ð ððð ðð ðððððððððððð ððððððððð â ððððð ððððððððð ððððð (ððð) ð
GRUs are a simplified yet powerful variation of the LSTM architecture. ð§ Introduced to solve the vanishing gradient problem while reducing computational overhead, GRUs merge gates to create a more efficient "memory" system. âĄïļ They are the go-to choice when you need the performance of an LSTM but have limited compute resources or smaller datasets. ðð
ð. ðððð ðððððððððððð & ððððð ððð ð§
The GRU streamlines the gating process by combining the cell state and hidden state. ð
ððĐðððð ðððð: Determines how much of the previous memory to keep and how much new information to add. ðĨâðĪ
ðððŽðð ðððð: Decides how much of the past information to forget before calculating the next state. ðâģ
ððð§ððĒðððð ððððĒðŊðððĒðĻð§: A "hidden" layer that suggests a potential update based on the current input and the reset memory. ð§Đð
ð. ððð ðððððððððð ðððð ðððð ð
Why choose GRU over its predecessor, the LSTM? ðĪ
ð ðð°ððŦ ðððððŽ: 2 instead of 3, GRUs train faster and use less memory. ððĻ
ðððŽðŽ ðððŦððĶððððŦðŽ: By merging the cell and hidden states, information flow is more direct. ðð
ððððððŦ ðð§ ððĶððĨðĨ ðððððŽðððŽ: GRUs often outperform LSTMs due to having fewer parameters (reducing the risk of overfitting). ðŊð
ð. ððððððððððð ðððððð ð
ððð: The basic loop; prone to short-term memory loss. ðâ
ðððð: The "Heavyweight"; highly accurate but computationally expensive. ðïļââïļð
ððð: The "Lightweight"; optimized for speed and modern efficiency. ðŠķâĄïļ
ð. ðððð-ððððð ðððððððððððð ð
GRUs excel in environments where latency matters: âąïļ
ððĻðĒðð ððĻ ðððąð: Converting voice to text with minimal delay. ðð
ððĻð & ððð ð ðððŊðĒðððŽ: Running sequential models on low-power hardware (like smart sensors). ðĄð
ððŪðŽðĒð ððð§ððŦðððĒðĻð§: Learning the structure of melodies and rhythm for AI-composed audio. ðĩðđ
ð. ððð ðððð ðððððð ðððð ð§Ū
ððĐðððð ðððð: Unlike LSTMs, which use separate input and forget gates, GRU update handles both simultaneously. ðð
ðððŽðð ðððð: Both gates use sigmoid activations to regulate the information flow between 0 and 1. ðð
ððð§ððĒðððð ððððĒðŊðððĒðĻð§: Used to calculate the candidate hidden state before it is merged into the final output. ð§Đâð
ð. ððð ðððððððððð ð
ðððŽðð: Decide how much of the past to ignore. ð
ððð§ððĒðððð: Create a potential new memory step. ð
ððĐðððð: Blend the old state and the new candidate based on the update gate's weight. âïļ
ððŪððĐðŪð: Pass the new hidden state to the next time step. ðŠðââïļ
"GRUs taught machines that sometimes, simplicity is the ultimate sophistication in intelligence." ðĪâĻ
#GRU #AI #MachineLearning #DeepLearning #NeuralNetworks #Tech
GRUs are a simplified yet powerful variation of the LSTM architecture. ð§ Introduced to solve the vanishing gradient problem while reducing computational overhead, GRUs merge gates to create a more efficient "memory" system. âĄïļ They are the go-to choice when you need the performance of an LSTM but have limited compute resources or smaller datasets. ðð
ð. ðððð ðððððððððððð & ððððð ððð ð§
The GRU streamlines the gating process by combining the cell state and hidden state. ð
ððĐðððð ðððð: Determines how much of the previous memory to keep and how much new information to add. ðĨâðĪ
ðððŽðð ðððð: Decides how much of the past information to forget before calculating the next state. ðâģ
ððð§ððĒðððð ððððĒðŊðððĒðĻð§: A "hidden" layer that suggests a potential update based on the current input and the reset memory. ð§Đð
ð. ððð ðððððððððð ðððð ðððð ð
Why choose GRU over its predecessor, the LSTM? ðĪ
ð ðð°ððŦ ðððððŽ: 2 instead of 3, GRUs train faster and use less memory. ððĻ
ðððŽðŽ ðððŦððĶððððŦðŽ: By merging the cell and hidden states, information flow is more direct. ðð
ððððððŦ ðð§ ððĶððĨðĨ ðððððŽðððŽ: GRUs often outperform LSTMs due to having fewer parameters (reducing the risk of overfitting). ðŊð
ð. ððððððððððð ðððððð ð
ððð: The basic loop; prone to short-term memory loss. ðâ
ðððð: The "Heavyweight"; highly accurate but computationally expensive. ðïļââïļð
ððð: The "Lightweight"; optimized for speed and modern efficiency. ðŠķâĄïļ
ð. ðððð-ððððð ðððððððððððð ð
GRUs excel in environments where latency matters: âąïļ
