineers

  • https://www.linkedin.com/posts/jackblandin_softwareengineering-machinelearning-datascience-activity-7155189899581661184-VAJF?utm_source=share&utm_medium=member_desktop