🔖 A huge open-source course on AI Engineering from scratch
In the repository, we've collected:
— 435 lessons;
— 320+ hours of content;
— Python, TypeScript, and Rust;
— AI agents, MCP servers, prompts, and AI skills.
Moreover, almost every lesson includes practical tasks, so this isn't just theory, but a full-fledged roadmap for AI Engineering. 🚀
⛓️ Link to the repository
https://github.com/rohitg00/ai-engineering-from-scratch
#AI #MachineLearning #Python #Rust #OpenSource #Tech
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In the repository, we've collected:
— 435 lessons;
— 320+ hours of content;
— Python, TypeScript, and Rust;
— AI agents, MCP servers, prompts, and AI skills.
Moreover, almost every lesson includes practical tasks, so this isn't just theory, but a full-fledged roadmap for AI Engineering. 🚀
⛓️ Link to the repository
https://github.com/rohitg00/ai-engineering-from-scratch
#AI #MachineLearning #Python #Rust #OpenSource #Tech
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Transformer implementations for vision, audio, and AI agents 🤖👁️🎵
Repo: https://github.com/Nicolepcx/transformers-the-definitive-guide
#AI #MachineLearning #Vision #Audio #Agents #Tech
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Repo: https://github.com/Nicolepcx/transformers-the-definitive-guide
#AI #MachineLearning #Vision #Audio #Agents #Tech
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✅ 13 full courses live right now
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"Calculus: Early Transcendentals" is an excellent free textbook for building a solid foundation in mathematical analysis. 📘
The book is written in a clear and accessible language, while maintaining the necessary mathematical rigor. It contains a large number of examples and problems, making it suitable for both self-study and use in the educational process. 🎓
The textbook covers a wide range of topics, including:
• limits;
• derivatives;
• integrals;
• sequences and series;
• differential equations;
• multivariate analysis.
I consider this book another valuable tool in the arsenal of anyone studying mathematics. 🛠️
If you are a student and want to master or review key topics in mathematical analysis, or a teacher looking for new ideas and alternative explanations, this textbook is definitely worth attention.
https://open.umn.edu/opentextbooks/textbooks/415
https://github.com/antoniolupetti/algebrica
#Calculus #Math #FreeTextbook #StudyGuide #Mathematics #STEM
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The book is written in a clear and accessible language, while maintaining the necessary mathematical rigor. It contains a large number of examples and problems, making it suitable for both self-study and use in the educational process. 🎓
The textbook covers a wide range of topics, including:
• limits;
• derivatives;
• integrals;
• sequences and series;
• differential equations;
• multivariate analysis.
I consider this book another valuable tool in the arsenal of anyone studying mathematics. 🛠️
If you are a student and want to master or review key topics in mathematical analysis, or a teacher looking for new ideas and alternative explanations, this textbook is definitely worth attention.
https://open.umn.edu/opentextbooks/textbooks/415
https://github.com/antoniolupetti/algebrica
#Calculus #Math #FreeTextbook #StudyGuide #Mathematics #STEM
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Data leakage is one of the main reasons why ML demos look impressive... and then fail in production. 📉
The model didn't become smarter.
It just happened to see the correct answers in advance.
In 4 minutes, you'll understand where data leaks hide. 🔍
Let's break it down below: 👇
1. Data Leakage 🕳️
Data leakage occurs when information that won't be available at the time of actual prediction is used during the model training process.
Because of this, metrics on the validation stage can look much better than the actual quality of the model on new, previously unseen data.
2. Model Evaluation ⚖️
The test set isn't just "additional data".
It's a simulation of the future.
Only train the model on the information that would have been available to you at the time of prediction.
Evaluate it on examples that the model couldn't have influenced during training.
3. Direct Leakage 🚨
This is the most obvious type of leakage.
Examples:
- a field with information from the future;
- an ID that encodes the target variable;
- a variable that appears only after an event has occurred;
- duplicate records in both the training and test sets.
If a feature doesn't exist at the time of inference (prediction), then it's likely a source of data leakage.
4. Indirect Leakage 🕵️
This is the type of leakage that most often traps teams.
You perform normalization, imputation, feature selection, outlier removal, or dimensionality reduction before splitting the data into a training and test set.