ððĻðĒðð ððĻ ðððąð: Converting voice to text with minimal delay. ðð
ððĻð & ððð ð ðððŊðĒðððŽ: Running sequential models on low-power hardware (like smart sensors). ðĄð
ððŪðŽðĒð ððð§ððŦðððĒðĻð§: Learning the structure of melodies and rhythm for AI-composed audio. ðĩðđ
ð. ððð ðððð ðððððð ðððð ð§Ū
ððĐðððð ðððð: Unlike LSTMs, which use separate input and forget gates, GRU update handles both simultaneously. ðð
ðððŽðð ðððð: Both gates use sigmoid activations to regulate the information flow between 0 and 1. ðð
ððð§ððĒðððð ððððĒðŊðððĒðĻð§: Used to calculate the candidate hidden state before it is merged into the final output. ð§Đâð
ð. ððð ðððððððððð ð
ðððŽðð: Decide how much of the past to ignore. ð
ððð§ððĒðððð: Create a potential new memory step. ð
ððĐðððð: Blend the old state and the new candidate based on the update gate's weight. âïļ
ððŪððĐðŪð: Pass the new hidden state to the next time step. ðŠðââïļ
"GRUs taught machines that sometimes, simplicity is the ultimate sophistication in intelligence." ðĪâĻ
#GRU #AI #MachineLearning #DeepLearning #NeuralNetworks #Tech
âĪ2
Overfitting ðð
ðĪð§
#MachineLearning #AI #DataScience #DeepLearning #Algorithm #NeuralNetworks
ðĪð§
#MachineLearning #AI #DataScience #DeepLearning #Algorithm #NeuralNetworks
âĪ4ð2
"Dive into Deep Learning" ððĪ is an open-source book that forms the mathematical foundation for large language models. ð§ ð
It covers linear algebra, mathematical analysis, probability theory, optimization methods, backpropagation, attention mechanisms, and transformer architectures. ð§Ūðð
The book progressively moves from classical neural networks and convolutional neural networks to modern transformers and practical techniques used in large language models. ððð§
It contains over 1,000 pages ð and provides clear explanations, practical examples, and exercises. â ð Making it one of the most comprehensive free resources for understanding the mathematical structure of modern artificial intelligence systems and language models. ðððĪ
arxiv.org/pdf/2106.11342 ð
#DeepLearning #AI #MachineLearning #NeuralNetworks #Transformers #OpenSource
It covers linear algebra, mathematical analysis, probability theory, optimization methods, backpropagation, attention mechanisms, and transformer architectures. ð§Ūðð
The book progressively moves from classical neural networks and convolutional neural networks to modern transformers and practical techniques used in large language models. ððð§
It contains over 1,000 pages ð and provides clear explanations, practical examples, and exercises. â ð Making it one of the most comprehensive free resources for understanding the mathematical structure of modern artificial intelligence systems and language models. ðððĪ
arxiv.org/pdf/2106.11342 ð
#DeepLearning #AI #MachineLearning #NeuralNetworks #Transformers #OpenSource
âĪ4
ð Master Binary Classification with Neural Networks! ð§ âĻ
Ever wondered how to build a neural network from scratch in Python using NumPy? ðð
Binary classification is at the heart of many machine learning applications. ðŊðĪ
Our super-detailed guide walks you through the entire process step by step. ðð
ðĄ Dive in and start building your own neural network today! ððĨ
https://tinztwinshub.com/data-science/a-beginners-guide-to-developing-an-artificial-neural-network-from-zero/
#MachineLearning #NeuralNetworks #Python #DataScience #AI #Tech
Ever wondered how to build a neural network from scratch in Python using NumPy? ðð
Binary classification is at the heart of many machine learning applications. ðŊðĪ
Our super-detailed guide walks you through the entire process step by step. ðð
ðĄ Dive in and start building your own neural network today! ððĨ
https://tinztwinshub.com/data-science/a-beginners-guide-to-developing-an-artificial-neural-network-from-zero/
#MachineLearning #NeuralNetworks #Python #DataScience #AI #Tech
ð4âĪ2
ðĨ Awesome open-source project to learn more about Transformer Models! ðĪâĻ
We found this interactive website that shows you visually how transformer models work. ðð
Transformer Explainer:
https://poloclub.github.io/transformer-explainer/
#TransformerModels #OpenSource #AI #MachineLearning #DataScience #Tech
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We found this interactive website that shows you visually how transformer models work. ðð
