Python | Machine Learning | Coding | R
62.7K subscribers
1.13K photos
68 videos
143 files
789 links
List of our channels:
https://t.me/addlist/8_rRW2scgfRhOTc0

Discover powerful insights with Python, Machine Learning, Coding, and Rβ€”your essential toolkit for data-driven solutions, smart alg

Help and ads: @hussein_sheikho

https://telega.io/?r=nikapsOH
Download Telegram
πŸ“‚ Tags: #transformers #python #nlp

http://t.me/codeprogrammer πŸ”’

The Transformer's decoder clearly explained πŸ‘‡
Please open Telegram to view this post
VIEW IN TELEGRAM
πŸ‘10❀4
πŸ“‚ Tags: #transformers #python #nlp

http://t.me/codeprogrammer πŸ”’

The Transformers architecture clearly explained 🫴
Please open Telegram to view this post
VIEW IN TELEGRAM
πŸ‘13❀5
πŸ³οΈβ€πŸŒˆ Python became GitHub's first language!

πŸ‘¨πŸ»β€πŸ’» In a recent GitHub report, with the expansion of artificial intelligence, Python could finally overtake JavaScript and become the most popular language on GitHub in 2024. This happened after 10 years of JavaScript dominance and it is not very strange.

βœ”οΈ Because with the growth of artificial intelligence, developers are turning to Python more than ever, and Python's applications in data science and analytics are increasing every day. You can read the full GitHub report here:πŸ‘‡

β”Œ 🐱 Top programming along GitHub
β”œ
πŸ’° Report


βͺ I also introduced the most important Python libraries for working with data and AI here: πŸ‘‡


πŸ–₯ Data Manipulation & Analysis
▢️ pandas
▢️ Apache Spark
▢️ Polars
▢️ DuckDB


πŸ“Š Data Visualization
➑️ matplotlib
➑️ plotly
➑️ seaborn


πŸ–₯ Machine & Deep Learning
➑️ TensorFlow
➑️ PyTorch
➑️ Keras
➑️ scikit-learn
➑️ XGBoost
➑️ LightGBM
➑️ Prophet


🌫 NLP & Large Language Models
➑️ Hugging Face Transformers
➑️ LangChain
➑️ LlamaIndex

πŸ”‘ Tags: #PYTHON #AI #ML #NLP

https://t.me/CodeProgrammer βœ…
Please open Telegram to view this post
VIEW IN TELEGRAM
πŸ‘9❀3πŸ’―2
ChatGPT cheat sheet for data science.pdf
29 MB
Title: ChatGPT Cheat Sheet for Data Science (2025)
Source: DataCamp

Description:
This comprehensive cheat sheet serves as an essential guide for leveraging ChatGPT in data science workflows. Designed for both beginners and seasoned practitioners, it provides actionable prompts, code examples, and best practices to streamline tasks such as data generation, analysis, modeling, and automation. Key features include:
- Code Generation: Scripts for creating sample datasets in Python using Pandas and NumPy (e.g., generating tables with primary keys, names, ages, and salaries) .
- Data Analysis: Techniques for exploratory data analysis (EDA), hypothesis testing, and predictive modeling, including visualization recommendations (bar charts, line graphs) and statistical methods .
- Machine Learning: Guidance on algorithm selection, hyperparameter tuning, and model interpretation, with examples tailored for Python and SQL .
- NLP Applications: Tools for text classification, sentiment analysis, and named entity recognition, leveraging ChatGPT’s natural language processing capabilities .
- Workflow Automation: Strategies for automating repetitive tasks like data cleaning (handling duplicates, missing values) and report generation .

The guide also addresses ChatGPT’s limitations, such as potential biases and hallucinations, while emphasizing best practices for iterative prompting and verification . Updated for 2025, it integrates the latest advancements in AI-assisted data science, making it a must-have resource for efficient, conversational-driven analytics.

