Overfitting and Generalisation in ML.pdf
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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.me/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.me/CodeProgrammer
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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.me/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.me/CodeProgrammer
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Forwarded from Machine Learning
Algorithms by Jeff Erickson - one of the best algorithm books out there 📚.
The illustrations make complex concepts surprisingly easy to follow 🎨. Highly recommend this 👍.
Link: https://jeffe.cs.illinois.edu/teaching/algorithms/ 🔗
https://t.me/MachineLearning9
The illustrations make complex concepts surprisingly easy to follow 🎨. Highly recommend this 👍.
Link: https://jeffe.cs.illinois.edu/teaching/algorithms/ 🔗
https://t.me/MachineLearning9
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🧐 Confusion Matrix: Less confusing 🤯
Many data science beginners struggle to understand true negative (TN), false negative (FN), false positive (FP), and true positive (TP). 🤔
You can easily understand the values using the confusion matrix. 📊
💡 It is a 2x2 matrix for a binary classifier:
- True Negative (TN): True Negative prediction ✅
- False Negative (FN): False Negative prediction ❌
- False Positive (FP): False Positive prediction 🚨
- True Positive (TP): True Positive prediction 🎯
❓ For each prediction, ask two questions:
1. Did the model do it right? Yes (True) or No (False)
2. What was the predicted class? Positive or Negative
https://t.me/CodeProgrammer
Many data science beginners struggle to understand true negative (TN), false negative (FN), false positive (FP), and true positive (TP). 🤔
You can easily understand the values using the confusion matrix. 📊
💡 It is a 2x2 matrix for a binary classifier:
- True Negative (TN): True Negative prediction ✅
- False Negative (FN): False Negative prediction ❌
- False Positive (FP): False Positive prediction 🚨
- True Positive (TP): True Positive prediction 🎯
❓ For each prediction, ask two questions:
1. Did the model do it right? Yes (True) or No (False)
2. What was the predicted class? Positive or Negative
https://t.me/CodeProgrammer
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Stop asking "CNN or VLM?" — the answer is both. 🤔
Everyone's talking about Vision Language Models replacing traditional computer vision. 📢
Here's the reality: they're not replacing anything. They're expanding what's possible. 🚀
CNNs are excellent at precise perception — detecting, localizing, classifying fixed objects at high speed and low cost. 🎯
Vision Language Models are better at interpretation — answering open-ended questions about a scene that you can't define as fixed labels in advance. 🧠
The smartest production systems combine both:
→ A lightweight CNN runs first (fast, cheap) ⚡️
→ A VLM handles the complex reasoning (flexible, expensive) 💎
This is the difference between giving machines eyes 👁 vs giving them the ability to talk about what they see. 🗣
Dr. Satya Mallick breaks it down in under 2 minutes. 👇
#ComputerVision #AI #MachineLearning #VisionLanguageModel #DeepLearning #OpenCV #AIEngineering
https://t.me/CodeProgrammer✅
Everyone's talking about Vision Language Models replacing traditional computer vision. 📢
Here's the reality: they're not replacing anything. They're expanding what's possible. 🚀
CNNs are excellent at precise perception — detecting, localizing, classifying fixed objects at high speed and low cost. 🎯
Vision Language Models are better at interpretation — answering open-ended questions about a scene that you can't define as fixed labels in advance. 🧠
The smartest production systems combine both:
→ A lightweight CNN runs first (fast, cheap) ⚡️
→ A VLM handles the complex reasoning (flexible, expensive) 💎
This is the difference between giving machines eyes 👁 vs giving them the ability to talk about what they see. 🗣
Dr. Satya Mallick breaks it down in under 2 minutes. 👇
#ComputerVision #AI #MachineLearning #VisionLanguageModel #DeepLearning #OpenCV #AIEngineering
https://t.me/CodeProgrammer
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🧐 Python Cheatsheet — a convenient cheat sheet for Python that really saves time at work!