Transformer Explainer:
https://poloclub.github.io/transformer-explainer/
#TransformerModels #OpenSource #AI #MachineLearning #DataScience #Tech
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âĪ3ðĨ3ð2ðĐ1
Forwarded from Machine Learning with Python
Found an easy way to learn math for ML: Mathematics for Machine Learning ðð
This is a curated collection on GitHub, including books, research papers, video lectures, and basic materials on math for studying and reviewing the mathematical foundations of machine learning. ðð
It helps build a stronger knowledge base by bringing together trusted resources around topics that machine learning engineers constantly encounter: linear algebra, mathematical analysis, probability theory, statistics, information theory, matrix calculus, and deep learning mathematics. ð§ŪðĪ
Free public repository on GitHub. ðŧâĻ
https://github.com/dair-ai/Mathematics-for-ML
#MachineLearning #Mathematics #DataScience #Learning #GitHub #AI
This is a curated collection on GitHub, including books, research papers, video lectures, and basic materials on math for studying and reviewing the mathematical foundations of machine learning. ðð
It helps build a stronger knowledge base by bringing together trusted resources around topics that machine learning engineers constantly encounter: linear algebra, mathematical analysis, probability theory, statistics, information theory, matrix calculus, and deep learning mathematics. ð§ŪðĪ
Free public repository on GitHub. ðŧâĻ
https://github.com/dair-ai/Mathematics-for-ML
#MachineLearning #Mathematics #DataScience #Learning #GitHub #AI
GitHub
GitHub - dair-ai/Mathematics-for-ML: ð§Ū A collection of resources to learn mathematics for machine learning
ð§Ū A collection of resources to learn mathematics for machine learning - dair-ai/Mathematics-for-ML
âĪ6
ð A huge open-source course on AI Engineering from scratch
In the repository, we've collected:
â 435 lessons;
â 320+ hours of content;
â Python, TypeScript, and Rust;
â 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. ð
âïļ 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:
â 435 lessons;
â 320+ hours of content;
â Python, TypeScript, and Rust;
â 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. ð
âïļ Link to the repository
https://github.com/rohitg00/ai-engineering-from-scratch
#AI #MachineLearning #Python #Rust #OpenSource #Tech
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âĪ6ð1
Transformer implementations for vision, audio, and AI agents ðĪðïļðĩ
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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âĪ4ð2
Data leakage is one of the main reasons why ML demos look impressive... and then fail in production. ð
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. ð
Let's break it down below: ð
1. Data Leakage ðģïļ
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 âïļ
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 ðĻ
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 ðĩïļ
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 âïļ
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 ð
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 ð ïļ
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 ðĪ
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 â
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. ð
Let's break it down below: ð
1. Data Leakage ðģïļ
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 âïļ
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 ðĻ
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 ðĩïļ
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 âïļ
Wrong:
fit the scaler on all data â split the data â evaluate
Right:
split the data â fit the scaler only on the training set â 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 ð
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 ð ïļ
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 ðĪ
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 â
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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AI PYTHON ð
Youâve been invited to add the folder âAI PYTHON ðâ, which includes 14 chats.
