@Codeprogrammer Cheat Sheet Numpy.pdf
213.7 KB
This checklist covers the essentials of NumPy in one place, helping you:
- Create and initialize arrays
- Perform element-wise computations
- Stack and split arrays
- Apply linear algebra functions
- Efficiently index, slice, and manipulate arrays
…and much more!
Feel free to share if you found this useful, and let me know in the comments if I missed anything!
⚡️ BEST DATA SCIENCE CHANNELS ON TELEGRAM 🌟
- Create and initialize arrays
- Perform element-wise computations
- Stack and split arrays
- Apply linear algebra functions
- Efficiently index, slice, and manipulate arrays
…and much more!
Feel free to share if you found this useful, and let me know in the comments if I missed anything!
#NumPy #Python #DataScience #MachineLearning #Automation #DeepLearning #Programming #Tech #DataAnalysis #SoftwareDevelopment #Coding #TechTips #PythonForDataScience
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Click Me Load More CSV files into a database using Python.
#python #csv #dataAnalysis
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#python #csv #dataAnalysis
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Master Machine Learning in Just 20 Days.1745724742524
30.8 MB
Title:
Master Machine Learning in Just 20 Days - Your Ultimate Guide! 🔥
Description:
Struggling to break into Data Science or ace ML interviews at top product-based companies?
This 20-day roadmap covers ML basics to advanced topics like tuning, deep learning, and deployment with top resources and practice questions!
What’s Inside:
✅ Supervised & Unsupervised Learning – Regression, Classification, Clustering
✅ Deep Learning & Neural Networks – CNNs, RNNs, LSTMs
✅ End-to-End ML Projects – Data Preprocessing, Feature Engineering, Deployment
✅ Model Optimization – Hyperparameter Tuning, Ensemble Methods
✅ Real-World ML Applications – NLP, AutoML, Scalable ML Systems
#MachineLearning #DeepLearning #DataScience #ArtificialIntelligence #MLEngineering #CareerGrowth #MLRoadmap
By: www.tgoop.com/HusseinSheikho✅
💯 BEST DATA SCIENCE CHANNELS ON TELEGRAM 🌟
Master Machine Learning in Just 20 Days - Your Ultimate Guide! 🔥
Description:
Struggling to break into Data Science or ace ML interviews at top product-based companies?
This 20-day roadmap covers ML basics to advanced topics like tuning, deep learning, and deployment with top resources and practice questions!
What’s Inside:
✅ Supervised & Unsupervised Learning – Regression, Classification, Clustering
✅ Deep Learning & Neural Networks – CNNs, RNNs, LSTMs
✅ End-to-End ML Projects – Data Preprocessing, Feature Engineering, Deployment
✅ Model Optimization – Hyperparameter Tuning, Ensemble Methods
✅ Real-World ML Applications – NLP, AutoML, Scalable ML Systems
#MachineLearning #DeepLearning #DataScience #ArtificialIntelligence #MLEngineering #CareerGrowth #MLRoadmap
By: www.tgoop.com/HusseinSheikho
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Forwarded from ENG. Hussein Sheikho
فرصة عمل عن بعد 🧑💻
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ساعات العمل مرنه⏰
يتم التسجيل ثم التواصل معك لحضور لقاء تعريفي بالعمل والشركه
https://forms.gle/hqUZXu7u4uLjEDPv8
لا يتطلب اي مؤهل او خبره الشركه تقدم تدريب كامل
ساعات العمل مرنه
يتم التسجيل ثم التواصل معك لحضور لقاء تعريفي بالعمل والشركه
https://forms.gle/hqUZXu7u4uLjEDPv8
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Google Docs
فرصة عمل
العمل من المنزل هو ببساطة حل لمشكلة البطالة للشباب العربي ولكل البشر حول العالم،👌 انه طريقك للوصول الى الحرية المالية وبعيداً عن شغل الوظيفة الحكومية المملة والمرتبات الضعيفة..
أصبح الربح من الانترنت أمر حقيقي وليس وهم..🤜
نقدم لك فرصة الآن من غير أي شهادات…
أصبح الربح من الانترنت أمر حقيقي وليس وهم..🤜
نقدم لك فرصة الآن من غير أي شهادات…
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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!
💯 BEST DATA SCIENCE CHANNELS ON TELEGRAM 🌟
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. Let’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 don’t use just one filter—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, you’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)
Let’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!
💯 BEST DATA SCIENCE CHANNELS ON TELEGRAM 🌟
If you're learning Computer Vision, understanding the Conv2D layer in Convolutional Neural Networks (#CNNs) is crucial. Let’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 don’t use just one filter—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, you’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)
Let’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!
#DeepLearning #ComputerVision #CNNs #Conv2D #MachineLearning #AI #NeuralNetworks #DataScience #ModelTraining #ImageProcessing
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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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The price of promoting a post on our channel (permanent post on our channel) is $15.
We accept personal or business promotions.
Contact @HusseinSheikho
We accept personal or business promotions.
Contact @HusseinSheikho
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Four best-advanced university courses on NLP & LLM to advance your skills:
1. Advanced NLP -- Carnegie Mellon University
Link: https://lnkd.in/ddEtMghr
2. Recent Advances on Foundation Models -- University of Waterloo
Link: https://lnkd.in/dbdpUV9v
3. Large Language Model Agents -- University of California, Berkeley
Link: https://lnkd.in/d-MdSM8Y
4. Advanced LLM Agent -- University Berkeley
Link: https://lnkd.in/dvCD4HR4
#LLM #python #AI #Agents #RAG #NLP
💯 BEST DATA SCIENCE CHANNELS ON TELEGRAM 🌟
1. Advanced NLP -- Carnegie Mellon University
Link: https://lnkd.in/ddEtMghr
2. Recent Advances on Foundation Models -- University of Waterloo
Link: https://lnkd.in/dbdpUV9v
3. Large Language Model Agents -- University of California, Berkeley
Link: https://lnkd.in/d-MdSM8Y
4. Advanced LLM Agent -- University Berkeley
Link: https://lnkd.in/dvCD4HR4
#LLM #python #AI #Agents #RAG #NLP
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👨🏻💻 Carnegie University in the United States has come to offer a free #datamining course in 25 lectures to those interested in this field.