The model didn't directly see the data from the test set.
But your preprocessing pipeline already saw it.
5. Train/Test Split ✂️
Wrong:
Right:
The same idea applies to imputers, encoders, feature selection, PCA, and any preprocessing step that is trained on the data.
6. Cross-Validation 🔄
Each fold is a mini-experiment with a training and test set.
Therefore, preprocessing should be performed within each fold.
If you prepared the entire dataset once and then ran cross-validation, each fold would already have had access to its held-out data.
7. Pipelines 🛠️
A pipeline isn't just a way to make the code cleaner.
It's also a defense against data leakage.
Combine preprocessing, feature selection, and the model into a single pipeline, and then pass this pipeline to cross-validation or hyperparameter search (grid search).
8. AI Engineering Version 🤖
Data leaks also occur in RAG systems and when evaluating LLMs.
Leakage occurs when you tune chunks, prompts, re-rankers, thresholds, or examples on the same evaluation dataset that you later present as "held-out".
As a result, your benchmark turns into training data.
9. Leakage Checklist ✅
Before trusting the obtained metric, ask yourself:
- Could this feature exist at the time of prediction?
- Was any transformation (transform) step trained (fit) on the test data?
- Did cross-validation include the entire pipeline?
- Were we tuning parameters on the final evaluation dataset?
If the answer is "yes", then the metric likely doesn't reflect the actual quality of the model.
#MachineLearning #DataScience #MLOps #DataLeakage #ArtificialIntelligence #TechTips
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The model didn't become smarter.
It just happened to see the correct answers in advance.
In 4 minutes, you'll understand where data leaks hide. 🔍
Let's break it down below: 👇
1. Data Leakage 🕳️
Data leakage occurs when information that won't be available at the time of actual prediction is used during the model training process.
Because of this, metrics on the validation stage can look much better than the actual quality of the model on new, previously unseen data.
2. Model Evaluation ⚖️
The test set isn't just "additional data".
It's a simulation of the future.
Only train the model on the information that would have been available to you at the time of prediction.
Evaluate it on examples that the model couldn't have influenced during training.
3. Direct Leakage 🚨
This is the most obvious type of leakage.
Examples:
- a field with information from the future;
- an ID that encodes the target variable;
- a variable that appears only after an event has occurred;
- duplicate records in both the training and test sets.
If a feature doesn't exist at the time of inference (prediction), then it's likely a source of data leakage.
4. Indirect Leakage 🕵️
This is the type of leakage that most often traps teams.
You perform normalization, imputation, feature selection, outlier removal, or dimensionality reduction before splitting the data into a training and test set.
The model didn't directly see the data from the test set.
But your preprocessing pipeline already saw it.
5. Train/Test Split ✂️
Wrong:
fit the scaler on all data → split the data → evaluate
Right:
split the data → fit the scaler only on the training set → apply it to both the training and test sets
The same idea applies to imputers, encoders, feature selection, PCA, and any preprocessing step that is trained on the data.
6. Cross-Validation 🔄
Each fold is a mini-experiment with a training and test set.
Therefore, preprocessing should be performed within each fold.
If you prepared the entire dataset once and then ran cross-validation, each fold would already have had access to its held-out data.
7. Pipelines 🛠️
A pipeline isn't just a way to make the code cleaner.
It's also a defense against data leakage.
Combine preprocessing, feature selection, and the model into a single pipeline, and then pass this pipeline to cross-validation or hyperparameter search (grid search).
8. AI Engineering Version 🤖
Data leaks also occur in RAG systems and when evaluating LLMs.
Leakage occurs when you tune chunks, prompts, re-rankers, thresholds, or examples on the same evaluation dataset that you later present as "held-out".
As a result, your benchmark turns into training data.
9. Leakage Checklist ✅
Before trusting the obtained metric, ask yourself:
- Could this feature exist at the time of prediction?
- Was any transformation (transform) step trained (fit) on the test data?
- Did cross-validation include the entire pipeline?
- Were we tuning parameters on the final evaluation dataset?
If the answer is "yes", then the metric likely doesn't reflect the actual quality of the model.