âĪ4ð3
FREE MIT books on AI and Machine Learning: ððĪ
1. Foundations of Machine Learning cs.nyu.edu/~mohri/mlbook/
2. Understanding Deep Learning udlbook.github.io/udlbook/
3. Introduction to Machine Learning Systems âŊ Vol 1: mlsysbook.ai/vol1/assets/do âŊ 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
âŊ Part 1 : probml.github.io/pml-book/book1
âŊ 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 âŊ Vol 1: mlsysbook.ai/vol1/assets/do âŊ 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
âŊ Part 1 : probml.github.io/pml-book/book1
âŊ Part 2 : probml.github.io/pml-book/book2
#MIT #AI #MachineLearning #DeepLearning #ReinforcementLearning #FreeBooks
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âĪ6
Introduction to Deep RL and DQN
Link: https://www.dailydoseofds.com/rl-course-part-6/
ðĪ #DeepRL #DQN #ReinforcementLearning #AI #MachineLearning #DataScience
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ð Level up your AI & Data Science skills with HelloEncyclo â a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
â 13 courses live + 40+ coming soon
ðŊ One access, lifetime updates
ð Use code: PRESALE-BOOK-WAVE-2GFG
ð https://helloencyclo.com/?ref=HUSSEINSHEIKHO
Link: https://www.dailydoseofds.com/rl-course-part-6/
ðĪ #DeepRL #DQN #ReinforcementLearning #AI #MachineLearning #DataScience
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ð Level up your AI & Data Science skills with HelloEncyclo â a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
â 13 courses live + 40+ coming soon
ðŊ One access, lifetime updates
ð Use code: PRESALE-BOOK-WAVE-2GFG
ð https://helloencyclo.com/?ref=HUSSEINSHEIKHO
âĪ6
Optimizing the model's performance through Prompt Tuning with the PEFT library.
âĻ Full-fledged fine-tuning of language models requires a huge amount of video memory and completely overwrites the network's weights. We will apply the Prompt Tuning method (retraining virtual token prompts), which freezes the main model and adjusts only a tiny matrix of virtual embeddings. This allows adapting AI to a narrow task using a regular user's graphics card and without the risk of destroying the neural network's basic knowledge.
ðĶ First, we will install the necessary libraries for working with transformers and effective fine-tuning methods (PEFT).
â The packages have been successfully installed in the system and are ready for configuring lightweight training. We will create a basic Prompt Tuning configuration for training just twenty virtual tokens instead of billions of model parameters.
ð The configuration is initialized and links the text prompt to the trainable virtual embeddings. We will wrap the base model in a PEFT container to freeze the main weights and leave only the new tokens available for gradient descent.
ð The model is ready for training, and the percentage of active parameters will be displayed on the screen (usually less than 0.01%).
ð Expected output: PEFT Setup: OK
ðĄ Prompt Tuning â an ideal choice when you need to train a model for many different customers or tasks simultaneously. Instead of gigabyte-sized copies of neural networks, you store only lightweight configuration files weighing a few kilobytes, dynamically substituting them at inference.
#PromptTuning #PEFT #AI #MachineLearning #DeepLearning #DataScience
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ð Level up your AI & Data Science skills with HelloEncyclo â a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
â 13 courses live + 40+ coming soon
ðŊ One access, lifetime updates
ð Use code: PRESALE-BOOK-WAVE-2GFG
ð https://helloencyclo.com/?ref=HUSSEINSHEIKHO
âĻ Full-fledged fine-tuning of language models requires a huge amount of video memory and completely overwrites the network's weights. We will apply the Prompt Tuning method (retraining virtual token prompts), which freezes the main model and adjusts only a tiny matrix of virtual embeddings. This allows adapting AI to a narrow task using a regular user's graphics card and without the risk of destroying the neural network's basic knowledge.
