🚀 𝐓𝐇𝐄 𝐀𝐈 𝐀𝐑𝐂𝐇𝐈𝐓𝐄𝐂𝐓𝐔𝐑𝐄 𝐎𝐏𝐓𝐈𝐌𝐈𝐙𝐄𝐃 — 𝐆𝐀𝐓𝐄𝐃 𝐑𝐄𝐂𝐔𝐑𝐑𝐄𝐍𝐓 𝐔𝐍𝐈𝐓𝐒 (𝐆𝐑𝐔) 🌟
GRUs are a simplified yet powerful variation of the LSTM architecture. 🧠 Introduced to solve the vanishing gradient problem while reducing computational overhead, GRUs merge gates to create a more efficient "memory" system. ⚡️ They are the go-to choice when you need the performance of an LSTM but have limited compute resources or smaller datasets. 📉📈
𝟏. 𝐂𝐎𝐑𝐄 𝐀𝐑𝐂𝐇𝐈𝐓𝐄𝐂𝐓𝐔𝐑𝐄 & 𝐖𝐎𝐑𝐊𝐅𝐋𝐎𝐖 🔧
The GRU streamlines the gating process by combining the cell state and hidden state. 🔄
𝐔𝐩𝐝𝐚𝐭𝐞 𝐆𝐚𝐭𝐞: Determines how much of the previous memory to keep and how much new information to add. 📥➕📤
𝐑𝐞𝐬𝐞𝐭 𝐆𝐚𝐭𝐞: Decides how much of the past information to forget before calculating the next state. 🗑⏳
𝐂𝐚𝐧𝐝𝐢𝐝𝐚𝐭𝐞 𝐀𝐜𝐭𝐢𝐯𝐚𝐭𝐢𝐨𝐧: A "hidden" layer that suggests a potential update based on the current input and the reset memory. 🧩🔍
𝟐. 𝐊𝐄𝐘 𝐀𝐃𝐕𝐀𝐍𝐓𝐀𝐆𝐄𝐒 𝐎𝐕𝐄𝐑 𝐋𝐒𝐓𝐌 🚀
Why choose GRU over its predecessor, the LSTM? 🤔
𝐅𝐞𝐰𝐞𝐫 𝐆𝐚𝐭𝐞𝐬: 2 instead of 3, GRUs train faster and use less memory. 🏎💨
𝐋𝐞𝐬𝐬 𝐏𝐚𝐫𝐚𝐦𝐞𝐭𝐞𝐫𝐬: By merging the cell and hidden states, information flow is more direct. 📉📊
𝐁𝐞𝐭𝐭𝐞𝐫 𝐎𝐧 𝐒𝐦𝐚𝐥𝐥 𝐃𝐚𝐭𝐚𝐬𝐞𝐭𝐬: GRUs often outperform LSTMs due to having fewer parameters (reducing the risk of overfitting). 🎯📉
𝟑. 𝐂𝐎𝐌𝐏𝐀𝐑𝐀𝐓𝐈𝐕𝐄 𝐌𝐎𝐃𝐄𝐋𝐒 📊
𝐑𝐍𝐍: The basic loop; prone to short-term memory loss. 🔄❌
𝐋𝐒𝐓𝐌: The "Heavyweight"; highly accurate but computationally expensive. 🏋️♂️🔋
𝐆𝐑𝐔: The "Lightweight"; optimized for speed and modern efficiency. 🪶⚡️
𝟒. 𝐑𝐄𝐀𝐋-𝐖𝐎𝐑𝐋𝐃 𝐀𝐏𝐏𝐋𝐈𝐂𝐀𝐓𝐈𝐎𝐍𝐒 🌍
GRUs excel in environments where latency matters: ⏱️
𝐕𝐨𝐢𝐜𝐞 𝐓𝐨 𝐓𝐞𝐱𝐭: Converting voice to text with minimal delay. 🎙📝
𝐈𝐨𝐓 & 𝐄𝐝𝐠𝐞 𝐃𝐞𝐯𝐢𝐜𝐞𝐬: Running sequential models on low-power hardware (like smart sensors). 📡🏠
𝐌𝐮𝐬𝐢𝐜 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧: Learning the structure of melodies and rhythm for AI-composed audio. 🎵🎹
𝟓. 𝐓𝐇𝐄 𝐌𝐀𝐓𝐇 𝐁𝐄𝐇𝐈𝐍𝐃 𝐆𝐑𝐔𝐒 🧮
𝐔𝐩𝐝𝐚𝐭𝐞 𝐆𝐚𝐭𝐞: Unlike LSTMs, which use separate input and forget gates, GRU update handles both simultaneously. 🔄🔄
𝐑𝐞𝐬𝐞𝐭 𝐆𝐚𝐭𝐞: Both gates use sigmoid activations to regulate the information flow between 0 and 1. 📈📉
𝐂𝐚𝐧𝐝𝐢𝐝𝐚𝐭𝐞 𝐀𝐜𝐭𝐢𝐯𝐚𝐭𝐢𝐨𝐧: Used to calculate the candidate hidden state before it is merged into the final output. 🧩➕🏁
𝟔. 𝐆𝐑𝐔 𝐄𝐒𝐒𝐄𝐍𝐓𝐈𝐀𝐋𝐒 📚
𝐑𝐞𝐬𝐞𝐭: Decide how much of the past to ignore. 🙈
𝐂𝐚𝐧𝐝𝐢𝐝𝐚𝐭𝐞: Create a potential new memory step. 🆕
