Research Papers PHD

✔️ 10 Books to Understand How Large Language Models Function (2026) 1. Deep Learning https://deeplearningbook.org The definitive reference for neural networks, covering backpropagation, architectures, and foundational concepts. 2. Artificial Intelligence: A Modern Approach https://aima.cs.berkeley.edu A fundamental perspective on artificial intelligence as a comprehensive system. 3. Speech and Language Processing https://web.stanford.edu/~jurafsky/slp3/ An in-depth examination of natural language processing, transformers, and linguistics. 4. Machine Learning: A Probabilistic Perspective https://probml.github.io/pml-book/ An exploration of probabilities, statistics, and the theoretical foundations of machine learning. 5. Understanding Deep Learning https://udlbook.github.io/udlbook/ A contemporary explanation of deep learning principles with strong intuitive insights. 6. Designing Machine Learning Systems https://oreilly.com/library/view/designing-machine-learning/9781098107956/ Strategies for deploying models into production environments. 7. Generative Deep Learning https://github.com/3p5ilon/ML-books/blob/main/generative-deep-learning-teaching-machines-to-paint-write-compose-and-play.pdf Practical applications of generative models and transformer architectures. 8. Natural Language Processing with Transformers https://dokumen.pub/natural-language-processing-with-transformers-revised-edition-1098136799-9781098136796-9781098103248.html Methodologies for constructing natural language processing systems based on transformers. 9. Machine Learning Engineering https://mlebook.com Principles of machine learning engineering and operational deployment. 10. The Hundred-Page Machine Learning Book https://themlbook.com A highly concentrated foundational overview without extraneous detail. 📚🤖

🚀 Sber has released two open-source MoE models: GigaChat-3.1 Ultra and Lightning Both code and weights are available under the MIT license on HuggingFace. 👉 Key details: • Trained from scratch (not a finetune) on proprietary data and infrastructure • Mixture-of-Experts (MoE) architecture Models: 🧠 GigaChat-3.1 Ultra • 702B MoE model for high-performance environments • Outperforms DeepSeek-V3-0324 and Qwen3-235B on math and reasoning benchmarks • Supports FP8 training and MTP ⚡️ GigaChat-3.1 Lightning • 10B model (1.8B active parameters) • Outperforms Qwen3-4B and Gemma-3-4B on Sber benchmarks • Efficient local inference • Up to 256k context Engineering highlights: • Custom metric to detect and reduce generation loops • DPO training moved to native FP8 • Improvements in post-training pipeline • Identified and fixed a critical issue affecting evaluation quality 🌍 Trained on 14 languages (optimized for English and Russian) Use cases: • chatbots • AI assistants • copilots • internal ML systems Sber provides a solid open foundation for developers to build production-ready AI systems with lower infrastructure costs.

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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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PhD Students – How to compare 10 papers in 10 seconds? Meet 𝐒𝐜𝐢𝐒𝐩𝐚𝐜𝐞 – this tool compares papers for you. Here is how it works. 1. Go to https://lnkd.in/dyirEcYG and log in 2. Click on + 𝑠𝑖𝑔𝑛 and upload the 10 papers. 3. After uploading papers, write your prompt. 𝐶𝑜𝑚𝑝𝑎𝑟𝑒 𝑡ℎ𝑒 𝑢𝑝𝑙𝑜𝑎𝑑𝑒𝑑 10 𝑟𝑒𝑠𝑒𝑎𝑟𝑐ℎ 𝑝𝑎𝑝𝑒𝑟𝑠 4. SciSpace will start comparing the papers. 5. You will see the comparison result on right side. 6. Here you will see various insights with paper numbers. 7. At the end, you will see summary of the comparison. 8. SciSpace compares the papers based on: ✓ Similarities in research themes ✓ Differences in approaches ✓ Relative strengths and weaknesses ✓ Gaps identified across papers ✓ Relationships and building upon each other 9. To trace to each paper, click on the 𝑝𝑎𝑝𝑒𝑟 𝑛𝑢𝑚𝑏𝑒𝑟𝑠 10. To trace to exact location, click on 𝑙𝑜𝑐𝑎𝑡𝑒 𝑃𝐷𝐹. Where can you use such comparison? You can use it to: ➝ Understand the related literature. ➝ Position the novelty of your research paper. ➝ Understand niche questions in a research area. ➝ Grasp key insights from a bunch of papers in one go. Try SciSpace today: https://lnkd.in/dyirEcYG