ðĶ First, we will install the necessary libraries for working with transformers and effective fine-tuning methods (PEFT).
pip install torch transformers peft
â The packages have been successfully installed in the system and are ready for configuring lightweight training. We will create a basic Prompt Tuning configuration for training just twenty virtual tokens instead of billions of model parameters.
from peft import PromptTuningConfig, PromptTuningInit, get_peft_model
from transformers import AutoModelForCausalLM
peft_config = PromptTuningConfig(
task_type="CAUSAL_LM",
prompt_tuning_init=PromptTuningInit.TEXT,
num_virtual_tokens=20,
prompt_tuning_init_text="Classify the sentiment of this text:",
tokenizer_name_or_path="gpt2"
)
ð The configuration is initialized and links the text prompt to the trainable virtual embeddings. We will wrap the base model in a PEFT container to freeze the main weights and leave only the new tokens available for gradient descent.
base_model = AutoModelForCausalLM.from_pretrained("gpt2")
peft_model = get_peft_model(base_model, peft_config)
peft_model.print_trainable_parameters()ð The model is ready for training, and the percentage of active parameters will be displayed on the screen (usually less than 0.01%).
python3 -c "from peft import PromptTuningConfig; print('PEFT Setup: OK')"ð Expected output: PEFT Setup: OK
pip uninstall peft -y
ðĄ Prompt Tuning â an ideal choice when you need to train a model for many different customers or tasks simultaneously. Instead of gigabyte-sized copies of neural networks, you store only lightweight configuration files weighing a few kilobytes, dynamically substituting them at inference.
#PromptTuning #PEFT #AI #MachineLearning #DeepLearning #DataScience
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âĪ4ðĨ1
If you want to finally understand how neural networks actually learn, I recommend these notes from Stanford CS224N. ð§
"Computing Neural Network Gradients" explains the calculation of gradients and backpropagation without black-box formulas. ð
Inside:
âĒ Chain Rule
âĒ Computational Graphs
âĒ Vectorized derivatives
âĒ Efficient gradient calculation
âĒ Step-by-step examples with formula analysis
Many people use PyTorch or TensorFlow every day, but never understood what happens after calling .backward(). ðĨ
These notes just fill this gap. ð ïļ
PDF:
https://web.stanford.edu/class/cs224n/readings/gradient-notes.pdf
#NeuralNetworks #DeepLearning #StanfordCS #Backpropagation #MachineLearning #AIResearch
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"Computing Neural Network Gradients" explains the calculation of gradients and backpropagation without black-box formulas. ð
Inside:
âĒ Chain Rule
âĒ Computational Graphs
âĒ Vectorized derivatives
âĒ Efficient gradient calculation
âĒ Step-by-step examples with formula analysis
Many people use PyTorch or TensorFlow every day, but never understood what happens after calling .backward(). ðĨ
These notes just fill this gap. ð ïļ
PDF:
https://web.stanford.edu/class/cs224n/readings/gradient-notes.pdf
#NeuralNetworks #DeepLearning #StanfordCS #Backpropagation #MachineLearning #AIResearch
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ð Level up your AI & Data Science skills with HelloEncyclo â a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
â 13 courses live + 40+ coming soon
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âĪ2
Forwarded from Machine Learning with Python
Data Science Interview Questions.pdf
1.4 MB
Data Science Interview Questions
ðĄ Here is your curated list for Data Science interviews!
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#DataScience #AI #MachineLearning #LLM #TechJobs #InterviewPrep
ðĄ Here is your curated list for Data Science interviews!