𝐔𝐩𝐝𝐚𝐭𝐞: Blend the old state and the new candidate based on the update gate's weight. ⚖️
𝐎𝐮𝐭𝐩𝐮𝐭: Pass the new hidden state to the next time step. 🚪🏃♂️
"GRUs taught machines that sometimes, simplicity is the ultimate sophistication in intelligence." 🤖✨
#GRU #AI #MachineLearning #DeepLearning #NeuralNetworks #Tech
GRUs are a simplified yet powerful variation of the LSTM architecture. 🧠 Introduced to solve the vanishing gradient problem while reducing computational overhead, GRUs merge gates to create a more efficient "memory" system. ⚡️ They are the go-to choice when you need the performance of an LSTM but have limited compute resources or smaller datasets. 📉📈
𝟏. 𝐂𝐎𝐑𝐄 𝐀𝐑𝐂𝐇𝐈𝐓𝐄𝐂𝐓𝐔𝐑𝐄 & 𝐖𝐎𝐑𝐊𝐅𝐋𝐎𝐖 🔧
The GRU streamlines the gating process by combining the cell state and hidden state. 🔄
𝐔𝐩𝐝𝐚𝐭𝐞 𝐆𝐚𝐭𝐞: Determines how much of the previous memory to keep and how much new information to add. 📥➕📤
𝐑𝐞𝐬𝐞𝐭 𝐆𝐚𝐭𝐞: Decides how much of the past information to forget before calculating the next state. 🗑⏳
𝐂𝐚𝐧𝐝𝐢𝐝𝐚𝐭𝐞 𝐀𝐜𝐭𝐢𝐯𝐚𝐭𝐢𝐨𝐧: A "hidden" layer that suggests a potential update based on the current input and the reset memory. 🧩🔍
𝟐. 𝐊𝐄𝐘 𝐀𝐃𝐕𝐀𝐍𝐓𝐀𝐆𝐄𝐒 𝐎𝐕𝐄𝐑 𝐋𝐒𝐓𝐌 🚀
Why choose GRU over its predecessor, the LSTM? 🤔
𝐅𝐞𝐰𝐞𝐫 𝐆𝐚𝐭𝐞𝐬: 2 instead of 3, GRUs train faster and use less memory. 🏎💨
𝐋𝐞𝐬𝐬 𝐏𝐚𝐫𝐚𝐦𝐞𝐭𝐞𝐫𝐬: By merging the cell and hidden states, information flow is more direct. 📉📊
𝐁𝐞𝐭𝐭𝐞𝐫 𝐎𝐧 𝐒𝐦𝐚𝐥𝐥 𝐃𝐚𝐭𝐚𝐬𝐞𝐭𝐬: GRUs often outperform LSTMs due to having fewer parameters (reducing the risk of overfitting). 🎯📉
𝟑. 𝐂𝐎𝐌𝐏𝐀𝐑𝐀𝐓𝐈𝐕𝐄 𝐌𝐎𝐃𝐄𝐋𝐒 📊
𝐑𝐍𝐍: The basic loop; prone to short-term memory loss. 🔄❌
𝐋𝐒𝐓𝐌: The "Heavyweight"; highly accurate but computationally expensive. 🏋️♂️🔋
𝐆𝐑𝐔: The "Lightweight"; optimized for speed and modern efficiency. 🪶⚡️
𝟒. 𝐑𝐄𝐀𝐋-𝐖𝐎𝐑𝐋𝐃 𝐀𝐏𝐏𝐋𝐈𝐂𝐀𝐓𝐈𝐎𝐍𝐒 🌍
GRUs excel in environments where latency matters: ⏱️
𝐕𝐨𝐢𝐜𝐞 𝐓𝐨 𝐓𝐞𝐱𝐭: Converting voice to text with minimal delay. 🎙📝
𝐈𝐨𝐓 & 𝐄𝐝𝐠𝐞 𝐃𝐞𝐯𝐢𝐜𝐞𝐬: Running sequential models on low-power hardware (like smart sensors). 📡🏠
𝐌𝐮𝐬𝐢𝐜 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧: Learning the structure of melodies and rhythm for AI-composed audio. 🎵🎹
𝟓. 𝐓𝐇𝐄 𝐌𝐀𝐓𝐇 𝐁𝐄𝐇𝐈𝐍𝐃 𝐆𝐑𝐔𝐒 🧮
𝐔𝐩𝐝𝐚𝐭𝐞 𝐆𝐚𝐭𝐞: Unlike LSTMs, which use separate input and forget gates, GRU update handles both simultaneously. 🔄🔄
𝐑𝐞𝐬𝐞𝐭 𝐆𝐚𝐭𝐞: Both gates use sigmoid activations to regulate the information flow between 0 and 1. 📈📉