The repository contains a summary of key topics: from basic syntax and data structures to working with files, environments, and OOP with classes and magic methods. Everything is presented compactly, without unnecessary theory, with examples that can be immediately applied in code.
Repo: https://github.com/onyxwizard/python-cheatsheet
https://t.me/CodeProgrammer 👩💻
The repository contains a summary of key topics: from basic syntax and data structures to working with files, environments, and OOP with classes and magic methods. Everything is presented compactly, without unnecessary theory, with examples that can be immediately applied in code.
Repo: https://github.com/onyxwizard/python-cheatsheet
https://t.me/CodeProgrammer 👩💻
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This Machine Learning Cheat Sheet Saved Me Hours of Revision ⏳
It includes:
✅ Supervised & Unsupervised algorithms
✅ Regression, Classification & Clustering techniques
✅ PCA & Dimensionality Reduction
✅ Neural Networks, CNN, RNN & Transformers
✅ Assumptions, Pros/Cons & Real-world use cases
Whether you're:
🔹 Preparing for data science interviews
🔹 Working on ML projects
🔹 Or strengthening your fundamentals
this one-page guide is a must-save.
♻️ Repost and share with your ML circle.
#MachineLearning #DataScience #AI #MLAlgorithms #InterviewPrep #LearnML
https://t.me/CodeProgrammer🐍
It includes:
✅ Supervised & Unsupervised algorithms
✅ Regression, Classification & Clustering techniques
✅ PCA & Dimensionality Reduction
✅ Neural Networks, CNN, RNN & Transformers
✅ Assumptions, Pros/Cons & Real-world use cases
Whether you're:
🔹 Preparing for data science interviews
🔹 Working on ML projects
🔹 Or strengthening your fundamentals
this one-page guide is a must-save.
♻️ Repost and share with your ML circle.
#MachineLearning #DataScience #AI #MLAlgorithms #InterviewPrep #LearnML
https://t.me/CodeProgrammer
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Forwarded from Learn Python Coding
𝗠𝗮𝘀𝘁𝗲𝗿_𝗣𝘆𝘁𝗵𝗼𝗻_𝘁𝗵𝗲_𝗥𝗶𝗴𝗵𝘁_𝗪𝗮𝘆.pdf
6.6 MB
Master Python the Right Way – Without Procrastination. 🐍✨
When I first started learning Python, I quickly realized:
You can't master a programming language just by reading syntax or watching tutorials. 📚🚫
Real growth happens when you practice, build, and solve problems on your own. 🛠💻
That's exactly why I've compiled a collection of Python programs – designed to take you from basics to advanced logic-building. 📈🧠
What is this collection about? 🤔
✔️ Beginner to advanced programs with clear explanations
✔️ Pattern-based exercises to strengthen core fundamentals
✔️ Problem-solving programs that sharpen logical thinking
Why is this important? 🌟
You don't just learn "how to code", you start learning "how to think like a programmer". 🧠⚡️
This is perfect for: 🎯
• Preparing for technical interviews 🤝
• Participating in coding challenges 🏆
• Building real-world Python projects 🚀
https://t.me/pythonRe
When I first started learning Python, I quickly realized:
You can't master a programming language just by reading syntax or watching tutorials. 📚🚫
Real growth happens when you practice, build, and solve problems on your own. 🛠💻
That's exactly why I've compiled a collection of Python programs – designed to take you from basics to advanced logic-building. 📈🧠
What is this collection about? 🤔
✔️ Beginner to advanced programs with clear explanations
✔️ Pattern-based exercises to strengthen core fundamentals
✔️ Problem-solving programs that sharpen logical thinking
Why is this important? 🌟
You don't just learn "how to code", you start learning "how to think like a programmer". 🧠⚡️
This is perfect for: 🎯
• Preparing for technical interviews 🤝
• Participating in coding challenges 🏆
• Building real-world Python projects 🚀
https://t.me/pythonRe
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🔥 Convolutional Neural Networks: Clearly explained!
🖼 Convolutional Neural Networks (CNNs): CNNs belong to the deep learning methods with layers like convolutional, pooling, and fully-connected layers that transform input images for recognition.
