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Do you want to understand the methods used to train LLMs?

The training of large language models (LLMs) is based on various approaches that help models understand and generate text.

Each method shapes the learning process in its own way - from predicting the next word to classifying entire sentences or labeling entities.

Here are 4 common methods of training LLMs in simple language 👇

1. Causal Language Modeling
Predicts the next word in a sequence based on the previous ones. Helps the model master the natural flow of speech and the structure of sentences.
Analogy: how to finish a sentence for another person by guessing the next word.

2. Masked Language Modeling
Learns by guessing the missing words in a sentence based on the surrounding context. Improves the overall understanding of language.
Analogy: how to solve tasks with missing words.

3. Text Classification Modeling
Determines the general class of a sentence (for example, tone or topic) by comparing predictions with actual labels.
Analogy: how to sort letters into folders "Work", "Personal", or "Promotions".

4. Token Classification Modeling
Assigns labels to each word or subword - for example, highlights names, places, or dates in the text.
Analogy: how to highlight words with different colors - names in blue, places in green, dates in yellow.

These methods form the basis of modern LLMs, and each of them plays a role in making AI smarter and more useful.

https://t.me/CodeProgrammer

𝐕𝐢𝐬𝐮𝐚𝐥 𝐛𝐥𝐨𝐠 on Vision Transformers is live.
https://vizuaranewsletter.com/p/vision-transformers?r=5b5pyd&utm_campaign=post&utm_medium=web

Learn how ViT works from the ground up, and fine-tune one on a real classification dataset.

CNNs process images through small sliding filters. Each filter only sees a tiny local region, and the model has to stack many layers before distant parts of an image can even talk to each other.

Vision Transformers threw that whole approach out.

ViT chops an image into patches, treats each patch like a token, and runs self-attention across the full sequence.
Every patch can attend to every other patch from the very first layer. No stacking required.

That global view from layer one is what made ViT surpass CNNs on large-scale benchmarks.

𝐖𝐡𝐚𝐭 𝐭𝐡𝐞 𝐛𝐥𝐨𝐠 𝐜𝐨𝐯𝐞𝐫𝐬:

- Introduction to Vision Transformers and comparison with CNNs
- Adapting transformers to images: patch embeddings and flattening
- Positional encodings in Vision Transformers
- Encoder-only structure for classification
- Benefits and drawbacks of ViT
- Real-world applications of Vision Transformers
- Hands-on: fine-tuning ViT for image classification

The Image below shows

Self-attention connects every pixel to every other pixel at once. Convolution only sees a small local window. That's why ViT captures things CNNs miss, like the optical illusion painting where distant patches form a hidden face.

The architecture is simple. Split image into patches, flatten them into embeddings (like words in a sentence), run them through a Transformer encoder, and the class token collects info from all patches for the final prediction. Patch in, class out.

Inside attention: each patch (query) compares itself to all other patches (keys), softmax gives attention weights, and the weighted sum of values produces a new representation aware of the full image, visualizes what the CLS token actually attends to through attention heatmaps.

The second half of the blog is hands-on code. I fine-tuned ViT-Base from google (86M params) on the Oxford-IIIT Pet dataset, 37 breeds, ~7,400 images.

𝐁𝐥𝐨𝐠 𝐋𝐢𝐧𝐤
https://vizuaranewsletter.com/p/vision-transformers?r=5b5pyd&utm_campaign=post&utm_medium=web

𝐒𝐨𝐦𝐞 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬
ViT paper dissection
https://youtube.com/watch?v=U_sdodhcBC4

Build ViT from Scratch
https://youtube.com/watch?v=ZRo74xnN2SI

Original Paper
https://arxiv.org/abs/2010.11929

https://t.me/CodeProgrammer

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Classical filters & convolution: The heart of computer vision

Before Deep Learning exploded onto the scene, traditional computer vision centered on filters. Filters were small, hand-engineered matrices that you convolved with an image to detect specific features like edges, corners, or textures. In this article, we will dive into the details of classical filters and convolution operation - how they work, why they matter, and how to implement them.

More: https://www.vizuaranewsletter.com/p/classical-filters-and-convolution

RAG won't work in 2026 if you're still using old approaches.

Yes, many companies are still failing with RAG — not because they're doing it wrong, but because they're stuck on outdated techniques.

Here's what usually happens: most companies start with a chatbot / chat app when talking about AI implementation. And here RAG becomes key — to connect their data via a database and enable the chat app to retrieve relevant documents.

But today, RAG is no longer limited to just chats. The applications of RAG are practically limitless, and that's a good thing.

RAG still remains the foundation for everything you build on LLMs and AI agents. The only thing that's changed is the RAG techniques themselves. The old approach no longer works — more advanced techniques are needed, what's now called advanced RAG.

The essence of RAG is to enrich the system with your data via a database so it can find relevant documents or their parts. The results are simple and often "okay", especially if the documents are well-structured and there aren't many of them.

But when the documents are unstructured and it's important to get not just accurate documents but also the right context, advanced techniques come into play:

- query decomposition
- metadata enrichment
- hybrid indexing
- reranking
- context fusion

These approaches allow the RAG system to find and generate more accurate and contextually relevant answers.

Therefore, advanced RAG is important. RAG isn't dead and can't die. Just use smarter techniques.

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