𝐂𝐚𝐧𝐝𝐢𝐝𝐚𝐭𝐞 𝐀𝐜𝐭𝐢𝐯𝐚𝐭𝐢𝐨𝐧: Used to calculate the candidate hidden state before it is merged into the final output. 🧩➕🏁
𝟔. 𝐆𝐑𝐔 𝐄𝐒𝐒𝐄𝐍𝐓𝐈𝐀𝐋𝐒 📚
𝐑𝐞𝐬𝐞𝐭: Decide how much of the past to ignore. 🙈
𝐂𝐚𝐧𝐝𝐢𝐝𝐚𝐭𝐞: Create a potential new memory step. 🆕
𝐔𝐩𝐝𝐚𝐭𝐞: Blend the old state and the new candidate based on the update gate's weight. ⚖️
𝐎𝐮𝐭𝐩𝐮𝐭: Pass the new hidden state to the next time step. 🚪🏃♂️
"GRUs taught machines that sometimes, simplicity is the ultimate sophistication in intelligence." 🤖✨
#GRU #AI #MachineLearning #DeepLearning #NeuralNetworks #Tech
❤2
"Dive into Deep Learning" 📘🤖 is an open-source book that forms the mathematical foundation for large language models. 🧠📐
It covers linear algebra, mathematical analysis, probability theory, optimization methods, backpropagation, attention mechanisms, and transformer architectures. 🧮📉🔄
The book progressively moves from classical neural networks and convolutional neural networks to modern transformers and practical techniques used in large language models. 🚀🔗🧠
It contains over 1,000 pages 📖 and provides clear explanations, practical examples, and exercises. ✅📝 Making it one of the most comprehensive free resources for understanding the mathematical structure of modern artificial intelligence systems and language models. 🌐🔍🤖
arxiv.org/pdf/2106.11342 🔗
#DeepLearning #AI #MachineLearning #NeuralNetworks #Transformers #OpenSource
It covers linear algebra, mathematical analysis, probability theory, optimization methods, backpropagation, attention mechanisms, and transformer architectures. 🧮📉🔄
The book progressively moves from classical neural networks and convolutional neural networks to modern transformers and practical techniques used in large language models. 🚀🔗🧠
It contains over 1,000 pages 📖 and provides clear explanations, practical examples, and exercises. ✅📝 Making it one of the most comprehensive free resources for understanding the mathematical structure of modern artificial intelligence systems and language models. 🌐🔍🤖
arxiv.org/pdf/2106.11342 🔗
#DeepLearning #AI #MachineLearning #NeuralNetworks #Transformers #OpenSource
❤4
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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❤6
Optimizing the model's performance through Prompt Tuning with the PEFT library.