Tags:
#ChatGPT #DataScience #CheatSheet #2025Edition #DataCamp #Python #MachineLearning #DataAnalysis #Automation #NLP #SQL

https://t.me/CodeProgrammer ⭐️
Please open Telegram to view this post
VIEW IN TELEGRAM
πŸ‘14❀6πŸ’―2
The Big Book of Large Language Models by Damien Benveniste

βœ… Chapters:
1⃣ Introduction

πŸ”’ Language Models Before Transformers

πŸ”’ Attention Is All You Need: The Original Transformer Architecture

πŸ”’ A More Modern Approach To The Transformer Architecture

πŸ”’ Multi-modal Large Language Models

πŸ”’ Transformers Beyond Language Models

πŸ”’ Non-Transformer Language Models

πŸ”’ How LLMs Generate Text

πŸ”’ From Words To Tokens

1⃣0⃣ Training LLMs to Follow Instructions

1⃣1⃣ Scaling Model Training

1βƒ£πŸ”’ Fine-Tuning LLMs

1βƒ£πŸ”’ Deploying LLMs

Read it: https://book.theaiedge.io/

#ArtificialIntelligence #AI #MachineLearning #LargeLanguageModels #LLMs #DeepLearning #NLP #NaturalLanguageProcessing #AIResearch #TechBooks #AIApplications #DataScience #FutureOfAI #AIEducation #LearnAI #TechInnovation #AIethics #GPT #BERT #T5 #AIBook #AIEnthusiast

https://t.me/CodeProgrammer
Please open Telegram to view this post
VIEW IN TELEGRAM
πŸ‘17❀4πŸ‘Ž1
πŸ”° How to become a data scientist in 2025?

πŸ‘¨πŸ»β€πŸ’» If you want to become a data science professional, follow this path! I've prepared a complete roadmap with the best free resources where you can learn the essential skills in this field.


πŸ”’ Step 1: Strengthen your math and statistics!

✏️ The foundation of learning data science is mathematics, linear algebra, statistics, and probability. Topics you should master:

βœ… Linear algebra: matrices, vectors, eigenvalues.

πŸ”— Course: MIT 18.06 Linear Algebra


βœ… Calculus: derivative, integral, optimization.

πŸ”— Course: MIT Single Variable Calculus


βœ… Statistics and probability: Bayes' theorem, hypothesis testing.

πŸ”— Course: Statistics 110

βž–βž–βž–βž–βž–

πŸ”’ Step 2: Learn to code.

✏️ Learn Python and become proficient in coding. The most important topics you need to master are:

βœ… Python: Pandas, NumPy, Matplotlib libraries

πŸ”— Course: FreeCodeCamp Python Course

βœ… SQL language: Join commands, Window functions, query optimization.

πŸ”— Course: Stanford SQL Course

βœ… Data structures and algorithms: arrays, linked lists, trees.

πŸ”— Course: MIT Introduction to Algorithms

βž–βž–βž–βž–βž–

πŸ”’ Step 3: Clean and visualize data

✏️ Learn how to process and clean data and then create an engaging story from it!

βœ… Data cleaning: Working with missing values ​​and detecting outliers.

πŸ”— Course: Data Cleaning

βœ… Data visualization: Matplotlib, Seaborn, Tableau

πŸ”— Course: Data Visualization Tutorial

βž–βž–βž–βž–βž–

πŸ”’ Step 4: Learn Machine Learning

✏️ It's time to enter the exciting world of machine learning! You should know these topics:

βœ… Supervised learning: regression, classification.

βœ… Unsupervised learning: clustering, PCA, anomaly detection.

βœ… Deep learning: neural networks, CNN, RNN


πŸ”— Course: CS229: Machine Learning

βž–βž–βž–βž–βž–

πŸ”’ Step 5: Working with Big Data and Cloud Technologies

✏️ If you're going to work in the real world, you need to know how to work with Big Data and cloud computing.

βœ… Big Data Tools: Hadoop, Spark, Dask

βœ… Cloud platforms: AWS, GCP, Azure

πŸ”— Course: Data Engineering

βž–βž–βž–βž–βž–

πŸ”’ Step 6: Do real projects!

✏️ Enough theory, it's time to get coding! Do real projects and build a strong portfolio.

βœ… Kaggle competitions: solving real-world challenges.

βœ… End-to-End projects: data collection, modeling, implementation.

βœ… GitHub: Publish your projects on GitHub.

πŸ”— Platform: KaggleπŸ”— Platform: ods.ai

βž–βž–βž–βž–βž–

πŸ”’ Step 7: Learn MLOps and deploy models

✏️ Machine learning is not just about building a model! You need to learn how to deploy and monitor a model.

βœ… MLOps training: model versioning, monitoring, model retraining.

