Forwarded from Python | Machine Learning | Coding | R
This book is for readers looking to learn new #machinelearning algorithms or understand algorithms at a deeper level. Specifically, it is intended for readers interested in seeing machine learning algorithms derived from start to finish. Seeing these derivations might help a reader previously unfamiliar with common algorithms understand how they work intuitively. Or, seeing these derivations might help a reader experienced in modeling understand how different #algorithms create the models they do and the advantages and disadvantages of each one.
This book will be most helpful for those with practice in basic modeling. It does not review best practices—such as feature engineering or balancing response variables—or discuss in depth when certain models are more appropriate than others. Instead, it focuses on the elements of those models.
https://dafriedman97.github.io/mlbook/content/introduction.html
#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #GAN #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming #Keras
https://yangx.top/CodeProgrammer✅
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Forwarded from Python | Machine Learning | Coding | R
"Introduction to Probability for Data Science"
One of the best books on #Probability. Available FREE.
Download the book:
probability4datascience.com/download.html
One of the best books on #Probability. Available FREE.
Download the book:
probability4datascience.com/download.html
#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #GAN #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming #Keras
https://yangx.top/CodeProgrammer✅
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Forwarded from Data Science | Machine Learning with Python for Researchers
#DataScience #MachineLearning #DeepLearning #Python #AI #MLProjects #DataAnalysis #ExplainableAI #100DaysOfCode #TechEducation #MLInterviewPrep #NeuralNetworks #MathForML #Statistics #Coding #AIForEveryone #PythonForDataScience
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Forwarded from Python | Machine Learning | Coding | R
Foundations of Large Language Models
Download it: https://readwise-assets.s3.amazonaws.com/media/wisereads/articles/foundations-of-large-language-/2501.09223v1.pdf
#LLM #AIresearch #DeepLearning #NLP #FoundationModels #MachineLearning #LanguageModels #ArtificialIntelligence #NeuralNetworks #AIPaper
Download it: https://readwise-assets.s3.amazonaws.com/media/wisereads/articles/foundations-of-large-language-/2501.09223v1.pdf
#LLM #AIresearch #DeepLearning #NLP #FoundationModels #MachineLearning #LanguageModels #ArtificialIntelligence #NeuralNetworks #AIPaper
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Forwarded from Python | Machine Learning | Coding | R
Dive deep into the world of Transformers with this comprehensive PyTorch implementation guide. Whether you're a seasoned ML engineer or just starting out, this resource breaks down the complexities of the Transformer model, inspired by the groundbreaking paper "Attention Is All You Need".
https://www.k-a.in/pyt-transformer.html
This guide offers:
By following along, you'll gain a solid understanding of how Transformers work and how to implement them from scratch.
#MachineLearning #DeepLearning #PyTorch #Transformer #AI #NLP #AttentionIsAllYouNeed #Coding #DataScience #NeuralNetworks
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Forwarded from Python | Machine Learning | Coding | R
10 GitHub repos to build a career in AI engineering:
(100% free step-by-step roadmap)
1️⃣ ML for Beginners by Microsoft
A 12-week project-based curriculum that teaches classical ML using Scikit-learn on real-world datasets.
Includes quizzes, lessons, and hands-on projects, with some videos.
GitHub repo → https://lnkd.in/dCxStbYv
2️⃣ AI for Beginners by Microsoft
This repo covers neural networks, NLP, CV, transformers, ethics & more. There are hands-on labs in PyTorch & TensorFlow using Jupyter.
Beginner-friendly, project-based, and full of real-world apps.
GitHub repo → https://lnkd.in/dwS5Jk9E
3️⃣ Neural Networks: Zero to Hero
Now that you’ve grasped the foundations of AI/ML, it’s time to dive deeper.
This repo by Andrej Karpathy builds modern deep learning systems from scratch, including GPTs.
GitHub repo → https://lnkd.in/dXAQWucq
4️⃣ DL Paper Implementations
So far, you have learned the fundamentals of AI, ML, and DL. Now study how the best architectures work.
This repo covers well-documented PyTorch implementations of 60+ research papers on Transformers, GANs, Diffusion models, etc.
GitHub repo → https://lnkd.in/dTrtDrvs
5️⃣ Made With ML
Now it’s time to learn how to go from notebooks to production.
Made With ML teaches you how to design, develop, deploy, and iterate on real-world ML systems using MLOps, CI/CD, and best practices.
GitHub repo → https://lnkd.in/dYyjjBGb
6️⃣ Hands-on LLMs
- You've built neural nets.
- You've explored GPTs and LLMs.