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#DataScience #AI #MachineLearning #LLM #TechJobs #InterviewPrep
âĪ4
Forwarded from Machine Learning with Python
A new collection of free courses has been added:
ð https://github.com/dair-ai/ML-Course-Notes
Those studying ML through dozens of random tabs and unclosed playlists may find this repository useful for organizing their learning. ð
Machine Learning Course Notes is an open collection of notes on machine learning, NLP, and AI, compiled around full-fledged courses, not just individual videos. ð§
What's inside:
âĒ Courses from the Machine Learning Specialization, MIT 6.S191, CMU Neural Nets for NLP, CS224N, CS25, and others
âĒ A table with lectures, descriptions, videos, notes, and authors
âĒ Links to the original lectures and accompanying notes
âĒ WIP markers for incomplete materials
âĒ Instructions for contributors on adding and improving notes
The idea was appreciated. ð
Instead of another collection of hundreds of links, a course map has been created where one can systematically go through the material without getting lost after a week of studying. ðšïļ
#MachineLearning #AI #DataScience #TechCommunity #LearningResources #OpenSource
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ð https://github.com/dair-ai/ML-Course-Notes
Those studying ML through dozens of random tabs and unclosed playlists may find this repository useful for organizing their learning. ð
Machine Learning Course Notes is an open collection of notes on machine learning, NLP, and AI, compiled around full-fledged courses, not just individual videos. ð§
What's inside:
âĒ Courses from the Machine Learning Specialization, MIT 6.S191, CMU Neural Nets for NLP, CS224N, CS25, and others
âĒ A table with lectures, descriptions, videos, notes, and authors
âĒ Links to the original lectures and accompanying notes
âĒ WIP markers for incomplete materials
âĒ Instructions for contributors on adding and improving notes
The idea was appreciated. ð
Instead of another collection of hundreds of links, a course map has been created where one can systematically go through the material without getting lost after a week of studying. ðšïļ
#MachineLearning #AI #DataScience #TechCommunity #LearningResources #OpenSource
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ð Level up your AI & Data Science skills with HelloEncyclo â a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
â 13 courses live + 40+ coming soon
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ð Use code: PRESALE-BOOK-WAVE-2GFG
ð https://helloencyclo.com/?ref=HUSSEINSHEIKHO
GitHub
GitHub - dair-ai/ML-Course-Notes: ð Sharing machine learning course / lecture notes.
ð Sharing machine learning course / lecture notes. - dair-ai/ML-Course-Notes
âĪ3
If you already have 200 open tabs with courses, articles, and GitHub repositories on ML, this repository might save the situation a bit. ð
Awesome Machine Learning Resources is a huge collection of sub-collections on machine learning, deep learning, and AI. ðĪ
Instead of endless Google searches, everything is organized into categories:
âĒ fundamentals of machine learning
âĒ neural networks and modern architectures
âĒ tasks and application areas
âĒ datasets
âĒ libraries and tools
âĒ fairness and AI ethics
âĒ production ML and MLOps
Each link has a short description, so you can quickly understand whether it's worth opening it or skipping it. ð
I particularly liked that the authors mark abandoned collections with an icon if they haven't been updated in over a year. â ïļ
https://github.com/ZhiningLiu1998/awesome-machine-learning-resources
#MachineLearning #DeepLearning #AI #MLOps #DataScience #TechResources
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â 13 courses live + 40+ coming soon
ðŊ One access, lifetime updates
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Awesome Machine Learning Resources is a huge collection of sub-collections on machine learning, deep learning, and AI. ðĪ
Instead of endless Google searches, everything is organized into categories:
âĒ fundamentals of machine learning
âĒ neural networks and modern architectures
âĒ tasks and application areas
âĒ datasets
âĒ libraries and tools
âĒ fairness and AI ethics
âĒ production ML and MLOps
Each link has a short description, so you can quickly understand whether it's worth opening it or skipping it. ð
I particularly liked that the authors mark abandoned collections with an icon if they haven't been updated in over a year. â ïļ
https://github.com/ZhiningLiu1998/awesome-machine-learning-resources
#MachineLearning #DeepLearning #AI #MLOps #DataScience #TechResources
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ð Level up your AI & Data Science skills with HelloEncyclo â a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
â 13 courses live + 40+ coming soon
ðŊ One access, lifetime updates
ð Use code: PRESALE-BOOK-WAVE-2GFG
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âĪ2
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Someone spent several months manually writing a 200-page guide on mathematics and the basics of machine learning. ð