✨ 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.
📦 First, we will install the necessary libraries for working with transformers and effective fine-tuning methods (PEFT).
✅ 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.
🔄 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.
🚀 The model is ready for training, and the percentage of active parameters will be displayed on the screen (usually less than 0.01%).
📝 Expected output: PEFT Setup: OK
💡 Prompt Tuning — 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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✨ 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.
📦 First, we will install the necessary libraries for working with transformers and effective fine-tuning methods (PEFT).
pip install torch transformers peft
✅ 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"
)
🔄 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()🚀 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')"📝 Expected output: PEFT Setup: OK
pip uninstall peft -y
💡 Prompt Tuning — 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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You’ve been invited to add the folder “AI PYTHON 🌟”, which includes 15 chats.
❤4🔥1
If you want to finally understand how neural networks actually learn, I recommend these notes from Stanford CS224N. 🧠
"Computing Neural Network Gradients" explains the calculation of gradients and backpropagation without black-box formulas. 📉
Inside:
• Chain Rule
• Computational Graphs
• Vectorized derivatives
• Efficient gradient calculation
• Step-by-step examples with formula analysis
Many people use PyTorch or TensorFlow every day, but never understood what happens after calling .backward(). 🔥
These notes just fill this gap. 🛠️
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. 📉
Inside:
• Chain Rule
• Computational Graphs
• Vectorized derivatives
• Efficient gradient calculation
• Step-by-step examples with formula analysis
Many people use PyTorch or TensorFlow every day, but never understood what happens after calling .backward(). 🔥
These notes just fill this gap. 🛠️
PDF:
https://web.stanford.edu/class/cs224n/readings/gradient-notes.pdf
#NeuralNetworks #DeepLearning #StanfordCS #Backpropagation #MachineLearning #AIResearch
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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.
✅ 13 courses live + 40+ coming soon
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❤2
Parallax: A Parameterized Local Linear Attention That Keeps Softmax and Adds a Learned Covariance Correction Branch 🧠✨
The Transformer’s attention mechanism has barely changed since 2017. Most efficiency work has tried to replace softmax attention outright. A new paper takes a different route. It keeps softmax attention and bolts on a correction branch. 🔄
A team of researchers from Northwestern University, Tilde Research, and University of Washington introduce a parameterized Local Linear Attention called ‘Parallax’ that scales to LLM pretraining and codesigns with Muon. 🎓
Parallax does not chase efficiency by cutting compute. It adds compute deliberately, then makes that compute cheaper to run on modern GPUs. 💻⚡
More: https://www.marktechpost.com/2026/05/31/parallax-a-parameterized-local-linear-attention-that-keeps-softmax-and-adds-a-learned-covariance-correction-branch/
#Parallax #LLM #AI #DeepLearning #Transformer #TechNews
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The Transformer’s attention mechanism has barely changed since 2017. Most efficiency work has tried to replace softmax attention outright. A new paper takes a different route. It keeps softmax attention and bolts on a correction branch. 🔄
A team of researchers from Northwestern University, Tilde Research, and University of Washington introduce a parameterized Local Linear Attention called ‘Parallax’ that scales to LLM pretraining and codesigns with Muon. 🎓
Parallax does not chase efficiency by cutting compute. It adds compute deliberately, then makes that compute cheaper to run on modern GPUs. 💻⚡
More: https://www.marktechpost.com/2026/05/31/parallax-a-parameterized-local-linear-attention-that-keeps-softmax-and-adds-a-learned-covariance-correction-branch/