Now apply them. This is a visually rich repo that covers everything about LLMs, like tokenization, fine-tuning, RAG, etc.
GitHub repo → https://lnkd.in/dh2FwYFe
7️⃣ Advanced RAG Techniques
Hands-on LLMs will give you a good grasp of RAG systems. Now learn advanced RAG techniques.
This repo covers 30+ methods to make RAG systems faster, smarter, and accurate, like HyDE, GraphRAG, etc.
GitHub repo → https://lnkd.in/dBKxtX-D
8️⃣ AI Agents for Beginners by Microsoft
After diving into LLMs and mastering RAG, learn how to build AI agents.
This hands-on course covers building AI agents using frameworks like AutoGen.
GitHub repo → https://lnkd.in/dbFeuznE
9️⃣ Agents Towards Production
The above course will teach what AI agents are. Next, learn how to ship them.
This is a practical playbook for building agents covering memory, orchestration, deployment, security & more.
GitHub repo → https://lnkd.in/dcwmamSb
🔟 AI Engg. Hub
To truly master LLMs, RAG, and AI agents, you need projects.
This covers 70+ real-world examples, tutorials, and agent app you can build, adapt, and ship.
GitHub repo → https://lnkd.in/geMYm3b6
(100% free step-by-step roadmap)
A 12-week project-based curriculum that teaches classical ML using Scikit-learn on real-world datasets.
Includes quizzes, lessons, and hands-on projects, with some videos.
GitHub repo → https://lnkd.in/dCxStbYv
This repo covers neural networks, NLP, CV, transformers, ethics & more. There are hands-on labs in PyTorch & TensorFlow using Jupyter.
Beginner-friendly, project-based, and full of real-world apps.
GitHub repo → https://lnkd.in/dwS5Jk9E
Now that you’ve grasped the foundations of AI/ML, it’s time to dive deeper.
This repo by Andrej Karpathy builds modern deep learning systems from scratch, including GPTs.
GitHub repo → https://lnkd.in/dXAQWucq
So far, you have learned the fundamentals of AI, ML, and DL. Now study how the best architectures work.
This repo covers well-documented PyTorch implementations of 60+ research papers on Transformers, GANs, Diffusion models, etc.
GitHub repo → https://lnkd.in/dTrtDrvs
Now it’s time to learn how to go from notebooks to production.
Made With ML teaches you how to design, develop, deploy, and iterate on real-world ML systems using MLOps, CI/CD, and best practices.
GitHub repo → https://lnkd.in/dYyjjBGb
- You've built neural nets.
- You've explored GPTs and LLMs.
Now apply them. This is a visually rich repo that covers everything about LLMs, like tokenization, fine-tuning, RAG, etc.
GitHub repo → https://lnkd.in/dh2FwYFe
Hands-on LLMs will give you a good grasp of RAG systems. Now learn advanced RAG techniques.
This repo covers 30+ methods to make RAG systems faster, smarter, and accurate, like HyDE, GraphRAG, etc.
GitHub repo → https://lnkd.in/dBKxtX-D
After diving into LLMs and mastering RAG, learn how to build AI agents.
This hands-on course covers building AI agents using frameworks like AutoGen.
GitHub repo → https://lnkd.in/dbFeuznE
The above course will teach what AI agents are. Next, learn how to ship them.
This is a practical playbook for building agents covering memory, orchestration, deployment, security & more.
GitHub repo → https://lnkd.in/dcwmamSb
To truly master LLMs, RAG, and AI agents, you need projects.
This covers 70+ real-world examples, tutorials, and agent app you can build, adapt, and ship.
GitHub repo → https://lnkd.in/geMYm3b6
#AIEngineering #MachineLearning #DeepLearning #LLMs #RAG #MLOps #Python #GitHubProjects #AIForBeginners #ArtificialIntelligence #NeuralNetworks #OpenSourceAI #DataScienceCareers
✉️ Our Telegram channels: https://yangx.top/addlist/0f6vfFbEMdAwODBk📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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Forwarded from Python | Machine Learning | Coding | R
Auto-Encoder & Backpropagation by hand ✍️ lecture video ~ 📺 https://byhand.ai/cv/10
It took me a few years to invent this method to show both forward and backward passes for a non-trivial case of a multi-layer perceptron over a batch of inputs, plus gradient descents over multiple epochs, while being able to hand calculate each step and code in Excel at the same time.
