### Hugging Face Transformers: Unlock the Power of Open-Source AI in Python

Discover the limitless potential of Hugging Face Transformers, a robust Python library that empowers developers and data scientists to harness thousands of pretrained, open-source AI models. These state-of-the-art models are designed for a wide array of tasks across various modalities, including natural language processing (NLP), computer vision, audio processing, and multimodal learning.

#### Why Choose Hugging Face Transformers?

1. Cost Efficiency: Utilizing pretrained models significantly reduces costs associated with developing custom AI solutions from scratch.
2. Time Savings: Save valuable time by leveraging pre-trained models, allowing you to focus on fine-tuning and deploying your applications faster.
3. Control and Customization: Gain greater control over your AI deployments, enabling you to tailor models to meet specific project requirements and achieve optimal performance.

#### Versatile Applications

Whether you're working on text classification, sentiment analysis, image recognition, speech-to-text conversion, or any other AI-driven task, Hugging Face Transformers provides the tools you need to succeed. The library's extensive collection of models ensures that you have access to cutting-edge technology without the need for extensive training resources.

#### Get Started Today!

Dive into the world of open-source AI with Hugging Face Transformers. Explore detailed tutorials and practical examples at:
https://realpython.com/huggingface-transformers/

to enhance your skills and unlock new possibilities in your projects. Join our community on Telegram (@DataScienceM) for continuous learning and support.

🧠 #HuggingFaceTransformers #OpenSourceAI #PretrainedModels #NaturalLanguageProcessing #ComputerVision #AudioProcessing #MultimodalLearning #AIDevelopment #PythonLibrary #DataScienceCommunity

Embark on an exciting journey through the intricate world of Artificial Intelligence with our comprehensive learning map! ✅

1⃣ Artificial Intelligence (AI)
Dive into the vast universe of AI, where machines learn to perform tasks that typically require human intelligence. From Reinforcement Learning to Augmented Programming, this broad circle encompasses a wide array of techniques and applications. Whether you're interested in Speech Recognition or Algorithm Building, this is your starting point for understanding how machines can mimic human cognition. #AI #MachineIntelligence

🔢 Machine Learning (ML)
As we move inward, explore the fascinating realm of Machine Learning, a subset of AI focused on developing algorithms that enable machines to learn from data. Discover the power of Supervised and Unsupervised Learning, K-Means clustering, and Hypothesis Testing. This circle will equip you with the skills needed to analyze data and build predictive models. #MachineLearning #DataScience

🔢 Neural Networks
Next, delve into Neural Networks, computer models designed to simulate the workings of the human brain. These networks are used in various applications, from image recognition to natural language processing. Learn about Backpropagation, Feed Forward networks, and Support Vector Machines. This circle will provide you with the foundation to develop complex models that can solve real-world problems. #NeuralNetworks #DeepLearningBasics

🔢 Deep Learning
In the narrower circle, discover Deep Learning, an advanced branch of ML that uses multi-layered neural networks to tackle complex challenges. Explore Long Short-Term Memory (LSTM) networks, Transformers, and Auto Encoders. These techniques are at the forefront of modern AI applications like machine translation and medical diagnosis. Join us to master these cutting-edge technologies. #DeepLearning #AdvancedAI

🔢 Generative AI
Finally, in the smallest and most specialized circle, uncover Generative AI, which focuses on creating new and innovative content using AI. Dive into Generative Adversarial Networks (GANs), Large Language Models (LLM), and Transfer Learning. This circle will empower you to generate creative content such as images and text using AI. #GenerativeAI #CreativeTech

Our AI learning map is your gateway to mastering the latest advancements in technology. Whether you're a beginner eager to grasp the basics or a professional looking to expand your expertise, this map offers a clear path to achieving your goals in the ever-evolving field of AI. Start your journey today and unlock the potential of artificial intelligence! #AILearningMap #TechFuture

https://t.iss.one/CodeProgrammer ✈️

🔗 Roadmap to become NLP Expert in 2025

https://t.iss.one/DataScienceM ✅

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.iss.one/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.iss.one/CodeProgrammer

MIT's "Machine Learning" lecture notes

PDF: https://introml.mit.edu/_static/spring24/LectureNotes/6_390_lecture_notes_spring24.pdf

#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #SupervisedLearning #IBMDataScience #FreeCourses #Certification #LearnDataScience

https://t.iss.one/CodeProgrammer ✈️

"Introduction to Applied Linear Algebra" by S. Boyd (Stanford) & L. Vandenberghe (UCLA)

📘 Freely available at: https://web.stanford.edu/~boyd/vmls/

📽 Lecture Videos at: https://youtube.com/playlist?list=PLoROMvodv4rMz-WbFQtNUsUElIh2cPmN9

#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #SupervisedLearning #IBMDataScience #FreeCourses #Certification #LearnDataScience

https://t.iss.one/CodeProgrammer ✈️

✔️ Awesome AI/ML Resources: Learn AI/ML for beginners with a roadmap and free resources.

🖥  https://github.com/armankhondker/awesome-ai-ml-resources

#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #SupervisedLearning #IBMDataScience #FreeCourses #Certification #LearnDataScience

https://t.iss.one/CodeProgrammer 🖥

Prepared a Statistical Analysis Cheatsheet Using Python.

https://t.iss.one/DataScienceM 📂