No marketing fluff or endless links between articles. Just an attempt to gather all the most important things in one place. ðŊ
Inside:
âĒ neural networks: backpropagation, SGD, Adam, BatchNorm; âïļ
âĒ classic ML: SVM, Gradient Boosting, K-Means, PCA; ð
âĒ hardware for AI: Tensor Cores, Systolic Arrays, CUDA; ðĨïļ
âĒ transformers: Multi-Head Attention, KV Cache, LoRA; ð§
âĒ computer vision: ViT, CNN, MAE, IoU, NMS, VLM; ðïļ
âĒ agent systems: ReAct, memory, orchestration, OpenClaw. ðĪ
The author describes it as the material he would have wanted to receive himself several years ago. ð°ïļ
And yes, the entire guide is distributed free of charge. ð
https://www.arjunvirk.com/writing/ml-guide
#MachineLearning #AI #DeepLearning #DataScience #NeuralNetworks #Tech
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â 13 courses live + 40+ coming soon
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No marketing fluff or endless links between articles. Just an attempt to gather all the most important things in one place. ðŊ
Inside:
âĒ neural networks: backpropagation, SGD, Adam, BatchNorm; âïļ
âĒ classic ML: SVM, Gradient Boosting, K-Means, PCA; ð
âĒ hardware for AI: Tensor Cores, Systolic Arrays, CUDA; ðĨïļ
âĒ transformers: Multi-Head Attention, KV Cache, LoRA; ð§
âĒ computer vision: ViT, CNN, MAE, IoU, NMS, VLM; ðïļ
âĒ agent systems: ReAct, memory, orchestration, OpenClaw. ðĪ
The author describes it as the material he would have wanted to receive himself several years ago. ð°ïļ
And yes, the entire guide is distributed free of charge. ð
https://www.arjunvirk.com/writing/ml-guide
#MachineLearning #AI #DeepLearning #DataScience #NeuralNetworks #Tech
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â 13 courses live + 40+ coming soon
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âĪ3
ð A large collection of AI projects for practice
We found a repository that will help you move from theory to real development of AI applications.
Inside are dozens of ready-made projects: AI analytics, RAG systems, OCR applications, code review agents, travel assistants, and much more.
âïļ Link to GitHub: https://github.com/Sumanth077/Hands-On-AI-Engineering
#AI #MachineLearning #Python #DataScience #OpenSource #Tech
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ð Level up your AI & Data Science skills with HelloEncyclo â a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
â 13 courses live + 40+ coming soon
ðŊ One access, lifetime updates
ð Use code: PRESALE-BOOK-WAVE-2GFG
ð https://helloencyclo.com/?ref=HUSSEINSHEIKHO
We found a repository that will help you move from theory to real development of AI applications.
Inside are dozens of ready-made projects: AI analytics, RAG systems, OCR applications, code review agents, travel assistants, and much more.
âïļ Link to GitHub: https://github.com/Sumanth077/Hands-On-AI-Engineering
#AI #MachineLearning #Python #DataScience #OpenSource #Tech
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ð Level up your AI & Data Science skills with HelloEncyclo â a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
â 13 courses live + 40+ coming soon
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âĪ5
Multi-Label Text Classification with Scikit-LLM ð
In this article, you will learn how to perform multi-label text classification using large language models and the scikit-LLM library, without the need for labeled training data or complex model training. ð
Topics we will cover include:
What multi-label classification is and why it matters for nuanced text analysis. ð
How to set up and configure scikit-LLM with a free, open-source LLM from Groq for zero-shot inference. âïļ
How to load a real-world dataset and run multi-label sentiment predictions using a familiar scikit-learn-style workflow. ð
Read: https://machinelearningmastery.com/multi-label-text-classification-with-scikit-llm/ ð
#ScikitLLM #TextClassification #LLM #MachineLearning #ZeroShot #DataScience
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â 13 courses live + 40+ coming soon
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In this article, you will learn how to perform multi-label text classification using large language models and the scikit-LLM library, without the need for labeled training data or complex model training. ð
Topics we will cover include:
What multi-label classification is and why it matters for nuanced text analysis. ð
How to set up and configure scikit-LLM with a free, open-source LLM from Groq for zero-shot inference. âïļ
How to load a real-world dataset and run multi-label sentiment predictions using a familiar scikit-learn-style workflow. ð
Read: https://machinelearningmastery.com/multi-label-text-classification-with-scikit-llm/ ð
#ScikitLLM #TextClassification #LLM #MachineLearning #ZeroShot #DataScience
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âĪ2
Forwarded from Machine Learning with Python
10 GitHub repositories that are worth checking out for an AI engineer ðĪ
1. Hands-On AI Engineering ð ïļ
A collection of AI applications and agent systems with practical use cases of LLM.
