Forwarded from Python | Machine Learning | Coding | R
Datasets Guide ๐
A practical and beginner-friendly guide that walks you through everything you need to know about datasets in machine learning and deep learning. This guide explains how to load, preprocess, and use datasets effectively for training models. It's an essential resource for anyone working with LLMs or custom training workflows, especially with tools like Unsloth.
Importance:
Understanding how to properly handle datasets is a critical step in building accurate and efficient AI models. This guide simplifies the process, helping you avoid common pitfalls and optimize your data pipeline for better performance.
Link: https://docs.unsloth.ai/basics/datasets-guide
#MachineLearning #DeepLearning #Datasets #DataScience #AI #Unsloth #LLM #TrainingData #MLGuide
โก๏ธ BEST DATA SCIENCE CHANNELS ON TELEGRAM ๐
A practical and beginner-friendly guide that walks you through everything you need to know about datasets in machine learning and deep learning. This guide explains how to load, preprocess, and use datasets effectively for training models. It's an essential resource for anyone working with LLMs or custom training workflows, especially with tools like Unsloth.
Importance:
Understanding how to properly handle datasets is a critical step in building accurate and efficient AI models. This guide simplifies the process, helping you avoid common pitfalls and optimize your data pipeline for better performance.
Link: https://docs.unsloth.ai/basics/datasets-guide
#MachineLearning #DeepLearning #Datasets #DataScience #AI #Unsloth #LLM #TrainingData #MLGuide
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Access whitepapers, podcasts, code labs, & recorded livestreams. Additionally, there is a bonus assignment for you!
https://www.kaggle.com/learn-guide/5-day-genai
#GenerativeAI #GoogleAI #AICourse #SelfPacedLearning #MachineLearning #DeepLearning #Kaggle #AICommunity #TechEducation #AIforEveryone
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Kaggle
5-Day Gen AI Intensive Course with Google
Kaggle is the worldโs largest data science community with powerful tools and resources to help you achieve your data science goals.
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Forwarded from Python | Machine Learning | Coding | R
A Complete Course to Learn Robotics and Perception
Notebook-based book "Introduction to Robotics and Perception" by Frank Dellaert and Seth Hutchinson
github.com/gtbook/robotics
roboticsbook.org/intro.html
โก๏ธ BEST DATA SCIENCE CHANNELS ON TELEGRAM ๐
Notebook-based book "Introduction to Robotics and Perception" by Frank Dellaert and Seth Hutchinson
github.com/gtbook/robotics
roboticsbook.org/intro.html
#Robotics #Perception #AI #DeepLearning #ComputerVision #RoboticsCourse #MachineLearning #Education #RoboticsResearch #GitHub
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Forwarded from Python | Machine Learning | Coding | R
Forwarded from Python | Machine Learning | Coding | R
ML Tools GRadio.pdf
203.3 KB
Gradio: The easiest way to demo your models.
- Core Idea: Quickly turn #ML models into interactive web apps.
- No frontend skills needed. It's all #Python.
- Works with any Python code, including custom functions.
- Share via temporary links or deploy on #HuggingFace Spaces.
- Get user feedback to improve your models.
If you're looking to create interactive demos for your ML project, check out #Gradio!
โป๏ธ Repost if you found this useful
โก๏ธ BEST DATA SCIENCE CHANNELS ON TELEGRAM ๐
- Core Idea: Quickly turn #ML models into interactive web apps.
- No frontend skills needed. It's all #Python.
- Works with any Python code, including custom functions.
- Share via temporary links or deploy on #HuggingFace Spaces.
- Get user feedback to improve your models.
If you're looking to create interactive demos for your ML project, check out #Gradio!
โป๏ธ Repost if you found this useful
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Forwarded from Python | Machine Learning | Coding | R
This channels is for Programmers, Coders, Software Engineers.
0๏ธโฃ Python
1๏ธโฃ Data Science
2๏ธโฃ Machine Learning
3๏ธโฃ Data Visualization
4๏ธโฃ Artificial Intelligence
5๏ธโฃ Data Analysis
6๏ธโฃ Statistics
7๏ธโฃ Deep Learning
8๏ธโฃ programming Languages
โ
https://www.tgoop.com/addlist/8_rRW2scgfRhOTc0
โ
https://www.tgoop.com/Codeprogrammer
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Forwarded from Python Courses
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Click Me Load More CSV files into a database using Python.
