GitHub repositories to enhance your Python proficiency:
- Web development with Django — https://github.com/django/django
- Data Science tools — https://github.com/rasbt/python-machine-learning-book
- Algorithmic challenges — https://github.com/TheAlgorithms/Python
- Machine learning recipes — https://github.com/ageron/handson-ml2
- Testing best practices — https://github.com/pytest-dev/pytest
- Automation scripts — https://github.com/soimort/you-get
- Advanced Python concepts — https://github.com/faif/python-patterns
Bookmark and share
https://t.iss.one/CodeProgrammer🌟
- Web development with Django — https://github.com/django/django
- Data Science tools — https://github.com/rasbt/python-machine-learning-book
- Algorithmic challenges — https://github.com/TheAlgorithms/Python
- Machine learning recipes — https://github.com/ageron/handson-ml2
- Testing best practices — https://github.com/pytest-dev/pytest
- Automation scripts — https://github.com/soimort/you-get
- Advanced Python concepts — https://github.com/faif/python-patterns
Bookmark and share
https://t.iss.one/CodeProgrammer
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Searched 35 free courses, so you don't have to! 🔍✨
Here are the 35 best free courses: 🎓
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4. Computer science for business professionals 💼
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5. How to conduct and write a literature review 📝
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8. Startup Success: How to launch a technology company in 6 steps 🚀
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9. Data analysis: statistical modeling and computation in applications 📊
Link: https://lnkd.in/gCeihcZN
10. The art and science of searching in systematic reviews 🔎
Link: https://lnkd.in/giFW5q4y
11. Introduction to conducting systematic review 📋
Link: https://lnkd.in/g6EEgCkW
12. Introduction to computer science and programming using python 🖥
Link: https://lnkd.in/gwhMpWck
13. Introduction to computational thinking and data science 💡
Link: https://lnkd.in/gfjuDp5y
14. Becoming an Entrepreneur 💸
Link: https://lnkd.in/gqkYmVAW
15. High-dimensional data analysis 📈
Link: https://lnkd.in/gv9RV9Zc
16. Statistics and R 📉
Link: https://lnkd.in/gUY3jd8v
17. Conduct a literature review 📚
Link: https://lnkd.in/g4au3w2j
18. Systematic Literature Review: An Introduction 🧐
Link: https://lnkd.in/gVwGAzzY
19. Introduction to systematic review and meta-analysis 🧮
Link: https://lnkd.in/gnpN9ivf
20. Creating a systematic literature review ✍️
Link: https://lnkd.in/gbevCuy6
21. Systematic reviews and meta-analysis 📊
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22. Research methodologies 🕵️♂️
Link: https://lnkd.in/gqh3VKCC
23. Quantitative and Qualitative research for beginners 📊💬
Link: https://shorturl.at/uNT58
24. Writing case studies: science of delivery 📑
Link: https://shorturl.at/ejnMY
25. research methodology: complete research project blueprint 🗺
Link: https://lnkd.in/gFU8Nbrv
26. How to write a successful research paper 📜
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27. Research proposal bootcamp: how to write a research proposal 🏃♂️
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28. Understanding technology 📱
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31. Web programming with Python and JavaScript 🌐
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32. Understanding Research methods 🔬
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33. How to write and publish a scientific paper 📢
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34. Introduction to systematic review and meta-analysis 📊
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Register for the FREE Python Demo Session!
📅 Date: 30 April 2026
⏰ Time: 7:30 PM
🔗 Zoom Link: https://us06web.zoom.us/meeting/register/HSOTmzzpTkGIGm9C9oGbaA
Everyone is welcome!
https://t.iss.one/CodeProgrammer
📅 Date: 30 April 2026
⏰ Time: 7:30 PM
🔗 Zoom Link: https://us06web.zoom.us/meeting/register/HSOTmzzpTkGIGm9C9oGbaA
Everyone is welcome!
https://t.iss.one/CodeProgrammer
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Softmax vs Hardmax by hand ✍️ ~ interactive calculator 👉 https://byhand.ai/vhUJDH
Softmax turns a set of raw scores (z) into a probability distribution (Y) over choices (a, b, c, d, e). Instead of just saying which option is best, it tells us how likely each option is to be chosen. In this example, most of the probability mass is concentrated on c, while the other options are still possible but clearly less likely. That's the point of softmax: it converts relative scores into meaningful, comparable probabilities that sum to 100%.