#Parallax #LLM #AI #DeepLearning #Transformer #TechNews
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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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If you already have 200 open tabs with courses, articles, and GitHub repositories on ML, this repository might save the situation a bit. 😅
Awesome Machine Learning Resources is a huge collection of sub-collections on machine learning, deep learning, and AI. 🤖
Instead of endless Google searches, everything is organized into categories:
• fundamentals of machine learning
• neural networks and modern architectures
• tasks and application areas
• datasets
• libraries and tools
• fairness and AI ethics
• production ML and MLOps
Each link has a short description, so you can quickly understand whether it's worth opening it or skipping it. 📝
I particularly liked that the authors mark abandoned collections with an icon if they haven't been updated in over a year. ⚠️
https://github.com/ZhiningLiu1998/awesome-machine-learning-resources
#MachineLearning #DeepLearning #AI #MLOps #DataScience #TechResources
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Awesome Machine Learning Resources is a huge collection of sub-collections on machine learning, deep learning, and AI. 🤖
Instead of endless Google searches, everything is organized into categories:
• fundamentals of machine learning
• neural networks and modern architectures
• tasks and application areas
• datasets
• libraries and tools
• fairness and AI ethics
• production ML and MLOps
Each link has a short description, so you can quickly understand whether it's worth opening it or skipping it. 📝
I particularly liked that the authors mark abandoned collections with an icon if they haven't been updated in over a year. ⚠️
https://github.com/ZhiningLiu1998/awesome-machine-learning-resources
#MachineLearning #DeepLearning #AI #MLOps #DataScience #TechResources
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Someone spent several months manually writing a 200-page guide on mathematics and the basics of machine learning. 📘
No marketing fluff or endless links between articles. Just an attempt to gather all the most important things in one place. 🎯
Inside:
• neural networks: backpropagation, SGD, Adam, BatchNorm; ⚙️
• classic ML: SVM, Gradient Boosting, K-Means, PCA; 📊
• hardware for AI: Tensor Cores, Systolic Arrays, CUDA; 🖥️
• transformers: Multi-Head Attention, KV Cache, LoRA; 🧠
• computer vision: ViT, CNN, MAE, IoU, NMS, VLM; 👁️
• agent systems: ReAct, memory, orchestration, OpenClaw. 🤖
The author describes it as the material he would have wanted to receive himself several years ago. 🕰️
And yes, the entire guide is distributed free of charge. 🆓
https://www.arjunvirk.com/writing/ml-guide
#MachineLearning #AI #DeepLearning #DataScience #NeuralNetworks #Tech
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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. 🎯
Inside:
• neural networks: backpropagation, SGD, Adam, BatchNorm; ⚙️
• classic ML: SVM, Gradient Boosting, K-Means, PCA; 📊
• hardware for AI: Tensor Cores, Systolic Arrays, CUDA; 🖥️
• transformers: Multi-Head Attention, KV Cache, LoRA; 🧠
• computer vision: ViT, CNN, MAE, IoU, NMS, VLM; 👁️
• agent systems: ReAct, memory, orchestration, OpenClaw. 🤖
The author describes it as the material he would have wanted to receive himself several years ago. 🕰️
And yes, the entire guide is distributed free of charge. 🆓
https://www.arjunvirk.com/writing/ml-guide
#MachineLearning #AI #DeepLearning #DataScience #NeuralNetworks #Tech
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Forwarded from Machine Learning with Python
🎓 A Free AI Course for Beginners by Microsoft
For those just getting into artificial intelligence, Microsoft offers a free course.
It runs for 12 weeks and includes 24 lessons with theory, hands-on assignments, labs, and quizzes.
The curriculum covers neural networks and deep learning, computer vision, natural language processing, genetic algorithms, and AI ethics. For practice, it uses the two main ML frameworks—TensorFlow and PyTorch.