┌
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Forwarded from Python | Machine Learning | Coding | R
This channels is for Programmers, Coders, Software Engineers.
0️⃣ Python
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8️⃣ programming Languages
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✅ 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
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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LLM Engineer’s Handbook (2024)
🚀 Unlock the Future of AI with the LLM Engineer’s Handbook 🚀
Step into the world of Large Language Models (LLMs) with this comprehensive guide that takes you from foundational concepts to deploying advanced applications using LLMOps best practices. Whether you're an AI engineer, NLP professional, or LLM enthusiast, this book offers practical insights into designing, training, and deploying LLMs in real-world scenarios.
Why Choose the LLM Engineer’s Handbook?
Comprehensive Coverage: Learn about data engineering, supervised fine-tuning, and deployment strategies.
Hands-On Approach: Implement MLOps components through practical examples, including building an LLM-powered twin that's cost-effective, scalable, and modular.
Cutting-Edge Techniques: Explore inference optimization, preference alignment, and real-time data processing to apply LLMs effectively in your projects.
Real-World Applications: Move beyond isolated Jupyter notebooks and focus on building production-grade end-to-end LLM systems.
Limited-Time Offer
Originally priced at $55, the LLM Engineer’s Handbook is now available for just $25—a 55% discount! This special offer is available for a limited quantity, so act fast to secure your copy.
Who Should Read This Book?
This handbook is ideal for AI engineers, NLP professionals, and LLM engineers looking to deepen their understanding of LLMs. A basic knowledge of LLMs, Python, and AWS is recommended. Whether you're new to AI or seeking to enhance your skills, this book provides comprehensive guidance on implementing LLMs in real-world scenarios.
Don't miss this opportunity to advance your expertise in LLM engineering. Secure your discounted copy today and take the next step in your AI journey!
Buy book: https://www.patreon.com/DataScienceBooks/shop/llm-engineers-handbook-2024-1582908
🚀 Unlock the Future of AI with the LLM Engineer’s Handbook 🚀
Step into the world of Large Language Models (LLMs) with this comprehensive guide that takes you from foundational concepts to deploying advanced applications using LLMOps best practices. Whether you're an AI engineer, NLP professional, or LLM enthusiast, this book offers practical insights into designing, training, and deploying LLMs in real-world scenarios.
Why Choose the LLM Engineer’s Handbook?
Comprehensive Coverage: Learn about data engineering, supervised fine-tuning, and deployment strategies.
Hands-On Approach: Implement MLOps components through practical examples, including building an LLM-powered twin that's cost-effective, scalable, and modular.
Cutting-Edge Techniques: Explore inference optimization, preference alignment, and real-time data processing to apply LLMs effectively in your projects.
Real-World Applications: Move beyond isolated Jupyter notebooks and focus on building production-grade end-to-end LLM systems.
Limited-Time Offer
Originally priced at $55, the LLM Engineer’s Handbook is now available for just $25—a 55% discount! This special offer is available for a limited quantity, so act fast to secure your copy.
Who Should Read This Book?
This handbook is ideal for AI engineers, NLP professionals, and LLM engineers looking to deepen their understanding of LLMs. A basic knowledge of LLMs, Python, and AWS is recommended. Whether you're new to AI or seeking to enhance your skills, this book provides comprehensive guidance on implementing LLMs in real-world scenarios.
Don't miss this opportunity to advance your expertise in LLM engineering. Secure your discounted copy today and take the next step in your AI journey!
Buy book: https://www.patreon.com/DataScienceBooks/shop/llm-engineers-handbook-2024-1582908
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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
https://www.tgoop.com/addlist/0f6vfFbEMdAwODBk
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Introduction to Machine Learning” by Alex Smola and S.V.N.
Vishwanathan is a foundational textbook that offers a comprehensive and mathematically rigorous introduction to core concepts in machine learning. The book covers key topics including supervised and unsupervised learning, kernels, graphical models, optimization techniques, and large-scale learning. It balances theory and practical application, making it ideal for graduate students, researchers, and professionals aiming to deepen their understanding of machine learning fundamentals and algorithmic principles.
PDF:
https://alex.smola.org/drafts/thebook.pdf
Vishwanathan is a foundational textbook that offers a comprehensive and mathematically rigorous introduction to core concepts in machine learning. The book covers key topics including supervised and unsupervised learning, kernels, graphical models, optimization techniques, and large-scale learning. It balances theory and practical application, making it ideal for graduate students, researchers, and professionals aiming to deepen their understanding of machine learning fundamentals and algorithmic principles.
PDF:
https://alex.smola.org/drafts/thebook.pdf
#MachineLearning #AI #DataScience #MLAlgorithms #DeepLearning #MathForML #MLTheory #MLResearch #AlexSmola #SVNVishwanathan
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Keep up with the latest developments in artificial intelligence and Python through our WhatsApp channel. The resources will be diverse and of great importance. We strive to make our WhatsApp channel the number one channel in the world of artificial intelligence.
Tell your friends
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Machine Learning Notes 📝 (1).pdf
4.9 MB
Machine Learning Notes with Real Project and Amazing discussion
https://www.tgoop.com/CodeProgrammer🌟
#MachineLearning #AI #DataScience #MLAlgorithms #DeepLearning
https://www.tgoop.com/CodeProgrammer
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