Think of a raffle. Hardmax is when the person who bought the most tickets always wins the prize — the top score takes it, every time. Softmax is when everyone's chance is proportional to the tickets they hold: even if I bought just one ticket, I may still get lucky. Who knows. That's the psychology of softmax.
This is how a language model chooses its next word. Each time a word appears in the training data, it earns a ticket. Hardmax would always speak the word with the most tickets — the same safe choice, over and over. Softmax gives every word a chance proportional to its tickets, so less common words can still be spoken. The word with the most tickets still has the highest chance of winning — just not 100%. That's what lets the model surprise us with its creativity (and also its hallucinations) instead of repeating itself.
https://t.iss.one/CodeProgrammer😱
Softmax turns a set of raw scores (z) into a probability distribution (Y) over choices (a, b, c, d, e). Instead of just saying which option is best, it tells us how likely each option is to be chosen. In this example, most of the probability mass is concentrated on c, while the other options are still possible but clearly less likely. That's the point of softmax: it converts relative scores into meaningful, comparable probabilities that sum to 100%.
Think of a raffle. Hardmax is when the person who bought the most tickets always wins the prize — the top score takes it, every time. Softmax is when everyone's chance is proportional to the tickets they hold: even if I bought just one ticket, I may still get lucky. Who knows. That's the psychology of softmax.
This is how a language model chooses its next word. Each time a word appears in the training data, it earns a ticket. Hardmax would always speak the word with the most tickets — the same safe choice, over and over. Softmax gives every word a chance proportional to its tickets, so less common words can still be spoken. The word with the most tickets still has the highest chance of winning — just not 100%. That's what lets the model surprise us with its creativity (and also its hallucinations) instead of repeating itself.
https://t.iss.one/CodeProgrammer
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A good selection for those who want to improve their skills in practice, rather than just reading theory:
tags: #ML #DataScience #DataAnalysis
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Awesome DataScience — a structured list of open-source data, datasets, libraries, and tutorials for solving real-world problems. 🛠️
It's useful for both beginners and those already familiar with the field — you'll find something new here.
⛓️ Link to GitHub: https://github.com/academic/awesome-datascience
tags: #DataScientist
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Most AI engineers never fully understood the maths behind what they build! 🤯🧮
This is an open, unconventional textbook covering maths, CS, and AI from the ground up, written for curious practitioners who want to deeply understand the field, not just survive an interview. 📘✨
Over 7 years of AI/ML experience distilled into intuition-first, no hand-waving explanations that connect the concepts in a way that actually sticks. 🧠🔗
What it covers:
- Vectors, linear algebra, calculus, and optimization 📐📉
- Classical machine learning and deep learning 🤖
- Transformer architectures and LLMs 🦄
- Efficient architectures, quantization, and distillation ⚡️
- CUDA, GPU programming, and SIMD 🚀
- AI inference and deployment 🌐
Ships with an MCP server so Claude Code, Cursor, and any MCP-compatible agent can use the compendium as a live knowledge base during development. You only need elementary maths and basic Python to start. 🐍🏗
Repo: https://github.com/HenryNdubuaku/maths-cs-ai-compendium 🔗
https://t.iss.one/CodeProgrammer
This is an open, unconventional textbook covering maths, CS, and AI from the ground up, written for curious practitioners who want to deeply understand the field, not just survive an interview. 📘✨
Over 7 years of AI/ML experience distilled into intuition-first, no hand-waving explanations that connect the concepts in a way that actually sticks. 🧠🔗
What it covers:
- Vectors, linear algebra, calculus, and optimization 📐📉
- Classical machine learning and deep learning 🤖
- Transformer architectures and LLMs 🦄
- Efficient architectures, quantization, and distillation ⚡️
- CUDA, GPU programming, and SIMD 🚀
- AI inference and deployment 🌐
Ships with an MCP server so Claude Code, Cursor, and any MCP-compatible agent can use the compendium as a live knowledge base during development. You only need elementary maths and basic Python to start. 🐍🏗
Repo: https://github.com/HenryNdubuaku/maths-cs-ai-compendium 🔗
https://t.iss.one/CodeProgrammer
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Overfitting and Generalisation in ML.pdf
380.5 KB
Overfitting and Generalization in Machine Learning
My ML model had 100% accuracy.