#MachineLearning #DataScience #MLOps #DataLeakage #ArtificialIntelligence #TechTips
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Telegram
AI PYTHON 🌟
You’ve been invited to add the folder “AI PYTHON 🌟”, which includes 14 chats.
❤3👍3
FREE MIT books on AI and Machine Learning: 📚🤖
1. Foundations of Machine Learning cs.nyu.edu/~mohri/mlbook/
2. Understanding Deep Learning udlbook.github.io/udlbook/
3. Introduction to Machine Learning Systems ❯ Vol 1: mlsysbook.ai/vol1/assets/do ❯ Vol 2: mlsysbook.ai/vol2/assets/do
4. Algorithms for ML algorithmsbook.com
5. Deep Learning deeplearningbook.org
6. Reinforcement Learning andrew.cmu.edu/course/10-703/
7. Distributional Reinforcement Learning direct.mit.edu/books/oa-monog
8. Multi Agent Reinforcement Learning marl-book.com
9. Agents in the Long Game of AI direct.mit.edu/books/oa-monog
10. Fairness and Machine Learning fairmlbook.org
11. Probabilistic Machine Learning
❯ Part 1 : probml.github.io/pml-book/book1
❯ Part 2 : probml.github.io/pml-book/book2
#MIT #AI #MachineLearning #DeepLearning #ReinforcementLearning #FreeBooks
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1. Foundations of Machine Learning cs.nyu.edu/~mohri/mlbook/
2. Understanding Deep Learning udlbook.github.io/udlbook/
3. Introduction to Machine Learning Systems ❯ Vol 1: mlsysbook.ai/vol1/assets/do ❯ Vol 2: mlsysbook.ai/vol2/assets/do
4. Algorithms for ML algorithmsbook.com
5. Deep Learning deeplearningbook.org
6. Reinforcement Learning andrew.cmu.edu/course/10-703/
7. Distributional Reinforcement Learning direct.mit.edu/books/oa-monog
8. Multi Agent Reinforcement Learning marl-book.com
9. Agents in the Long Game of AI direct.mit.edu/books/oa-monog
10. Fairness and Machine Learning fairmlbook.org
11. Probabilistic Machine Learning
❯ Part 1 : probml.github.io/pml-book/book1
❯ Part 2 : probml.github.io/pml-book/book2
#MIT #AI #MachineLearning #DeepLearning #ReinforcementLearning #FreeBooks
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# Algebra 2 📚
"Algebra 2" is another completely free textbook that covers a significant portion of algebra at both the pre-university and initial university levels. 🎓
With over 1,100 pages and a large number of worked examples, practical problems, and exercises, it covers linear equations, quadratic equations, polynomial equations, rational equations, irrational equations, exponential and logarithmic equations, systems of equations, inequalities, and many fundamental concepts underlying algebra. 🧮
In my opinion, this is one of the most comprehensive free resources for studying equation theory and algebraic methods typically encountered in the first years of university study. 💡
Source: https://openstax.org/details/books/algebra-and-trigonometry-2e
#Algebra #Math #FreeTextbook #Education #Study #University
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"Algebra 2" is another completely free textbook that covers a significant portion of algebra at both the pre-university and initial university levels. 🎓
With over 1,100 pages and a large number of worked examples, practical problems, and exercises, it covers linear equations, quadratic equations, polynomial equations, rational equations, irrational equations, exponential and logarithmic equations, systems of equations, inequalities, and many fundamental concepts underlying algebra. 🧮
In my opinion, this is one of the most comprehensive free resources for studying equation theory and algebraic methods typically encountered in the first years of university study. 💡
Source: https://openstax.org/details/books/algebra-and-trigonometry-2e
#Algebra #Math #FreeTextbook #Education #Study #University
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Introduction to Deep RL and DQN
Link: https://www.dailydoseofds.com/rl-course-part-6/
🤖 #DeepRL #DQN #ReinforcementLearning #AI #MachineLearning #DataScience
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🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
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Link: https://www.dailydoseofds.com/rl-course-part-6/
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⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
✅ 13 courses live + 40+ coming soon
🎯 One access, lifetime updates
🔑 Use code: PRESALE-BOOK-WAVE-2GFG
👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO
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