βœ… Deployment models: Flask, FastAPI, Docker

πŸ”— Course: Stanford MLOps Course

βž–βž–βž–βž–βž–

πŸ”’ Step 8: Stay up to date and network

✏️ Data science is changing every day, so it is necessary to update yourself every day and stay in regular contact with experienced people and experts in this field.

βœ… Read scientific articles: arXiv, Google Scholar

βœ… Connect with the data community:

πŸ”— Site: Papers with code
πŸ”— Site: AI Research at Google


#ArtificialIntelligence #AI #MachineLearning #LargeLanguageModels #LLMs #DeepLearning #NLP #NaturalLanguageProcessing #AIResearch #TechBooks #AIApplications #DataScience #FutureOfAI #AIEducation #LearnAI #TechInnovation #AIethics #GPT #BERT #T5 #AIBook #AIEnthusiast

https://t.me/CodeProgrammer
Please open Telegram to view this post
VIEW IN TELEGRAM
πŸ‘35❀15πŸ‘1
Some people asked me about a resource for learning about Transformers.

Here's a good one I am sharing again -- it covers just about everything you need to know.

brandonrohrer.com/transformers

Amazing stuff. It's totally worth your weekend.

#Transformers #DeepLearning #NLP #AI #MachineLearning #SelfAttention #DataScience #Technology #Python #LearningResource


https://t.me/CodeProgrammer
πŸ‘7❀6πŸ”₯2
Media is too big
VIEW IN TELEGRAM
πŸ”₯ MIT has updated its famous course 6.S191: Introduction to Deep Learning.

The program covers topics of #NLP, #CV, #LLM and the use of technology in medicine, offering a full cycle of training - from theory to practical classes using current versions of libraries.

The course is designed even for beginners: if you know how to take derivatives and multiply matrices, everything else will be explained in the process.

The lectures are released for free on YouTube and the #MIT platform on Mondays, with the first one already available

.

All slides, #code and additional materials can be found at the link provided.

πŸ“Œ Fresh lecture : https://youtu.be/alfdI7S6wCY?si=6682DD2LlFwmghew

#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://t.me/CodeProgrammer βœ…
Please open Telegram to view this post
VIEW IN TELEGRAM
πŸ‘12πŸ†’3πŸ”₯2πŸ†2πŸ’―1
πŸš€ Master the Transformer Architecture with PyTorch! 🧠

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".

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

This guide offers:

🌟 Detailed explanations of each component of the Transformer architecture.

🌟 Step-by-step code implementations in PyTorch.

🌟 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
ο»Ώ

πŸ’― BEST DATA SCIENCE CHANNELS ON TELEGRAM 🌟

πŸ§ πŸ’»πŸ“Š
Please open Telegram to view this post
VIEW IN TELEGRAM
πŸ‘3
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

πŸ’― BEST DATA SCIENCE CHANNELS ON TELEGRAM 🌟
Please open Telegram to view this post
VIEW IN TELEGRAM
πŸ‘10❀3
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://t.me/CodeProgrammer
πŸ‘7
This media is not supported in your browser
VIEW IN TELEGRAM
A new interactive sentiment visualization project has been developed, featuring a dynamic smiley face that reflects sentiment analysis results in real time. Using a natural language processing model, the system evaluates input text and adjusts the smiley face expression accordingly:

πŸ™‚ Positive sentiment

☹️ Negative sentiment

The visualization offers an intuitive and engaging way to observe sentiment dynamics as they happen.

πŸ”— GitHub: https://lnkd.in/e_gk3hfe
πŸ“° Article: https://lnkd.in/e_baNJd2

#AI #SentimentAnalysis #DataVisualization #InteractiveDesign #NLP #MachineLearning #Python #GitHubProjects #TowardsDataScience

πŸ”— Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk

πŸ“± Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
Please open Telegram to view this post
VIEW IN TELEGRAM
πŸ‘7πŸ‘3
Anyone trying to deeply understand Large Language Models.

Checkout
Foundations of Large Language Models


by Tong Xiao & Jingbo Zhu. It’s one of the clearest, most comprehensive resource.