ð https://github.com/Sumanth077/Hands-On-AI-Engineering
2. Hands-On Large Language Models ð
Full code from the book Hands-On Large Language Models: from basics to fine-tuning.
ð https://github.com/HandsOnLLM/Hands-On-Large-Language-Models
3. AI Agents for Beginners ð
A free course from Microsoft with 11 lessons on creating AI agents.
ð https://github.com/microsoft/ai-agents-for-beginners
4. GenAI Agents ðĪ
A large collection of tutorials and implementations of agent systems.
ð https://github.com/NirDiamant/GenAI_Agents
5. Made With ML ð
About the development, deployment, and support of production-ready ML systems.
ð https://github.com/GokuMohandas/Made-With-ML
6. Learn Harness Engineering âïļ
A practical course on Harness Engineering for AI agents.
ð https://github.com/walkinglabs/learn-harness-engineering
7. AutoResearch ðŽ
Autonomous cycles of ML experiments from Andrej Karpathy.
ð https://github.com/karpathy/autoresearch
8. Designing Machine Learning Systems ð
Notes and materials from Chip Huyen's book.
ð https://github.com/chiphuyen/dmls-book
9. Awesome LLM Inference âĄ
A collection of materials on LLM inference: Flash Attention, KV Cache, quantization, and more.
ð https://github.com/xlite-dev/Awesome-LLM-Inference
10. LLM Course ðšïļ
A practical course on LLM with a roadmap and Colab notebooks.
ð https://github.com/mlabonne/llm-course
#AI #MachineLearning #LLM #DataScience #Tech #GitHub
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1. Hands-On AI Engineering ð ïļ
A collection of AI applications and agent systems with practical use cases of LLM.
ð https://github.com/Sumanth077/Hands-On-AI-Engineering
2. Hands-On Large Language Models ð
Full code from the book Hands-On Large Language Models: from basics to fine-tuning.
ð https://github.com/HandsOnLLM/Hands-On-Large-Language-Models
3. AI Agents for Beginners ð
A free course from Microsoft with 11 lessons on creating AI agents.
ð https://github.com/microsoft/ai-agents-for-beginners
4. GenAI Agents ðĪ
A large collection of tutorials and implementations of agent systems.
ð https://github.com/NirDiamant/GenAI_Agents
5. Made With ML ð
About the development, deployment, and support of production-ready ML systems.
ð https://github.com/GokuMohandas/Made-With-ML
6. Learn Harness Engineering âïļ
A practical course on Harness Engineering for AI agents.
ð https://github.com/walkinglabs/learn-harness-engineering
7. AutoResearch ðŽ
Autonomous cycles of ML experiments from Andrej Karpathy.
ð https://github.com/karpathy/autoresearch
8. Designing Machine Learning Systems ð
Notes and materials from Chip Huyen's book.
ð https://github.com/chiphuyen/dmls-book
9. Awesome LLM Inference âĄ
A collection of materials on LLM inference: Flash Attention, KV Cache, quantization, and more.
ð https://github.com/xlite-dev/Awesome-LLM-Inference
10. LLM Course ðšïļ
A practical course on LLM with a roadmap and Colab notebooks.
ð https://github.com/mlabonne/llm-course
#AI #MachineLearning #LLM #DataScience #Tech #GitHub
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ð Level up your AI & Data Science skills with HelloEncyclo â a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
â 13 courses live + 40+ coming soon
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ð Use code: PRESALE-BOOK-WAVE-2GFG
ð https://helloencyclo.com/?ref=HUSSEINSHEIKHO
âĪ4