And was completely useless.
That's not a paradox; that's overfitting.
The model didn't learn. It memorized.
Here's the mathematical core most tutorials skip:
E[loss] = Bias² + Variance + σ²
→ Bias² = too simple → Underfitting
→ Variance = too complex → Overfitting
→ σ² = irreducible → always there
What this actually means in practice:
→ A degree-9 polynomial on 6 data points hits R² = 1.0 and oscillates wildly between them
→ A linear model on sine-wave data has near-zero variance — but massive bias
→ The optimal model isn't the simplest. Not the most complex. It's the one minimizing Bias² + Variance
And the generalization gap?
Formally defined as:
gen_gap(f) = R(f) − R_emp(f)
When this value is ≫ 0, your model is learning noise, not signal.
The fix isn't "collect more data and hope."
The fix is regularization, which I derive fully in my paper: L1, L2, Dropout, and Early Stopping, all from first principles.
Which regularization strategy do you use most and why?
https://t.iss.one/CodeProgrammer
My ML model had 100% accuracy.
And was completely useless.
That's not a paradox; that's overfitting.
The model didn't learn. It memorized.
Here's the mathematical core most tutorials skip:
E[loss] = Bias² + Variance + σ²
→ Bias² = too simple → Underfitting
→ Variance = too complex → Overfitting
→ σ² = irreducible → always there
What this actually means in practice:
→ A degree-9 polynomial on 6 data points hits R² = 1.0 and oscillates wildly between them
→ A linear model on sine-wave data has near-zero variance — but massive bias
→ The optimal model isn't the simplest. Not the most complex. It's the one minimizing Bias² + Variance
And the generalization gap?
Formally defined as:
gen_gap(f) = R(f) − R_emp(f)
When this value is ≫ 0, your model is learning noise, not signal.
The fix isn't "collect more data and hope."
The fix is regularization, which I derive fully in my paper: L1, L2, Dropout, and Early Stopping, all from first principles.
Which regularization strategy do you use most and why?
https://t.iss.one/CodeProgrammer
❤6🔥1💯1
Hugging Face has literally gathered all the key "secrets". 🤔
It's important to understand the evaluation of large language models.📊
While you're working with language models:
> training or retraining your models,🔄
> selecting a model for a task, 🎯
> or trying to understand the current state of the field,🌍
the question almost inevitably arises:
how to understand that a model is good?❓
The answer is quality evaluation. It's everywhere:
> leaderboards with model ratings,🏆
> benchmarks that supposedly measure reasoning,🧠
> knowledge, coding or mathematics,👨💻
> articles with claimed new best results.📈
But what is evaluation actually?🤷♂️
And what does it really show?🔍
This guide helps to understand everything.📚
https://huggingface.co/spaces/OpenEvals/evaluation-guidebook#what-is-model-evaluation-about
What is model evaluation all about🤖
Basic concepts of large language models for understanding evaluation 🏗️
Evaluation through ready-made benchmarks 📏
Creating your own evaluation system🔧
The main problem of evaluation ⚠️
Evaluation of free text📝
Statistical correctness of evaluation📉
Cost and efficiency of evaluation💰
https://t.iss.one/CodeProgrammer🟢
It's important to understand the evaluation of large language models.
While you're working with language models:
> training or retraining your models,
> selecting a model for a task, 🎯
> or trying to understand the current state of the field,
the question almost inevitably arises:
how to understand that a model is good?
The answer is quality evaluation. It's everywhere:
> leaderboards with model ratings,
> benchmarks that supposedly measure reasoning,
> knowledge, coding or mathematics,
> articles with claimed new best results.
But what is evaluation actually?
And what does it really show?
This guide helps to understand everything.
https://huggingface.co/spaces/OpenEvals/evaluation-guidebook#what-is-model-evaluation-about
What is model evaluation all about
Basic concepts of large language models for understanding evaluation 🏗️
Evaluation through ready-made benchmarks 📏
Creating your own evaluation system
The main problem of evaluation ⚠️
Evaluation of free text
Statistical correctness of evaluation
Cost and efficiency of evaluation
https://t.iss.one/CodeProgrammer
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