= Chapters =
• Encoder & Decoder (00:00)
• Equation (10:09)
• 4-2-4 AutoEncoder (16:38)
• 6-4-2-4-6 AutoEncoder (18:39)
• L2 Loss (20:49)
• L2 Loss Gradient (27:31)
• Backpropagation (30:12)
• Implement Backpropagation (39:00)
• Gradient Descent (44:30)
• Summary (51:39)
✉️ Our Telegram channels: https://yangx.top/addlist/0f6vfFbEMdAwODBk
It took me a few years to invent this method to show both forward and backward passes for a non-trivial case of a multi-layer perceptron over a batch of inputs, plus gradient descents over multiple epochs, while being able to hand calculate each step and code in Excel at the same time.
= Chapters =
• Encoder & Decoder (00:00)
• Equation (10:09)
• 4-2-4 AutoEncoder (16:38)
• 6-4-2-4-6 AutoEncoder (18:39)
• L2 Loss (20:49)
• L2 Loss Gradient (27:31)
• Backpropagation (30:12)
• Implement Backpropagation (39:00)
• Gradient Descent (44:30)
• Summary (51:39)
#AIEngineering #MachineLearning #DeepLearning #LLMs #RAG #MLOps #Python #GitHubProjects #AIForBeginners #ArtificialIntelligence #NeuralNetworks #OpenSourceAI #DataScienceCareers
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What is torch.nn really?
This article explains it quite well.
📌 Read
✉️ Our Telegram channels: https://yangx.top/addlist/0f6vfFbEMdAwODBk
When I started working with PyTorch, my biggest question was: "What is torch.nn?".
This article explains it quite well.
📌 Read
#pytorch #AIEngineering #MachineLearning #DeepLearning #LLMs #RAG #MLOps #Python #GitHubProjects #AIForBeginners #ArtificialIntelligence #NeuralNetworks #OpenSourceAI #DataScienceCareers
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Topic: CNN (Convolutional Neural Networks) – Part 1: Introduction and Basic Concepts
---
1. What is a CNN?
• A Convolutional Neural Network (CNN) is a type of deep learning model primarily used for analyzing visual data.
• CNNs automatically learn spatial hierarchies of features through convolutional layers.
---
2. Key Components of CNN
• Convolutional Layer: Applies filters (kernels) to input images to extract features like edges, textures, and shapes.
• Activation Function: Usually ReLU (Rectified Linear Unit) is applied after convolution for non-linearity.
• Pooling Layer: Reduces the spatial size of feature maps, typically using Max Pooling.
• Fully Connected Layer: After feature extraction, maps features to output classes.
---
3. How Convolution Works
• A kernel (small matrix) slides over the input image, computing element-wise multiplications and summing them up to form a feature map.
• Kernels detect features like edges, lines, and patterns.
---
4. Basic CNN Architecture Example
| Layer Type | Description |
| --------------- | ---------------------------------- |
| Input | Image of size (e.g., 28x28x1) |
| Conv Layer | 32 filters of size 3x3 |
| Activation | ReLU |
| Pooling Layer | MaxPooling 2x2 |
| Fully Connected | Flatten + Dense for classification |
---
5. Simple CNN with PyTorch Example
---
6. Why CNN over Fully Connected Networks?
• CNNs reduce the number of parameters by weight sharing in kernels.
• They preserve spatial relationships unlike fully connected layers.
---
Summary
• CNNs are powerful for image and video tasks due to convolution and pooling.
• Understanding convolution, pooling, and architecture basics is key to building models.
---
Exercise
• Implement a CNN with two convolutional layers and train it on MNIST digits.
---
#CNN #DeepLearning #NeuralNetworks #Convolution #MachineLearning
https://yangx.top/DataScience4
---
1. What is a CNN?
• A Convolutional Neural Network (CNN) is a type of deep learning model primarily used for analyzing visual data.
• CNNs automatically learn spatial hierarchies of features through convolutional layers.
---
2. Key Components of CNN
• Convolutional Layer: Applies filters (kernels) to input images to extract features like edges, textures, and shapes.
• Activation Function: Usually ReLU (Rectified Linear Unit) is applied after convolution for non-linearity.
• Pooling Layer: Reduces the spatial size of feature maps, typically using Max Pooling.
• Fully Connected Layer: After feature extraction, maps features to output classes.
---
3. How Convolution Works
• A kernel (small matrix) slides over the input image, computing element-wise multiplications and summing them up to form a feature map.
• Kernels detect features like edges, lines, and patterns.