Each lesson follows the same structure: first, reading material, then a Jupyter notebook with code, and for some topics, a lab. The course is in English but has been translated into dozens of languages.
➡️ All materials and links are on GitHub
https://github.com/microsoft/AI-For-Beginners/blob/main/translations/ru/README.md
What's your AI level right now?
❤️ — Advanced user
🔥 — Almost zero
#AICourse #Microsoft #DeepLearning #TensorFlow #PyTorch #MachineLearning
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For those just getting into artificial intelligence, Microsoft offers a free course.
It runs for 12 weeks and includes 24 lessons with theory, hands-on assignments, labs, and quizzes.
The curriculum covers neural networks and deep learning, computer vision, natural language processing, genetic algorithms, and AI ethics. For practice, it uses the two main ML frameworks—TensorFlow and PyTorch.
Each lesson follows the same structure: first, reading material, then a Jupyter notebook with code, and for some topics, a lab. The course is in English but has been translated into dozens of languages.
➡️ All materials and links are on GitHub
https://github.com/microsoft/AI-For-Beginners/blob/main/translations/ru/README.md
What's your AI level right now?
❤️ — Advanced user
🔥 — Almost zero
#AICourse #Microsoft #DeepLearning #TensorFlow #PyTorch #MachineLearning
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The Attention Mechanism allows transformer neural networks to determine the connection between words in a text and dynamically focus on the most important context. We will step by step implement the basic algorithm Scaled Dot-Product Attention, using classic matrices of queries (Query), keys (Key) and values (Value). This will help us to visually see how the attention weights are mathematically calculated and how the model matches the tokens with each other. 🧠✨
To start, we will install the PyTorch library for performing tensor calculations. 🛠️
pip install torch
The library has been successfully loaded and is ready for mathematical modeling of transformer layers. ✅
We will generate random vectors Query, Key and Value to simulate the passage of tokens through linear projections. 🎲
import torch
import torch.nn.functional as F
q = torch.randn(1, 3, 4) # (batch, seq_len, dim)
k = torch.randn(1, 3, 4)
v = torch.randn(1, 3, 4)
The tensors have been initialized and represent three hidden states for a sequence of three words. 📝
We will calculate the token similarity matrix through the scalar product and then scale it by the square root of the vector dimensions. 🔢
scores = torch.bmm(q, k.transpose(1, 2)) / (q.shape[-1] ** 0.5)
attention_weights = F.softmax(scores, dim=-1)
output = torch.bmm(attention_weights, v)
The scalar product has been translated into probability weights, based on which the final contextual vector has been formed. 🔄
A control run of the output dimension calculation:
python3 -c "import torch; q, k = torch.randn(1, 3, 4), torch.randn(1, 3, 4); print('Attention OK') if torch.bmm(q, k.transpose(1, 2)).shape == (1, 3, 3) else print('Error')"Expected output: Attention OK ✅
The Self-Attention formula lies at the heart of all modern LLMs, allowing them to process long contexts in parallel, unlike old recurrent networks (RNNs). Understanding this base is critically important for working with transformers, optimizing architectures and configuring KV-cache mechanisms. 🚀🧠
#PyTorch #Transformer #DeepLearning #AI #MachineLearning #LLM
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AI PYTHON 🌟
You’ve been invited to add the folder “AI PYTHON 🌟”, which includes 15 chats.
