Deep Learning Handwritten Notes.
#DL
#CNN

@Machine_learn

Lots of math for CS & ML. Looks pretty interesting.

📚 Book

@Machine_learn

Click-Calib: A Robust Extrinsic Calibration Method for Surround-View Systems

Surround-View System (SVS) is an essential component in Advanced Driver Assistance System (ADAS) and requires precise calibrations.

Paper: https://arxiv.org/pdf/2501.01557v2.pdf

Code: https://github.com/lwangvaleo/click_calib

Dataset: WoodScape

@Machine_learn

All Cheat Sheets
Machine Learning, Deep Learning,
Artificial Intelligence

@Machine_learn

Free access to our secret channels ✅

📚 Free Data Science Books
👨‍💻 Programming Handwritten Notes
🎁 Python Free Courses
🤖 Learn AI with ChatGPT
🏆 Data Science Projects
👩‍🎓 Coding Projects
💝 Free Coding Certified Courses
💪 Quiz and Job Opportunities
And Many More......

✅ Join now : https://www.tg-me.com/machinelearning_deeplearning

✅ Data Science & AI Jobs

Join fast before I delete the link ❤️

Foundations of Geometry. DAVID HILBERT, PH. D.

📚 Book


@Machine_learn

🎬 Random Graphs

🎞 Watch
▫️Part 1
▫️Part 2

@Machine_learn

📃 Perspectives on Computational Enzyme Modeling: From Mechanisms to Design and Drug Development


📎 Study the paper


@Machine_learn

JanusFlow: Harmonizing Autoregression and Rectified Flow for Unified Multimodal Understanding and Generation

We present JanusFlow, a powerful framework that unifies image understanding and generation in a single model. JanusFlow introduces a minimalist architecture that integrates autoregressive language models with rectified flow, a state-of-the-art method in generative modeling. Our key finding demonstrates that rectified flow can be straightforwardly trained within the large language model framework, eliminating the need for complex architectural modifications. To further improve the performance of our unified model, we adopt two key strategies: (i) decoupling the understanding and generation encoders, and (ii) aligning their representations during unified training. Extensive experiments show that JanusFlow achieves comparable or superior performance to specialized models in their respective domains, while significantly outperforming existing unified approaches across standard benchmarks. This work represents a step toward more efficient and versatile vision-language models.

Paper: https://arxiv.org/pdf/2411.07975v1.pdf

Code: https://github.com/deepseek-ai/janus

Datasets: GQA MMBench MM-Vet SEED-Bench

@Machine_learn

DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Paper submitted by #DeepSeek team has generated significant attention in the AI community.

This work addresses the enhancement of reasoning capabilities in Large Language Models (LLMs) through the application of reinforcement learning techniques. The authors introduce a novel framework, DeepSeek-R1, which aims to improve LLM reasoning abilities by incorporating incentives for logical reasoning processes within their training. This integration of reinforcement learning allows LLMs to go beyond basic linguistic processing, developing sophisticated reasoning methods that can boost performance across a wide array of complex applications.

This approach has cause lots of discussions in different communities, but it definitely opens up the whole new direction of development for the research.

Paper: https://arxiv.org/abs/2501.12948

#nn #LLM

@Machine_learn