➡️ Feedforward Process: Data flows from input to output layers. Images undergo convolution operations, ReLu activation, and Max-Pooling to reduce size and enhance translation and scaling invariance. Finally, data is classified through a fully connected network.
🔄 Training Process: The training involves batches, backpropagation, and gradient descent to minimize errors. The weights start with random values and are updated through backpropagation. This cycle repeats until accuracy is achieved.
📊 Use Cases: CNNs excel in processing images, videos, and audio for tasks like classification, segmentation, and object detection.
⚠️ Limitations: While CNNs handle translation and scaling well, they struggle with rotation invariance.
Want to learn more about CNNs?
Then, check out super-detailed article about it.👇
https://lnkd.in/eyA_DnYj
https://t.me/CodeProgrammer🧠
🖼 Convolutional Neural Networks (CNNs): CNNs belong to the deep learning methods with layers like convolutional, pooling, and fully-connected layers that transform input images for recognition.
➡️ Feedforward Process: Data flows from input to output layers. Images undergo convolution operations, ReLu activation, and Max-Pooling to reduce size and enhance translation and scaling invariance. Finally, data is classified through a fully connected network.
🔄 Training Process: The training involves batches, backpropagation, and gradient descent to minimize errors. The weights start with random values and are updated through backpropagation. This cycle repeats until accuracy is achieved.
📊 Use Cases: CNNs excel in processing images, videos, and audio for tasks like classification, segmentation, and object detection.
⚠️ Limitations: While CNNs handle translation and scaling well, they struggle with rotation invariance.
Want to learn more about CNNs?
Then, check out super-detailed article about it.
https://lnkd.in/eyA_DnYj
https://t.me/CodeProgrammer
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Unlock Your AI Career
Join our Data Science Full Stack with AI Course – a real-time, project-based online training designed for hands-on mastery.
Core Topics Covered
• Data Science using Python with Generative AI: Build end-to-end data pipelines, from data wrangling to deploying AI models with Python libraries like Pandas, Scikit-learn, and Hugging Face transformers.
• Prompt Engineering: Craft precise prompts to maximize output from models like GPT and Gemini for accurate, creative results.
• AI Agents & Agentic AI: Develop autonomous agents that reason, plan, and act using frameworks like Lang Chain for real-world automation.
Why Choose This Course?
This training emphasizes live sessions, industry projects, and practical skills for immediate job impact, similar to top programs offering 100+ hours of Python-to-AI progression.
Ready to start? Call/WhatsApp: (+91)-7416877757
WhatsApp Link:-
http://wa.me/+917416877757
Join our Data Science Full Stack with AI Course – a real-time, project-based online training designed for hands-on mastery.
Core Topics Covered
• Data Science using Python with Generative AI: Build end-to-end data pipelines, from data wrangling to deploying AI models with Python libraries like Pandas, Scikit-learn, and Hugging Face transformers.
• Prompt Engineering: Craft precise prompts to maximize output from models like GPT and Gemini for accurate, creative results.
• AI Agents & Agentic AI: Develop autonomous agents that reason, plan, and act using frameworks like Lang Chain for real-world automation.
Why Choose This Course?
This training emphasizes live sessions, industry projects, and practical skills for immediate job impact, similar to top programs offering 100+ hours of Python-to-AI progression.
Ready to start? Call/WhatsApp: (+91)-7416877757
WhatsApp Link:-
http://wa.me/+917416877757
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Machine Learning Specialization — Study Notes & Labs 📚 🔬
Personal notes and lab notebooks from the Machine Learning Specialization by DeepLearning.AI & Stanford Online (Coursera), instructed by Prof. Andrew Ng.🧑🎓
📂 Repo: https://github.com/TruongDat05/machine-learning-notes-and-code
https://t.me/CodeProgrammer📁
Personal notes and lab notebooks from the Machine Learning Specialization by DeepLearning.AI & Stanford Online (Coursera), instructed by Prof. Andrew Ng.
https://t.me/CodeProgrammer
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🎓 Thesis • Dissertation • Research • Programming • Simulation
From a single research idea…
to a complete academic masterpiece.