๐ฅ By: https://www.tgoop.com/Python53
โญ๏ธ BEST DATA SCIENCE CHANNELS ON TELEGRAM โญ๏ธ
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Forwarded from ENG. Hussein Sheikho
ูุฑุตุฉ ุนู
ู ุนู ุจุนุฏ ๐งโ๐ป
ูุง ูุชุทูุจ ุงู ู ุคูู ุงู ุฎุจุฑู ุงูุดุฑูู ุชูุฏู ุชุฏุฑูุจ ูุงู ูโจ
ุณุงุนุงุช ุงูุนู ู ู ุฑููโฐ
ูุชู ุงูุชุณุฌูู ุซู ุงูุชูุงุตู ู ุนู ูุญุถูุฑ ููุงุก ุชุนุฑููู ุจุงูุนู ู ูุงูุดุฑูู
https://forms.gle/hqUZXu7u4uLjEDPv8
ูุง ูุชุทูุจ ุงู ู ุคูู ุงู ุฎุจุฑู ุงูุดุฑูู ุชูุฏู ุชุฏุฑูุจ ูุงู ู
ุณุงุนุงุช ุงูุนู ู ู ุฑูู
ูุชู ุงูุชุณุฌูู ุซู ุงูุชูุงุตู ู ุนู ูุญุถูุฑ ููุงุก ุชุนุฑููู ุจุงูุนู ู ูุงูุดุฑูู
https://forms.gle/hqUZXu7u4uLjEDPv8
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Google Docs
ูุฑุตุฉ ุนู
ู
ุงูุนู
ู ู
ู ุงูู
ูุฒู ูู ุจุจุณุงุทุฉ ุญู ูู
ุดููุฉ ุงูุจุทุงูุฉ ููุดุจุงุจ ุงูุนุฑุจู ูููู ุงูุจุดุฑ ุญูู ุงูุนุงูู
ุ๐ ุงูู ุทุฑููู ูููุตูู ุงูู ุงูุญุฑูุฉ ุงูู
ุงููุฉ ูุจุนูุฏุงู ุนู ุดุบู ุงููุธููุฉ ุงูุญููู
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ู
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ุฑุชุจุงุช ุงูุถุนููุฉ..
ุฃุตุจุญ ุงูุฑุจุญ ู ู ุงูุงูุชุฑูุช ุฃู ุฑ ุญูููู ูููุณ ููู ..๐ค
ููุฏู ูู ูุฑุตุฉ ุงูุขู ู ู ุบูุฑ ุฃู ุดูุงุฏุงุชโฆ
ุฃุตุจุญ ุงูุฑุจุญ ู ู ุงูุงูุชุฑูุช ุฃู ุฑ ุญูููู ูููุณ ููู ..๐ค
ููุฏู ูู ูุฑุตุฉ ุงูุขู ู ู ุบูุฑ ุฃู ุดูุงุฏุงุชโฆ
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Forwarded from Python Courses
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Forwarded from Python | Machine Learning | Coding | R
SciPy.pdf
206.4 KB
Unlock the full power of SciPy with my comprehensive cheat sheet!
Master essential functions for:
Function optimization and solving equations
Linear algebra operations
ODE integration and statistical analysis
Signal processing and spatial data manipulation
Data clustering and distance computation ...and much more!
๐ฏ BEST DATA SCIENCE CHANNELS ON TELEGRAM ๐
Master essential functions for:
Function optimization and solving equations
Linear algebra operations
ODE integration and statistical analysis
Signal processing and spatial data manipulation
Data clustering and distance computation ...and much more!
#Python #SciPy #MachineLearning #DataScience #CheatSheet #ArtificialIntelligence #Optimization #LinearAlgebra #SignalProcessing #BigData
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Mastering CNNs: From Kernels to Model Evaluation
If you're learning Computer Vision, understanding the Conv2D layer in Convolutional Neural Networks (#CNNs) is crucial. Letโs break it down from basic to advanced.
1. What is Conv2D?
Conv2D is a 2D convolutional layer used in image processing. It takes an image as input and applies filters (also called kernels) to extract features.
