📂 Tags: #transformers #python #nlp

http://t.me/codeprogrammer 🔒

The Transformer's decoder clearly explained 👇

📂 Tags: #transformers #python #nlp

http://t.me/codeprogrammer 🔒

The Transformers architecture clearly explained 🫴

🏳️‍🌈 Python became GitHub's first language!

👨🏻‍💻 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.

✔️ 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:👇

┌ 🐱 Top programming along GitHub
├ 💰 Report


⏪ I also introduced the most important Python libraries for working with data and AI here: 👇


🖥 Data Manipulation & Analysis
▶️ pandas
▶️ Apache Spark
▶️ Polars
▶️ DuckDB


📊 Data Visualization
➡️ matplotlib
➡️ plotly
➡️ seaborn


🖥 Machine & Deep Learning
➡️ TensorFlow
➡️ PyTorch
➡️ Keras
➡️ scikit-learn
➡️ XGBoost
➡️ LightGBM
➡️ Prophet


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

🔑 Tags: #PYTHON #AI #ML #NLP

https://t.me/CodeProgrammer ✅

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 ⭐️

The Big Book of Large Language Models by Damien Benveniste

✅ Chapters:
1⃣ Introduction

🔢 Language Models Before Transformers

🔢 Attention Is All You Need: The Original Transformer Architecture

🔢 A More Modern Approach To The Transformer Architecture

🔢 Multi-modal Large Language Models

🔢 Transformers Beyond Language Models

🔢 Non-Transformer Language Models

🔢 How LLMs Generate Text

🔢 From Words To Tokens

1⃣0⃣ Training LLMs to Follow Instructions

1⃣1⃣ Scaling Model Training

1⃣🔢 Fine-Tuning LLMs

1⃣🔢 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

🔰 How to become a data scientist in 2025?

👨🏻‍💻 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.


🔢 Step 1: Strengthen your math and statistics!

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

✅ Linear algebra: matrices, vectors, eigenvalues.

🔗 Course: MIT 18.06 Linear Algebra


✅ Calculus: derivative, integral, optimization.

🔗 Course: MIT Single Variable Calculus


✅ Statistics and probability: Bayes' theorem, hypothesis testing.

🔗 Course: Statistics 110

➖➖➖➖➖

🔢 Step 2: Learn to code.

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

✅ Python: Pandas, NumPy, Matplotlib libraries

🔗 Course: FreeCodeCamp Python Course

✅ SQL language: Join commands, Window functions, query optimization.

🔗 Course: Stanford SQL Course

✅ Data structures and algorithms: arrays, linked lists, trees.

🔗 Course: MIT Introduction to Algorithms

➖➖➖➖➖

🔢 Step 3: Clean and visualize data

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

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

🔗 Course: Data Cleaning

✅ Data visualization: Matplotlib, Seaborn, Tableau

🔗 Course: Data Visualization Tutorial

➖➖➖➖➖

🔢 Step 4: Learn Machine Learning

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

✅ Supervised learning: regression, classification.

✅ Unsupervised learning: clustering, PCA, anomaly detection.

✅ Deep learning: neural networks, CNN, RNN


🔗 Course: CS229: Machine Learning

➖➖➖➖➖

🔢 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.

✅ Big Data Tools: Hadoop, Spark, Dask

✅ Cloud platforms: AWS, GCP, Azure

🔗 Course: Data Engineering

➖➖➖➖➖

🔢 Step 6: Do real projects!

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

✅ Kaggle competitions: solving real-world challenges.

✅ End-to-End projects: data collection, modeling, implementation.

✅ GitHub: Publish your projects on GitHub.

🔗 Platform: Kaggle🔗 Platform: ods.ai

➖➖➖➖➖

🔢 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.

✅ MLOps training: model versioning, monitoring, model retraining.

✅ Deployment models: Flask, FastAPI, Docker

🔗 Course: Stanford MLOps Course

➖➖➖➖➖

🔢 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.

✅ Read scientific articles: arXiv, Google Scholar

✅ Connect with the data community:

🔗 Site: Papers with code
🔗 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

🔥 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.

📌 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 ✅

🚀 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".

🔗 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



💯 BEST DATA SCIENCE CHANNELS ON TELEGRAM 🌟

🧠💻📊

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 🌟

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:

🙂 Positive sentiment

☹️ Negative sentiment

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

🔗 GitHub: https://lnkd.in/e_gk3hfe
📰 Article: https://lnkd.in/e_baNJd2

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

🔗 Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk

📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

Python Cheat Sheet

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

⚡️ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk

📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

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



✉️ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk

📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

Self-attention in LLMs, clearly explained

#SelfAttention #LLMs #Transformers #NLP #DeepLearning #MachineLearning #AIExplained #AttentionMechanism #AIConcepts #AIEducation

✉️ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk

📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

🔍 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:

📘 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.

🔧 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.

🚀 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.

🔗 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! 💡

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

✉️ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk

📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

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 — 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

➡️ 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

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