🔹 Professional assistance for:
✔️ Master’s & PhD Theses
✔️ ISI / Scopus Articles
✔️ Research Proposals & Methodology
✔️ Data Analysis & Statistical Modeling
✔️ AI & Machine Learning Projects
✔️ MATLAB • Python • Simulink • Abaqus • COMSOL • Ansys • ETAP • PSCAD • HOMER • Proteus • LabVIEW
✔️ Electrical, Civil, Mechanical, Medical, Management, Computer Science & All Engineering Fields
✔️ Rare & High-Quality Datasets
✔️ Simulation Projects & Optimization Algorithms
✔️ Academic Presentation Design
✔️ Journal Revision & Reviewer Response Preparation
📊 Accurate Results
📚 Professional Documentation
💻 Clean & Structured Coding
🔒 Full Confidentiality
⏳ On-Time Delivery
Your research deserves more than copy-paste work.
It deserves precision, originality, and engineering-level thinking.
✨ Turning complex ideas into publishable research.
📩 Contact us for consultation and project evaluation.
https://t.me/Omidyzd62
From a single research idea…
to a complete academic masterpiece.
🔹 Professional assistance for:
✔️ Master’s & PhD Theses
✔️ ISI / Scopus Articles
✔️ Research Proposals & Methodology
✔️ Data Analysis & Statistical Modeling
✔️ AI & Machine Learning Projects
✔️ MATLAB • Python • Simulink • Abaqus • COMSOL • Ansys • ETAP • PSCAD • HOMER • Proteus • LabVIEW
✔️ Electrical, Civil, Mechanical, Medical, Management, Computer Science & All Engineering Fields
✔️ Rare & High-Quality Datasets
✔️ Simulation Projects & Optimization Algorithms
✔️ Academic Presentation Design
✔️ Journal Revision & Reviewer Response Preparation
📊 Accurate Results
📚 Professional Documentation
💻 Clean & Structured Coding
🔒 Full Confidentiality
⏳ On-Time Delivery
Your research deserves more than copy-paste work.
It deserves precision, originality, and engineering-level thinking.
✨ Turning complex ideas into publishable research.
📩 Contact us for consultation and project evaluation.
https://t.me/Omidyzd62
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📚 AI / ML / DL Learning Resources Hub 🤖🧠
A structured, end-to-end roadmap to master AI — from fundamentals to cutting-edge research. 🚀
A carefully curated, all-in-one repository designed to help Computer Science students, AI enthusiasts, and professionals 👩💻👨💻 who want to build strong foundations and progress confidently from beginner to advanced levels 📈. This hub brings together the high-quality books 📖, courses 🎓, playlists 🎵, research papers 📝, tools 🛠, and learning roadmaps 🗺 covering: Artificial Intelligence, Machine Learning, Deep Learning, Data Science 📊, Transformers, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and MLOps 🔄, all organized in a clear, practical, and industry-relevant manner.
The resources are selected to balance theory 🧠, intuition 💡, and real-world application 🌍, allowing learners to follow modules sequentially or in parallel ⏳ based on their goals.
⭐️ Recommended resources highlight high-impact content widely used in academia 🏛, research 🔬, and industry 🏭, ensuring you focus on what truly matters in modern AI.
🆘 Repo: https://github.com/bishwaghimire/ai-learning-roadmaps
By: https://t.me/CodeProgrammer
A structured, end-to-end roadmap to master AI — from fundamentals to cutting-edge research. 🚀
A carefully curated, all-in-one repository designed to help Computer Science students, AI enthusiasts, and professionals 👩💻👨💻 who want to build strong foundations and progress confidently from beginner to advanced levels 📈. This hub brings together the high-quality books 📖, courses 🎓, playlists 🎵, research papers 📝, tools 🛠, and learning roadmaps 🗺 covering: Artificial Intelligence, Machine Learning, Deep Learning, Data Science 📊, Transformers, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and MLOps 🔄, all organized in a clear, practical, and industry-relevant manner.
