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Datasets Guide ๐Ÿ“š

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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Data cleaning and preparation techniques.

https://www.tgoop.com/DataScienceM ๐ŸŒŸ
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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

#Robotics #Perception #AI #DeepLearning #ComputerVision #RoboticsCourse #MachineLearning #Education #RoboticsResearch #GitHub


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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!

โ™ป๏ธ Repost if you found this useful

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This channels is for Programmers, Coders, Software Engineers.

0๏ธโƒฃ Python
1๏ธโƒฃ Data Science
2๏ธโƒฃ Machine Learning
3๏ธโƒฃ Data Visualization
4๏ธโƒฃ Artificial Intelligence
5๏ธโƒฃ Data Analysis
6๏ธโƒฃ Statistics
7๏ธโƒฃ Deep Learning
8๏ธโƒฃ programming Languages

โœ… https://www.tgoop.com/addlist/8_rRW2scgfRhOTc0

โœ… 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.

๐Ÿ–ฅ By: https://www.tgoop.com/Python53

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Forwarded from ENG. Hussein Sheikho
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ุณุงุนุงุช ุงู„ุนู…ู„ ู…ุฑู†ู‡  โฐ
ูŠุชู… ุงู„ุชุณุฌูŠู„ ุซู… ุงู„ุชูˆุงุตู„ ู…ุนูƒ ู„ุญุถูˆุฑ ู„ู‚ุงุก ุชุนุฑูŠููŠ ุจุงู„ุนู…ู„ ูˆุงู„ุดุฑูƒู‡

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Forwarded from Python Courses
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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!


#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. Letโ€™s 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 donโ€™t use just one filterโ€”we 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, youโ€™ll 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)

Letโ€™s 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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๐Ÿš€ Master the Transformer Architecture with PyTorch! ๐Ÿง 

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".

๐Ÿ”— Check it out here:
https://www.k-a.in/pyt-transformer.html

This guide offers:

๐ŸŒŸ Detailed explanations of each component of the Transformer architecture.

๐ŸŒŸ Step-by-step code implementations in PyTorch.

๐ŸŒŸ Insights into the self-attention mechanism and positional encoding.

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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How do transformers work? Learn it by hand ๐Ÿ‘‡

๐—ช๐—ฎ๐—น๐—ธ๐˜๐—ต๐—ฟ๐—ผ๐˜‚๐—ด๐—ต

[1] Given
โ†ณ Input features from the previous block (5 positions)

[2] Attention
โ†ณ 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
โ†ณ Multiply the input features with the attention weight matrix to obtain attention weighted features (Z). Note that there are still 5 positions.
โ†ณ The effect is to combine features across positions (horizontally), in this case, X1 := X1 + X2, X2 := X2 + X3....etc.

[4] FFN: First Layer
โ†ณ Feed all 5 attention weighted features into the first layer.
โ†ณ Multiply these features with the weights and biases.
โ†ณ The effect is to combine features across feature dimensions (vertically).
โ†ณ The dimensionality of each feature is increased from 3 to 4.
โ†ณ Note that each position is processed by the same weight matrix. This is what the term "position-wise" is referring to.
โ†ณ Note that the FFN is essentially a multi layer perceptron.

[5] ReLU
โ†ณ Negative values are set to zeros by ReLU.

[6] FFN: Second Layer
โ†ณ Feed all 5 features (d=3) into the second layer.
โ†ณ The dimensionality of each feature is decreased from 4 back to 3.
โ†ณ The output is fed to the next block to repeat this process.
โ†ณ Note that the next block would have a completely separate set of parameters.

#ai #tranformers #genai #learning

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๐Ÿ”ด Comprehensive course on "Data Mining"
๐Ÿ–ฅ Carnegie Mellon University, USA


๐Ÿ‘จ๐Ÿปโ€๐Ÿ’ป Carnegie University in the United States has come to offer a free #datamining course in 25 lectures to those interested in this field.

โ—€๏ธ In this course, you will deal with statistical concepts and model selection methods on the one hand, and on the other hand, you will have to implement these concepts in practice and present the results.

โ—€๏ธ The exercises are both combined: theory, #coding, and practical.๐Ÿ‘‡


โ”Œ ๐Ÿฅต Data Mining
โ””โฏ๏ธ Course Homepage

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This channels is for Programmers, Coders, Software Engineers.

0๏ธโƒฃ Python
1๏ธโƒฃ Data Science
2๏ธโƒฃ Machine Learning
3๏ธโƒฃ Data Visualization
4๏ธโƒฃ Artificial Intelligence
5๏ธโƒฃ Data Analysis
6๏ธโƒฃ Statistics
7๏ธโƒฃ Deep Learning
8๏ธโƒฃ programming Languages

โœ… https://www.tgoop.com/addlist/8_rRW2scgfRhOTc0

โœ… https://www.tgoop.com/Codeprogrammer
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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
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Top 100+ questions%0A %22Google Data Science Interview%22.pdf
16.7 MB
๐Ÿ’ฏ Top 100+ Google Data Science Interview Questions

๐ŸŒŸ Essential Prep Guide for Aspiring Candidates

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
๐Ÿ’ฏ Mastering Matplotlib in 20 Days

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
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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, weโ€™ve 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, weโ€™ll 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/

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2025/07/09 05:51:00
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