Comprehensive Reading Resources in Generative AI, ML, NLP, and Various Domains

Aakash Goel
3 min readMar 6, 2024

I will consistently incorporate valuable reading materials for reference. Therefore, I encourage you to regularly check or stay tuned for updates.

Newsletter to get updates on AI

  1. https://aiweekly.co/
  2. The Neuron — https://www.theneurondaily.com/ by Noah Edelman & Pete Huang
  3. Superhuman — https://www.superhuman.ai/ by Zain Kahn
  4. Ben’s Bites — https://www.bensbites.co/ by Ben Tossell
  5. Last Week in AI — https://lastweekin.ai/ by Andrey Kurenkov
  6. Guide to AI — https://nathanbenaich.substack.com/ by Nathan Benaich

Use cases of Generative AI in Automotive Industry

https://aakashgoel12.medium.com/driving-innovation-the-role-of-generative-ai-in-the-automotive-sector-dc765d5a84b2

Use cases of Generative AI in Supply Chain

https://aakashgoel12.medium.com/from-algorithms-to-warehouses-how-generative-ai-is-transforming-supply-chains-27542e53adde

Generative AI — Security Concerns

1. Overview

A. https://www.cio.com/article/656917/top-overlooked-genai-security-risks-for-businesses.html

B. https://medium.com/@akitrablog/how-to-manage-generative-ai-genai-security-risks-05345cb0662f

2. Risk and Mitigation

A. 3 Biggest GenAI Threats — https://www.tanium.com/blog/the-3-biggest-genai-threats-plus-1-other-risk-and-how-to-fend-them-off/

B. https://securiti.ai/generative-ai-security/

C. Managing the risks of generative AI (Report) — https://explore.pwc.com/generativeai?_pfses=w1zdoudzc78Ycge8rXvKAdep

3. News

A. https://www.deccanherald.com/technology/one-in-four-entities-banned-genai-use-due-to-privacy-data-security-risks-study-2869126

B. https://cradlepoint.com/resources/blog/generative-ai-security-risks-and-responses-for-enterprise-it-and-networking/

C. Video — https://www.helpnetsecurity.com/2023/11/27/genai-concerned-security-leaders-video/

D. https://www.forbes.com/sites/waynerash/2024/02/07/generative-ai-exposes-users-to-new-security-risks/?sh=1d4da2942dfe

Prompt Engineering

1. https://www.analyticsvidhya.com/blog/2023/05/what-is-prompt-engineering-guide/

2. https://www.datacamp.com/blog/what-is-prompt-engineering-the-future-of-ai-communication

3. Prompt Engineering for Medical Professionals — https://www.jmir.org/2023/1/e50638/PDF

Natural Language Processing (NLP)

  1. Evolution of Word Vectors in NLP — https://www.youtube.com/watch?v=0zaXiqzmr7w
  2. How to handle OOV

A. https://blog.marketmuse.com/glossary/out-of-vocabulary-oov-definition/

B. https://ychai.uk/notes/2019/03/08/NLP/How-to-handle-Out-Of-Vocabulary-words/

LLM

  1. https://huyenchip.com/2023/04/11/llm-engineering.html

Setting up Vector DB

  1. https://github.com/openai/chatgpt-retrieval-plugin

Machine Learning (ML)

  1. https://www.analyticsvidhya.com/blog/2015/06/machine-learning-basics/
  2. ML Basic concepts PDF — https://courses.edx.org/asset-v1:ColumbiaX+CSMM.101x+1T2017+type@asset+block@AI_edx_ml_5.1intro.pdf
  3. https://knowledge.dataiku.com/latest/ml-analytics/ml-concepts/concept-machine-learning-introduction.html
  4. https://www.mygreatlearning.com/blog/what-is-machine-learning/
  5. Google’s Quick introductory course — https://developers.google.com/machine-learning/intro-to-ml
  6. XGBoost — https://medium.com/@prathameshsonawane/xgboost-how-does-this-work-e1cae7c5b6cb#:~:text=XGBoost%20has%20gained%20fame%20for,errors%20made%20by%20previous%20models. , https://www.analyticsvidhya.com/blog/2018/09/an-end-to-end-guide-to-understand-the-math-behind-xgboost/
  7. Detailed study links summary

A. https://serokell.io/blog/top-resources-to-learn-ml

B. Very structured and detailed learning links — https://medium.com/machine-learning-for-humans/how-to-learn-machine-learning-24d53bb64aa1

C. Google’s List of courses — https://developers.google.com/machine-learning

D. https://www.kdnuggets.com/2018/06/30-free-resources-machine-learning-deep-learning-nlp-ai.html

E. The famous Andrew NG course — https://www.youtube.com/playlist?list=PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU

Lasso Regression + Regularization

  1. Why Lasso led to sparsity ?https://www.analyticsvidhya.com/blog/2020/11/lasso-regression-causes-sparsity-while-ridge-regression-doesnt-unfolding-the-math/
  2. Regularization of polynomial regression — https://ardianumam.wordpress.com/2017/09/22/deriving-polynomial-regression-with-regularization-to-avoid-overfitting/
  3. Code — https://notebook.community/albahnsen/PracticalMachineLearningClass/notebooks/07-regularization

Polynomial Regression

  1. https://www.geeksforgeeks.org/python-implementation-of-polynomial-regression/
  2. https://jashrathod.github.io/2021-06-03-diving-deep-into-linear-regression-and-polynomial-regression/
  3. Code — https://github.com/KonuTech/Machine-Learning-with-Python/blob/master/ML0101EN-Reg-Polynomial-Regression-Co2-py-v1.ipynb

Data Transformation

  1. code — Data Transformation: https://www.statology.org/transform-data-in-python/

Curse of Dimensionality

1. https://www.analyticsvidhya.com/blog/2021/04/the-curse-of-dimensionality-in-machine-learning/

2. https://www.mygreatlearning.com/blog/understanding-curse-of-dimensionality/

3. Some code given — https://medium.com/analytics-vidhya/the-curse-of-dimensionality-and-its-cure-f9891ab72e5c

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