2. What is a Kernel (or Filter)?
A kernel is a small matrix (like 3x3 or 5x5) that slides over the image and performs element-wise multiplication and summing.
A 3x3 kernel means the filter looks at 3x3 chunks of the image.
The kernel detects patterns like edges, textures, etc.
Example:
A vertical edge detection kernel might look like:
[-1, 0, 1]
[-1, 0, 1]
[-1, 0, 1]
3. What Are Filters in Conv2D?
In CNNs, we donโt use just one filterโwe use multiple filters in a single Conv2D layer.
Each filter learns to detect a different feature (e.g., horizontal lines, curves, textures).
So if you have 32 filters in the Conv2D layer, youโll get 32 feature maps.
More Filters = More Features = More Learning Power
4. Kernel Size and Its Impact
Smaller kernels (e.g., 3x3) are most common; they capture fine details.
Larger kernels (e.g., 5x5 or 7x7) capture broader patterns, but increase computational cost.
Many CNNs stack multiple small kernels (like 3x3) to simulate a large receptive field while keeping complexity low.
5. Life Cycle of a CNN Model (From Data to Evaluation)
Letโs visualize how a CNN model works from start to finish:
Step 1: Data Collection
Images are gathered and labeled (e.g., cat vs dog).
Step 2: Preprocessing
Resize images
Normalize pixel values
Data augmentation (flipping, rotation, etc.)
Step 3: Model Building (Conv2D layers)
Add Conv2D + Activation (ReLU)
Use Pooling layers (MaxPooling2D)
Add Dropout to prevent overfitting
Flatten and connect to Dense layers
Step 4: Training the Model
Feed data in batches
Use loss function (like cross-entropy)
Optimize using backpropagation + optimizer (like Adam)
Adjust weights over several epochs
Step 5: Evaluation
Test the model on unseen data
Use metrics like Accuracy, Precision, Recall, F1-Score
Visualize using confusion matrix
Step 6: Deployment
Convert model to suitable format (e.g., ONNX, TensorFlow Lite)
Deploy on web, mobile, or edge devices
Summary
Conv2D uses filters (kernels) to extract image features.
More filters = better feature detection.
The CNN pipeline takes raw image data, learns features, and gives powerful predictions.
If this helped you, let me know! Or feel free to share your experience learning CNNs!
๐ฏ BEST DATA SCIENCE CHANNELS ON TELEGRAM ๐
If you're learning Computer Vision, understanding the Conv2D layer in Convolutional Neural Networks (#CNNs) is crucial. Letโs break it down from basic to advanced.
1. What is Conv2D?
Conv2D is a 2D convolutional layer used in image processing. It takes an image as input and applies filters (also called kernels) to extract features.
2. What is a Kernel (or Filter)?
A kernel is a small matrix (like 3x3 or 5x5) that slides over the image and performs element-wise multiplication and summing.
A 3x3 kernel means the filter looks at 3x3 chunks of the image.
The kernel detects patterns like edges, textures, etc.
Example:
A vertical edge detection kernel might look like:
[-1, 0, 1]
[-1, 0, 1]
[-1, 0, 1]
3. What Are Filters in Conv2D?
In CNNs, we donโt use just one filterโwe use multiple filters in a single Conv2D layer.
Each filter learns to detect a different feature (e.g., horizontal lines, curves, textures).
So if you have 32 filters in the Conv2D layer, youโll get 32 feature maps.
More Filters = More Features = More Learning Power
4. Kernel Size and Its Impact
Smaller kernels (e.g., 3x3) are most common; they capture fine details.
Larger kernels (e.g., 5x5 or 7x7) capture broader patterns, but increase computational cost.
Many CNNs stack multiple small kernels (like 3x3) to simulate a large receptive field while keeping complexity low.
5. Life Cycle of a CNN Model (From Data to Evaluation)
Letโs visualize how a CNN model works from start to finish:
Step 1: Data Collection
Images are gathered and labeled (e.g., cat vs dog).
Step 2: Preprocessing
Resize images
Normalize pixel values
Data augmentation (flipping, rotation, etc.)