⭐️ Paper Link: arxiv.org/pdf/2501.09223

#LLMs #LargeLanguageModels #AIResearch #DeepLearning #MachineLearning #AIResources #NLP #AITheory #FoundationModels #AIUnderstanding

ο»Ώ

βœ‰οΈ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk

πŸ“± Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
Please open Telegram to view this post
VIEW IN TELEGRAM
❀14
Please open Telegram to view this post
VIEW IN TELEGRAM
❀8πŸ’―2πŸ‘¨β€πŸ’»1
rnn.pdf
5.6 MB
πŸ” Understanding Recurrent Neural Networks (RNNs) Cheat Sheet!
Recurrent Neural Networks are a powerful type of neural network designed to handle sequential data. They are widely used in applications like natural language processing, speech recognition, and time-series prediction. Here's a quick cheat sheet to get you started:

πŸ“˜ Key Concepts:
Sequential Data: RNNs are designed to process sequences of data, making them ideal for tasks where order matters.
Hidden State: Maintains information from previous inputs, enabling memory across time steps.
Backpropagation Through Time (BPTT): The method used to train RNNs by unrolling the network through time.

πŸ”§ Common Variants:
Long Short-Term Memory (LSTM): Addresses vanishing gradient problems with gates to manage information flow.
Gated Recurrent Unit (GRU): Similar to LSTMs but with a simpler architecture.

πŸš€ Applications:
Language Modeling: Predicting the next word in a sentence.
Sentiment Analysis: Understanding sentiments in text.
Time-Series Forecasting: Predicting future data points in a series.

πŸ”— Resources:
Dive deeper with tutorials on platforms like Coursera, edX, or YouTube.
Explore open-source libraries like TensorFlow or PyTorch for implementation.
Let's harness the power of RNNs to innovate and solve complex problems! πŸ’‘

#RNN #RecurrentNeuralNetworks #DeepLearning #NLP #LSTM #GRU #TimeSeriesForecasting #MachineLearning #NeuralNetworks #AIApplications #SequenceModeling #MLCheatSheet #PyTorch #TensorFlow #DataScience


βœ‰οΈ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk

πŸ“± Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
Please open Telegram to view this post
VIEW IN TELEGRAM
❀11πŸ‘3
A curated collection of Kaggle notebooks showcasing how to build end-to-end AI applications using Hugging Face pretrained models, covering text, speech, image, and vision-language tasks β€” full tutorials and code available on GitHub:

1️⃣ Text-Based Applications

1.1. Building a Chatbot Using HuggingFace Open Source Models

https://lnkd.in/dku3bigK

1.2. Building a Text Translation System using Meta NLLB Open-Source Model

https://lnkd.in/dgdjaFds

2️⃣ Speech-Based Applications

2.1. Zero-Shot Audio Classification Using HuggingFace CLAP Open-Source Model

https://lnkd.in/dbgQgDyn

2.2. Building & Deploying a Speech Recognition System Using the Whisper Model & Gradio

https://lnkd.in/dcbp-8fN

2.3. Building Text-to-Speech Systems Using VITS & ArTST Models

https://lnkd.in/dwFcQ_X5

3️⃣ Image-Based Applications

3.1. Step-by-Step Guide to Zero-Shot Image Classification using CLIP Model

https://lnkd.in/dnk6epGB

3.2. Building an Object Detection Assistant Application: A Step-by-Step Guide

https://lnkd.in/d573SvYV

3.3. Zero-Shot Image Segmentation using Segment Anything Model (SAM)

https://lnkd.in/dFavEdHS

3.4. Building Zero-Shot Depth Estimation Application Using DPT Model & Gradio

https://lnkd.in/d9jjJu_g

4️⃣ Vision Language Applications

4.1. Building a Visual Question Answering System Using Hugging Face Open-Source Models

https://lnkd.in/dHNFaHFV

4.2. Building an Image Captioning System using Salesforce Blip Model

https://lnkd.in/dh36iDn9

4.3. Building an Image-to-Text Matching System Using Hugging Face Open-Source Models

https://lnkd.in/d7fsJEAF

➑️ You can find the articles and the codes for each article in this GitHub repo:

https://lnkd.in/dG5jfBwE

#HuggingFace #Kaggle #AIapplications #DeepLearning #MachineLearning #ComputerVision #NLP #SpeechRecognition #TextToSpeech #ImageProcessing #OpenSourceAI #ZeroShotLearning #Gradio

βœ‰οΈ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk

πŸ“± Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
Please open Telegram to view this post
VIEW IN TELEGRAM
❀13πŸ’―1