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Classical machine learning equations and diagrams cheat sheet 📊
https://github.com/soulmachine/machine-learning-cheat-sheet
#MachineLearning #ML #DataScience #CheatSheet #AI #DeepLearning
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https://github.com/soulmachine/machine-learning-cheat-sheet
#MachineLearning #ML #DataScience #CheatSheet #AI #DeepLearning
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Multi-agent RL is beautiful precisely at the moment when it starts to converge. 🤖✨
#MultiAgent #RL #ReinforcementLearning #AI #MachineLearning #DeepLearning
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#MultiAgent #RL #ReinforcementLearning #AI #MachineLearning #DeepLearning
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500 AI/ML/Computer Vision/NLP projects with code 🚀
This is a large collection of 500 ready-made projects in the field of machine learning, deep learning, computer vision, and NLP 🧠
All examples come with code, so you can not just read them, but immediately analyze and run them ⚙️
➡️ Link to GitHub:
https://github.com/ashishpatel26/500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code
#AI #MachineLearning #DeepLearning #ComputerVision #NLP #DataScience
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This is a large collection of 500 ready-made projects in the field of machine learning, deep learning, computer vision, and NLP 🧠
All examples come with code, so you can not just read them, but immediately analyze and run them ⚙️
➡️ Link to GitHub:
https://github.com/ashishpatel26/500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code
#AI #MachineLearning #DeepLearning #ComputerVision #NLP #DataScience
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A Chinese developer has released an open-source replacement for NumPy that performs calculations on GPUs. It's called CuPy 🚀. In many cases, it's enough to replace a single line:
The same code can run on CUDA up to 100 times faster ⚡️.
What it can do:
→ Compatible with existing NumPy and SciPy code 🛠️.
→ No need to rewrite the program or learn new syntax 📝.
→ Supports not only CUDA but also AMD ROCm 💻.
The project is completely open-source 📂:
🔗 https://github.com/cupy/cupy
#Python #GPU #NumPy #CuPy #AI #DeepLearning
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import cupy as cp
The same code can run on CUDA up to 100 times faster ⚡️.
What it can do:
→ Compatible with existing NumPy and SciPy code 🛠️.
→ No need to rewrite the program or learn new syntax 📝.
→ Supports not only CUDA but also AMD ROCm 💻.
The project is completely open-source 📂:
🔗 https://github.com/cupy/cupy
#Python #GPU #NumPy #CuPy #AI #DeepLearning
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Forwarded from Machine Learning with Python
Reinforcement Learning Methods and Tutorials 🧠📚
In these tutorials for reinforcement learning, it covers from the basic RL algorithms to advanced algorithms developed recent years.
Learning Resources: https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow 🚀
Here's a collection of simple materials on methods and practical guides, covering both basic reinforcement learning algorithms and modern, recently developed, and updated advanced algorithms. 📖✨
#ReinforcementLearning #MachineLearning #AI #DeepLearning #TechTutorials #DataScience
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In these tutorials for reinforcement learning, it covers from the basic RL algorithms to advanced algorithms developed recent years.
Learning Resources: https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow 🚀
Here's a collection of simple materials on methods and practical guides, covering both basic reinforcement learning algorithms and modern, recently developed, and updated advanced algorithms. 📖✨
#ReinforcementLearning #MachineLearning #AI #DeepLearning #TechTutorials #DataScience
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Diving deep into Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP. 🤖🧠
Lectures: 🎓📚
https://github.com/kmario23/deep-learning-drizzle
#DeepLearning #MachineLearning #AI #ReinforcementLearning #ComputerVision #NLP
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Lectures: 🎓📚
https://github.com/kmario23/deep-learning-drizzle
#DeepLearning #MachineLearning #AI #ReinforcementLearning #ComputerVision #NLP
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This repository contains a collection of the best resources on PyTorch: https://github.com/ritchieng/the-incredible-pytorch
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#PyTorch #AI #MachineLearning #DeepLearning #Coding #Resources