---
4. Basic CNN Architecture Example
| Layer Type | Description |
| --------------- | ---------------------------------- |
| Input | Image of size (e.g., 28x28x1) |
| Conv Layer | 32 filters of size 3x3 |
| Activation | ReLU |
| Pooling Layer | MaxPooling 2x2 |
| Fully Connected | Flatten + Dense for classification |
---
5. Simple CNN with PyTorch Example
import torch.nn as nn
import torch.nn.functional as F
class SimpleCNN(nn.Module):
def __init__(self):
super(SimpleCNN, self).__init__()
self.conv1 = nn.Conv2d(1, 32, kernel_size=3) # 1 input channel, 32 filters
self.pool = nn.MaxPool2d(2, 2)
self.fc1 = nn.Linear(32 * 13 * 13, 10) # Assuming input 28x28
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = x.view(-1, 32 * 13 * 13) # Flatten
x = self.fc1(x)
return x
---
6. Why CNN over Fully Connected Networks?
• CNNs reduce the number of parameters by weight sharing in kernels.
• They preserve spatial relationships unlike fully connected layers.
---
Summary
• CNNs are powerful for image and video tasks due to convolution and pooling.
• Understanding convolution, pooling, and architecture basics is key to building models.
---
Exercise
• Implement a CNN with two convolutional layers and train it on MNIST digits.
---
#CNN #DeepLearning #NeuralNetworks #Convolution #MachineLearning
https://yangx.top/DataScience4
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Topic: CNN (Convolutional Neural Networks) – Part 3: Flattening, Fully Connected Layers, and Final Output
---
1. Flattening the Feature Maps
• After convolution and pooling layers, the resulting feature maps are multi-dimensional tensors.
• Flattening transforms these 3D tensors into 1D vectors to be passed into fully connected (dense) layers.
Example:
This reshapes the tensor from shape
---
2. Fully Connected (Dense) Layers
• These layers are used to perform classification based on the extracted features.
• Each neuron is connected to every neuron in the previous layer.
• They are placed after convolutional and pooling layers.
---
3. Output Layer
• The final layer is typically a fully connected layer with output neurons equal to the number of classes.
• Apply a softmax activation for multi-class classification (e.g., 10 classes for digits 0–9).
---
4. Complete CNN Example (PyTorch)
---
5. Why Fully Connected Layers Are Important
• They combine all learned spatial features into a single feature vector for classification.
• They introduce the final decision boundary between classes.
---
Summary
• Flattening bridges the convolutional part of the network to the fully connected part.
• Fully connected layers transform features into class scores.
• The output layer applies classification logic like softmax or sigmoid depending on the task.
---
Exercise
• Modify the CNN above to classify CIFAR-10 images (3 channels, 32x32) and calculate the total number of parameters in each layer.
---
#CNN #NeuralNetworks #Flattening #FullyConnected #DeepLearning
https://yangx.top/DataScienceM
---
1. Flattening the Feature Maps
• After convolution and pooling layers, the resulting feature maps are multi-dimensional tensors.
• Flattening transforms these 3D tensors into 1D vectors to be passed into fully connected (dense) layers.
Example:
x = x.view(x.size(0), -1)
This reshapes the tensor from shape
[batch_size, channels, height, width] to [batch_size, features].---
2. Fully Connected (Dense) Layers
• These layers are used to perform classification based on the extracted features.
• Each neuron is connected to every neuron in the previous layer.
• They are placed after convolutional and pooling layers.
---
3. Output Layer
• The final layer is typically a fully connected layer with output neurons equal to the number of classes.
• Apply a softmax activation for multi-class classification (e.g., 10 classes for digits 0–9).
---
4. Complete CNN Example (PyTorch)
import torch.nn as nn
import torch.nn.functional as F
class FullCNN(nn.Module):
def __init__(self):
super(FullCNN, self).__init__()
self.conv1 = nn.Conv2d(1, 32, 3, padding=1)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(32, 64, 3, padding=1)
self.fc1 = nn.Linear(64 * 7 * 7, 128) # assumes input 28x28
self.fc2 = nn.Linear(128, 10)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x))) # 28x28 -> 14x14
x = self.pool(F.relu(self.conv2(x))) # 14x14 -> 7x7
x = x.view(-1, 64 * 7 * 7) # Flatten
x = F.relu(self.fc1(x))
x = self.fc2(x) # Output layer
return x
---
5. Why Fully Connected Layers Are Important
• They combine all learned spatial features into a single feature vector for classification.
• They introduce the final decision boundary between classes.
---
Summary
• Flattening bridges the convolutional part of the network to the fully connected part.
• Fully connected layers transform features into class scores.
• The output layer applies classification logic like softmax or sigmoid depending on the task.
---
Exercise
• Modify the CNN above to classify CIFAR-10 images (3 channels, 32x32) and calculate the total number of parameters in each layer.
---
#CNN #NeuralNetworks #Flattening #FullyConnected #DeepLearning
https://yangx.top/DataScienceM
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