The resources are selected to balance theory 🧠, intuition 💡, and real-world application 🌍, allowing learners to follow modules sequentially or in parallel ⏳ based on their goals.
⭐️ Recommended resources highlight high-impact content widely used in academia 🏛, research 🔬, and industry 🏭, ensuring you focus on what truly matters in modern AI.
By: https://t.me/CodeProgrammer
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🎓 Thesis • Dissertation • Research • Programming • Simulation
From a single research idea…
to a complete academic masterpiece.
🔹 Professional assistance for:
✔️ Master’s & PhD Theses
✔️ ISI / Scopus Articles
✔️ Research Proposals & Methodology
✔️ Data Analysis & Statistical Modeling
✔️ AI & Machine Learning Projects
✔️ MATLAB • Python • Simulink • Abaqus • COMSOL • Ansys • ETAP • PSCAD • HOMER • Proteus • LabVIEW
✔️ Electrical, Civil, Mechanical, Medical, Management, Computer Science & All Engineering Fields
✔️ Rare & High-Quality Datasets
✔️ Simulation Projects & Optimization Algorithms
✔️ Academic Presentation Design
✔️ Journal Revision & Reviewer Response Preparation
📊 Accurate Results
📚 Professional Documentation
💻 Clean & Structured Coding
🔒 Full Confidentiality
⏳ On-Time Delivery
Your research deserves more than copy-paste work.
It deserves precision, originality, and engineering-level thinking.
✨ Turning complex ideas into publishable research.
📩 Contact us for consultation and project evaluation.
https://t.me/Omidyzd62
From a single research idea…
to a complete academic masterpiece.
🔹 Professional assistance for:
✔️ Master’s & PhD Theses
✔️ ISI / Scopus Articles
✔️ Research Proposals & Methodology
✔️ Data Analysis & Statistical Modeling
✔️ AI & Machine Learning Projects
✔️ MATLAB • Python • Simulink • Abaqus • COMSOL • Ansys • ETAP • PSCAD • HOMER • Proteus • LabVIEW
✔️ Electrical, Civil, Mechanical, Medical, Management, Computer Science & All Engineering Fields
✔️ Rare & High-Quality Datasets
✔️ Simulation Projects & Optimization Algorithms
✔️ Academic Presentation Design
✔️ Journal Revision & Reviewer Response Preparation
📊 Accurate Results
📚 Professional Documentation
💻 Clean & Structured Coding
🔒 Full Confidentiality
⏳ On-Time Delivery
Your research deserves more than copy-paste work.
It deserves precision, originality, and engineering-level thinking.
✨ Turning complex ideas into publishable research.
📩 Contact us for consultation and project evaluation.
https://t.me/Omidyzd62
Telegram
اميد
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Automate research with NotebookLM + Python 🤖🐍
Notebooklm-py — is a unofficial library for working with Google NotebookLM,📚🧠 which allows automating research tasks, generating content, and connecting AI agents. Suitable for prototypes, pet projects, and personal tools — works both via Python and CLI ⌨️
What it can do:
• integration with AI agents and Claude Code 🤖
• automatic import and processing of sources 📥
• generation of podcasts, videos, and educational materials 🎙️🎥📖
• working via Python API and command line 💻
• using unofficial Google APIs 🔧
https://github.com/teng-lin/notebooklm-py
https://t.me/CodeProgrammer📱
Notebooklm-py — is a unofficial library for working with Google NotebookLM,📚🧠 which allows automating research tasks, generating content, and connecting AI agents. Suitable for prototypes, pet projects, and personal tools — works both via Python and CLI ⌨️
What it can do:
• integration with AI agents and Claude Code 🤖
• automatic import and processing of sources 📥
• generation of podcasts, videos, and educational materials 🎙️🎥📖
• working via Python API and command line 💻
• using unofficial Google APIs 🔧
https://github.com/teng-lin/notebooklm-py
https://t.me/CodeProgrammer
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Forwarded from Machine Learning
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Google Gemma 4's pre-training is completely free
All you need is a browser and access to more than 500 models to choose from.