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#PyTorch #AI #MachineLearning #DeepLearning #Coding #Resources
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🔖 A large collection of lectures on Machine Learning and Deep Learning 🧠
We found a repository that brings together high-quality materials on several areas of artificial intelligence. 🤖
Excellent material for both learning and reviewing key topics. 📚
⛓️ Link to GitHub
https://github.com/kmario23/deep-learning-drizzle
#MachineLearning #DeepLearning #AI #Tech #Coding #Learning
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We found a repository that brings together high-quality materials on several areas of artificial intelligence. 🤖
Excellent material for both learning and reviewing key topics. 📚
⛓️ Link to GitHub
https://github.com/kmario23/deep-learning-drizzle
#MachineLearning #DeepLearning #AI #Tech #Coding #Learning
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sequence of four inputs, carrying every hidden state forward yourself. 🔄
1. Given
Four inputs X1 to X4, recurrent weights and biases for hidden layers a, b, c, and an output layer y. 📊
2. Initialize
Let us set the hidden states a0, b0, c0 to zeros. Nothing has been read yet. 🛑
3. First hidden layer (a)
We build the transformation matrix by laying the input weights, the state weights and the biases side by side. We stack X1, the previous state a0, and an extra 1 underneath. Multiply the two, and a1 = [0, 1]. 🧮
4. Second hidden layer (b)
Let us do it again, one layer up. Now a1 is the input, and b0 is the previous state. Multiply: b1 = [1, -1]. ⬆️
5. Third hidden layer (c)
Once more. b1 is the input, c0 is the previous state, and c1 = [1, 1]. 🔁
6. Output layer (y)
Let us read the answer off the top of the stack. Weights and biases against [c1; 1], and Y1 = [3, 0, 3]. 📝
7. Carry the states forward
We copy a1, b1, c1 across. This is the whole trick of a recurrent network: the states are the only thing the next input gets to see. 🚀
8. Process X2
Repeat steps 3 to 6 for the second input: three hidden layers, then the output. Y2 = [5, 0, 4]. 🔢
9. Carry the states forward
Let us copy a2, b2, c2 across, exactly as before. 🔄
10. Process X3
Same four moves, third input. Y3 = [13, -1, 9]. 🧩
11. Carry the states forward
We copy a3, b3, c3 across, one last time. ⏭️
12. Process X4
Repeat once more. Y4 = [15, 7, 2]. ✅
You have just run a Deep RNN over a whole sequence by hand. ✍️
The outputs:
Y1: [3, 0, 3]
Y2: [5, 0, 4]
Y3: [13, -1, 9]
Y4: [15, 7, 2]
The takeaway: the hidden states are the memory, and they are the only memory there is. Everything the network learns from X1 has to fit in those little two-cell columns and get handed forward, one step at a time. 🧠
#RNN #DeepLearning #AI #MachineLearning #NeuralNetworks #Tech
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1. Given
Four inputs X1 to X4, recurrent weights and biases for hidden layers a, b, c, and an output layer y. 📊
2. Initialize
Let us set the hidden states a0, b0, c0 to zeros. Nothing has been read yet. 🛑
3. First hidden layer (a)
We build the transformation matrix by laying the input weights, the state weights and the biases side by side. We stack X1, the previous state a0, and an extra 1 underneath. Multiply the two, and a1 = [0, 1]. 🧮
4. Second hidden layer (b)
Let us do it again, one layer up. Now a1 is the input, and b0 is the previous state. Multiply: b1 = [1, -1]. ⬆️
5. Third hidden layer (c)
Once more. b1 is the input, c0 is the previous state, and c1 = [1, 1]. 🔁
6. Output layer (y)
Let us read the answer off the top of the stack. Weights and biases against [c1; 1], and Y1 = [3, 0, 3]. 📝
7. Carry the states forward
We copy a1, b1, c1 across. This is the whole trick of a recurrent network: the states are the only thing the next input gets to see. 🚀
8. Process X2
Repeat steps 3 to 6 for the second input: three hidden layers, then the output. Y2 = [5, 0, 4]. 🔢
9. Carry the states forward
Let us copy a2, b2, c2 across, exactly as before. 🔄
10. Process X3
Same four moves, third input. Y3 = [13, -1, 9]. 🧩
11. Carry the states forward
We copy a3, b3, c3 across, one last time. ⏭️
12. Process X4
Repeat once more. Y4 = [15, 7, 2]. ✅
You have just run a Deep RNN over a whole sequence by hand. ✍️
The outputs:
Y1: [3, 0, 3]
Y2: [5, 0, 4]
Y3: [13, -1, 9]
Y4: [15, 7, 2]
The takeaway: the hidden states are the memory, and they are the only memory there is. Everything the network learns from X1 has to fit in those little two-cell columns and get handed forward, one step at a time. 🧠
#RNN #DeepLearning #AI #MachineLearning #NeuralNetworks #Tech
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❤4