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://xn--r1a.website/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://xn--r1a.website/CodeProgrammer

𝐋𝐨𝐠𝐢𝐬𝐭𝐢𝐜 𝐑𝐞𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧 𝐄𝐱𝐩𝐥𝐚𝐢𝐧𝐞𝐝 𝐬𝐢𝐦𝐩𝐥𝐲

If you’ve just started learning Machine Learning, 𝐋𝐨𝐠𝐢𝐬𝐭𝐢𝐜 𝐑𝐞𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧 is one of the most important and misunderstood algorithms.

Here’s everything you need to know 👇

𝟏 ⇨ 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐋𝐨𝐠𝐢𝐬𝐭𝐢𝐜 𝐑𝐞𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧?

It’s a supervised ML algorithm used to predict probabilities and classify data into binary outcomes (like 0 or 1, Yes or No, Spam or Not Spam).

𝟐 ⇨ 𝐇𝐨𝐰 𝐢𝐭 𝐰𝐨𝐫𝐤𝐬?

It starts like Linear Regression, but instead of outputting continuous values, it passes the result through a 𝐬𝐢𝐠𝐦𝐨𝐢𝐝 𝐟𝐮𝐧𝐜𝐭𝐢𝐨𝐧 to map the result between 0 and 1.

𝘗𝘳𝘰𝘣𝘢𝘣𝘪𝘭𝘪𝘵𝘺 = 𝟏 / (𝟏 + 𝐞⁻(𝐰𝐱 + 𝐛))

Here,
𝐰 = weights
𝐱 = inputs
𝐛 = bias
𝐞 = Euler’s number (approx. 2.718)

𝟑 ⇨ 𝐖𝐡𝐲 𝐧𝐨𝐭 𝐋𝐢𝐧𝐞𝐚𝐫 𝐑𝐞𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧?

Because Linear Regression predicts any number from -∞ to +∞, which doesn’t make sense for probability.
We need outputs between 0 and 1 and that’s where the sigmoid function helps.

𝟒 ⇨ 𝐋𝐨𝐬𝐬 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧 𝐮𝐬𝐞𝐝?

𝐁𝐢𝐧𝐚𝐫𝐲 𝐂𝐫𝐨𝐬𝐬-𝐄𝐧𝐭𝐫𝐨𝐩𝐲

ℒ = −(y log(p) + (1 − y) log(1 − p))
Where y is the actual value (0 or 1), and p is the predicted probability

𝟓 ⇨ 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 𝐢𝐧 𝐫𝐞𝐚𝐥 𝐥𝐢𝐟𝐞:

𝐄𝐦𝐚𝐢𝐥 𝐒𝐩𝐚𝐦 𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧
𝐃𝐢𝐬𝐞𝐚𝐬𝐞 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧
𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐂𝐡𝐮𝐫𝐧 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧
𝐂𝐥𝐢𝐜𝐤-𝐓𝐡𝐫𝐨𝐮𝐠𝐡 𝐑𝐚𝐭𝐞 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧
𝐁𝐢𝐧𝐚𝐫𝐲 𝐬𝐞𝐧𝐭𝐢𝐦𝐞𝐧𝐭 𝐜𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧

𝟔 ⇨ 𝐕𝐬. 𝐎𝐭𝐡𝐞𝐫 𝐂𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐞𝐫𝐬

It’s fast, interpretable, and easy to implement, but it struggles with non-linearly separable data unlike Decision Trees or SVMs.

𝟕 ⇨ 𝐂𝐚𝐧 𝐢𝐭 𝐡𝐚𝐧𝐝𝐥𝐞 𝐦𝐮𝐥𝐭𝐢𝐩𝐥𝐞 𝐜𝐥𝐚𝐬𝐬𝐞𝐬?

Yes, using One-vs-Rest (OvR) or Softmax in Multinomial Logistic Regression.

𝟖 ⇨ 𝐄𝐱𝐚𝐦𝐩𝐥𝐞 𝐢𝐧 𝐏𝐲𝐭𝐡𝐨𝐧

from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
model.fit(X_train, y_train)
pred = model.predict(X_test)

#LogisticRegression #MachineLearning #MLAlgorithms #SupervisedLearning #BinaryClassification #SigmoidFunction #PythonML #ScikitLearn #MLForBeginners #DataScienceBasics #MLExplained #ClassificationModels #AIApplications #PredictiveModeling #MLRoadmap

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