The process is simple:
1. Open the notebook of Unsloth in Colab
2. Select a model and a dataset
3. Start the trainin
Link: https://colab.research.google.com/github/unslothai/unsloth/blob/main/studio/Unsloth_Studio_Colab.ipynb
It's done😂
👉 https://t.me/MachineLearning9
All you need is a browser and access to more than 500 models to choose from.
The process is simple:
1. Open the notebook of Unsloth in Colab
2. Select a model and a dataset
3. Start the trainin
Link: https://colab.research.google.com/github/unslothai/unsloth/blob/main/studio/Unsloth_Studio_Colab.ipynb
It's done
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Forwarded from Python Courses & Resources
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14 minutes with an Anthropic engineer will teach you more about building agents 🤖 than most devs figure out in months of trial and error 🛠.
Same guy who wrote “Building Effective Agents”, the post every AI builder has bookmarked 📑.
No fluff. No 47-tool frameworks. Just the patterns that actually work in production 🚀:
→ When to use workflows vs. agents (most people get this wrong) ❌
→ Why simple > clever, every single time ✅
→ The orchestrator-worker pattern that scales 📈
→ When NOT to build an agent at all 🛑
If you’re shipping AI products in 2026 and haven’t watched this, you’re doing it on hard mode 🎮.
14 minutes. Bookmark it 📌. Watch it twice 👀.
#AI #Agents #Tech #DevCommunity #FutureTech #ProgrammingConcepts
Same guy who wrote “Building Effective Agents”, the post every AI builder has bookmarked 📑.
No fluff. No 47-tool frameworks. Just the patterns that actually work in production 🚀:
→ When to use workflows vs. agents (most people get this wrong) ❌
→ Why simple > clever, every single time ✅
→ The orchestrator-worker pattern that scales 📈
→ When NOT to build an agent at all 🛑
If you’re shipping AI products in 2026 and haven’t watched this, you’re doing it on hard mode 🎮.
14 minutes. Bookmark it 📌. Watch it twice 👀.
#AI #Agents #Tech #DevCommunity #FutureTech #ProgrammingConcepts
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🔖 Interactive textbook on probability theory and statistics 📊✨
A super-intuitive site where you can visually study distributions, sampling, and statistical concepts. 📈🎲
No tons of formulas and boring theory — everything is demonstrated through interactive examples and simulations. 💻🔬
⛓️ Download here 👇
https://seeing-theory.brown.edu/
#Probability #Statistics #DataScience #Learning #Interactive #Math
https://t.me/CodeProgrammer
A super-intuitive site where you can visually study distributions, sampling, and statistical concepts. 📈🎲
No tons of formulas and boring theory — everything is demonstrated through interactive examples and simulations. 💻🔬
⛓️ Download here 👇
https://seeing-theory.brown.edu/
#Probability #Statistics #DataScience #Learning #Interactive #Math
https://t.me/CodeProgrammer
❤5
Stop. I found “BTTS NO” picks that aren’t hype
Recently I stayed up watching a “sure goal” match… and it ended 0–0 😅⚽ That’s when I quit noisy tipsters and started tracking tight defences & low xG myself 📉🧤
Then I landed inside WinEveryDay’s BTTS-NO feed - straight to the point, no fluff, just match spots and logic ✅🧠
I spent weeks filtering stats so you don’t have to 💸🎯
👉 Join the safest BTTS-NO mindset
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Recently I stayed up watching a “sure goal” match… and it ended 0–0 😅⚽ That’s when I quit noisy tipsters and started tracking tight defences & low xG myself 📉🧤
Then I landed inside WinEveryDay’s BTTS-NO feed - straight to the point, no fluff, just match spots and logic ✅🧠
I spent weeks filtering stats so you don’t have to 💸🎯
👉 Join the safest BTTS-NO mindset
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❤1