Step 3: Model Building (Conv2D layers)
Add Conv2D + Activation (ReLU)
Use Pooling layers (MaxPooling2D)
Add Dropout to prevent overfitting
Flatten and connect to Dense layers
Step 4: Training the Model
Feed data in batches
Use loss function (like cross-entropy)
Optimize using backpropagation + optimizer (like Adam)
Adjust weights over several epochs
Step 5: Evaluation
Test the model on unseen data
Use metrics like Accuracy, Precision, Recall, F1-Score
Visualize using confusion matrix
Step 6: Deployment
Convert model to suitable format (e.g., ONNX, TensorFlow Lite)
Deploy on web, mobile, or edge devices
Summary
Conv2D uses filters (kernels) to extract image features.
More filters = better feature detection.
The CNN pipeline takes raw image data, learns features, and gives powerful predictions.
If this helped you, let me know! Or feel free to share your experience learning CNNs!
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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๏ปฟ
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How do transformers work? Learn it by hand ๐
๐ช๐ฎ๐น๐ธ๐๐ต๐ฟ๐ผ๐๐ด๐ต
[1] Given
โณ Input features from the previous block (5 positions)
[2] Attention
โณ Feed all 5 features to a query-key attention module (QK) to obtain an attention weight matrix (A). I will skip the details of this module. In a follow-up post I will unpack this module.
[3] Attention Weighting
โณ Multiply the input features with the attention weight matrix to obtain attention weighted features (Z). Note that there are still 5 positions.
โณ The effect is to combine features across positions (horizontally), in this case, X1 := X1 + X2, X2 := X2 + X3....etc.
[4] FFN: First Layer
โณ Feed all 5 attention weighted features into the first layer.
โณ Multiply these features with the weights and biases.
โณ The effect is to combine features across feature dimensions (vertically).
โณ The dimensionality of each feature is increased from 3 to 4.
โณ Note that each position is processed by the same weight matrix. This is what the term "position-wise" is referring to.
โณ Note that the FFN is essentially a multi layer perceptron.
[5] ReLU
โณ Negative values are set to zeros by ReLU.
[6] FFN: Second Layer
โณ Feed all 5 features (d=3) into the second layer.
โณ The dimensionality of each feature is decreased from 4 back to 3.
โณ The output is fed to the next block to repeat this process.
โณ Note that the next block would have a completely separate set of parameters.
#ai #tranformers #genai #learning
๐ฏ BEST DATA SCIENCE CHANNELS ON TELEGRAM ๐
๐ช๐ฎ๐น๐ธ๐๐ต๐ฟ๐ผ๐๐ด๐ต
[1] Given
โณ Input features from the previous block (5 positions)
[2] Attention
โณ Feed all 5 features to a query-key attention module (QK) to obtain an attention weight matrix (A). I will skip the details of this module. In a follow-up post I will unpack this module.
[3] Attention Weighting
โณ Multiply the input features with the attention weight matrix to obtain attention weighted features (Z). Note that there are still 5 positions.
โณ The effect is to combine features across positions (horizontally), in this case, X1 := X1 + X2, X2 := X2 + X3....etc.
[4] FFN: First Layer
โณ Feed all 5 attention weighted features into the first layer.
โณ Multiply these features with the weights and biases.
โณ The effect is to combine features across feature dimensions (vertically).
โณ The dimensionality of each feature is increased from 3 to 4.
โณ Note that each position is processed by the same weight matrix. This is what the term "position-wise" is referring to.
โณ Note that the FFN is essentially a multi layer perceptron.
[5] ReLU
โณ Negative values are set to zeros by ReLU.
[6] FFN: Second Layer
โณ Feed all 5 features (d=3) into the second layer.
โณ The dimensionality of each feature is decreased from 4 back to 3.
โณ The output is fed to the next block to repeat this process.
โณ Note that the next block would have a completely separate set of parameters.
#ai #tranformers #genai #learning
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Forwarded from Python | Machine Learning | Coding | R
๐จ๐ปโ๐ป Carnegie University in the United States has come to offer a free #datamining course in 25 lectures to those interested in this field.
โ
โ
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Forwarded from Python | Machine Learning | Coding | R
This channels is for Programmers, Coders, Software Engineers.
0๏ธโฃ Python
1๏ธโฃ Data Science
2๏ธโฃ Machine Learning
3๏ธโฃ Data Visualization
4๏ธโฃ Artificial Intelligence
5๏ธโฃ Data Analysis
6๏ธโฃ Statistics
7๏ธโฃ Deep Learning
8๏ธโฃ programming Languages
โ
https://www.tgoop.com/addlist/8_rRW2scgfRhOTc0
โ
https://www.tgoop.com/Codeprogrammer
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Forwarded from Python | Machine Learning | Coding | R
Full PyTorch Implementation of Transformer-XL
If you're looking to understand and experiment with Transformer-XL using PyTorch, this resource provides a clean and complete implementation. Transformer-XL is a powerful model that extends the Transformer architecture with recurrence, enabling learning dependencies beyond fixed-length segments.
The implementation is ideal for researchers, students, and developers aiming to dive deeper into advanced language modeling techniques.
Explore the code and start building:
https://www.k-a.in/pyt-transformerXL.html
#TransformerXL #PyTorch #DeepLearning #NLP #LanguageModeling #AI #MachineLearning #OpenSource #ResearchTools
https://www.tgoop.com/CodeProgrammer
If you're looking to understand and experiment with Transformer-XL using PyTorch, this resource provides a clean and complete implementation. Transformer-XL is a powerful model that extends the Transformer architecture with recurrence, enabling learning dependencies beyond fixed-length segments.
The implementation is ideal for researchers, students, and developers aiming to dive deeper into advanced language modeling techniques.
Explore the code and start building:
https://www.k-a.in/pyt-transformerXL.html
#TransformerXL #PyTorch #DeepLearning #NLP #LanguageModeling #AI #MachineLearning #OpenSource #ResearchTools
https://www.tgoop.com/CodeProgrammer
๐3
Forwarded from Python | Machine Learning | Coding | R
Top 100+ questions%0A %22Google Data Science Interview%22.pdf
16.7 MB
Google is known for its rigorous data science interview process, which typically follows a hybrid format. Candidates are expected to demonstrate strong programming skills, solid knowledge in statistics and machine learning, and a keen ability to approach problems from a product-oriented perspective.
To succeed, one must be proficient in several critical areas: statistics and probability, SQL and Python programming, product sense, and case study-based analytics.
This curated list features over 100 of the most commonly asked and important questions in Google data science interviews. It serves as a comprehensive resource to help candidates prepare effectively and confidently for the challenge ahead.
#DataScience #GoogleInterview #InterviewPrep #MachineLearning #SQL #Statistics #ProductAnalytics #Python #CareerGrowth
https://www.tgoop.com/addlist/0f6vfFbEMdAwODBk
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@CodeProgrammer Matplotlib.pdf
4.3 MB
The Complete Visual Guide for Data Enthusiasts
Matplotlib is a powerful Python library for data visualization, essential not only for acing job interviews but also for building a solid foundation in analytical thinking and data storytelling.
This step-by-step tutorial guide walks learners through everything from the basics to advanced techniques in Matplotlib. It also includes a curated collection of the most frequently asked Matplotlib-related interview questions, making it an ideal resource for both beginners and experienced professionals.
#Matplotlib #DataVisualization #Python #DataScience #InterviewPrep #Analytics #TechCareer #LearnToCode๏ปฟ
https://www.tgoop.com/addlist/0f6vfFbEMdAwODBk
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Automate Dataset Labeling with Active Learning
A few years ago, training AI models required massive amounts of labeled data. Manually collecting and labeling this data was both time-consuming and expensive. But thankfully, weโve come a long way since then, and now we have much more powerful tools and techniques to help us automate this labeling process. One of the most effective ways? Active Learning.
In this article, weโll walk through the concept of active learning, how it works, and share a step-by-step implementation of how to automate dataset labeling for a text classification task using this method.
Read article: https://machinelearningmastery.com/automate-dataset-labeling-with-active-learning/
https://www.tgoop.com/DataScienceM
A few years ago, training AI models required massive amounts of labeled data. Manually collecting and labeling this data was both time-consuming and expensive. But thankfully, weโve come a long way since then, and now we have much more powerful tools and techniques to help us automate this labeling process. One of the most effective ways? Active Learning.
In this article, weโll walk through the concept of active learning, how it works, and share a step-by-step implementation of how to automate dataset labeling for a text classification task using this method.
Read article: https://machinelearningmastery.com/automate-dataset-labeling-with-active-learning/
https://www.tgoop.com/DataScienceM
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