Learning
-
Understanding Multilinear Regression
Read Full Article: Understanding Multilinear Regression
Multilinear regression extends the concept of simple linear regression by incorporating multiple features, allowing the model to explore additional dimensions beyond a single line. Each new feature adds a new direction, transforming the model's output space from a line to a plane, and eventually to a hyperplane as more features are added. This expansion of the output space means that the set of reachable outputs becomes larger, which can reduce error or maintain it, as the model gains the ability to move in more directions. Understanding this concept is crucial for leveraging multilinear regression to improve model accuracy and performance.
-
YOLOv8 Tutorial: Classify Agricultural Pests
Read Full Article: YOLOv8 Tutorial: Classify Agricultural Pests
This tutorial provides a comprehensive guide for using the YOLOv8 model to classify agricultural pests through image classification. It covers the entire process from setting up the necessary Conda environment and Python libraries, to downloading and preparing the dataset, training the model, and testing it with new images. The tutorial is designed to be practical, offering both video and written explanations to help users understand how to effectively run inference and interpret model outputs. Understanding how to classify agricultural pests using machine learning can significantly enhance pest management strategies in agriculture, leading to more efficient and sustainable farming practices.
-
Clean PyTorch Implementations of 50+ ML Papers
Read Full Article: Clean PyTorch Implementations of 50+ ML Papers![[D] Clean, self-contained PyTorch re-implementations of 50+ ML papers (GANs, diffusion, meta-learning, 3D)](https://www.tweakedgeek.com/wp-content/uploads/2026/01/featured-article-8490-300x171.png)
A repository offers clean and self-contained PyTorch implementations of over 50 machine learning papers, covering areas like GANs, VAEs, diffusion models, meta-learning, and 3D reconstruction. These implementations are designed to remain true to the original methods while minimizing unnecessary code, making them easy to run and inspect. The goal is to reproduce key results where feasible, providing a valuable resource for understanding and experimenting with advanced machine learning concepts. This matters because it facilitates learning and experimentation in machine learning by providing accessible and concise code examples.
-
Recollections from Bernard Widrow’s Neural Network Classes
Read Full Article: Recollections from Bernard Widrow’s Neural Network Classes![[D] I took Bernard Widrow’s machine learning & neural networks classes in the early 2000s. Some recollections](https://www.tweakedgeek.com/wp-content/uploads/2026/01/featured-article-8489-300x171.png)
Bernard Widrow, a pioneer in neural networks and signal processing, left a lasting impact on his students by presenting neural networks as practical engineering systems rather than speculative ideas. His teachings in the early 2000s at Stanford highlighted the completeness of his understanding of neural networks, covering aspects like learning rules, stability, and hardware constraints. Widrow's approach was grounded in practicality, emphasizing the real-world implementation of concepts like reinforcement learning and adaptive filtering long before they became mainstream. His professional courtesy and engineering-oriented mindset influenced many, demonstrating the importance of treating learning systems as tangible entities rather than mere theoretical constructs. This matters because it highlights the enduring relevance of foundational engineering principles in modern machine learning advancements.
-
Train Models with Evolutionary Strategies
Read Full Article: Train Models with Evolutionary Strategies
The paper discussed demonstrates that using only 30 random Gaussian perturbations can effectively approximate a gradient, outperforming GRPO on RLVR tasks without overfitting. This approach significantly speeds up training as it eliminates the need for backward passes. The author tested and confirmed these findings by cleaning up the original codebase and successfully replicating the results. Additionally, they implemented LoRA and pass@k training, with plans for further enhancements, encouraging others to explore evolutionary strategies (ES) for training thinking models. This matters because it offers a more efficient method for training models, potentially advancing machine learning capabilities.
-
15 Years of Evolving ML Research Notes
Read Full Article: 15 Years of Evolving ML Research Notes![[D] My Machine learning research notes: 15 years of continuous writing and 8.8k GitHub stars!](https://www.tweakedgeek.com/wp-content/uploads/2026/01/featured-article-8464-300x171.png)
Over 15 years of continuous writing and updates have resulted in a comprehensive set of machine learning research notes that have garnered 8.8k stars on GitHub. These notes cover both theoretical and practical aspects of machine learning, providing a dynamic and evolving resource that adapts to the fast-paced changes in the industry. The author argues that traditional books cannot keep up with the rapid advancements in machine learning, making a continuously updated online resource a more effective way to disseminate knowledge. This matters because it highlights the importance of accessible, up-to-date educational resources in rapidly evolving fields like machine learning.
-
Revamped AI Agents Tutorial in Python
Read Full Article: Revamped AI Agents Tutorial in Python
A revamped tutorial for building AI agents from scratch has been released in Python, offering a clearer learning path with lessons that build on each other, exercises, and diagrams for visual learners. The new version emphasizes structure over prompting and clearly separates LLM behavior, agent logic, and user code, making it easier to grasp the underlying concepts. Python was chosen due to popular demand and its ability to help learners focus on concepts rather than language mechanics. This updated tutorial aims to provide a more comprehensive and accessible learning experience for those interested in understanding AI agent frameworks like LangChain or CrewAI. This matters because it provides a more effective educational resource for those looking to understand AI agent frameworks, potentially leading to better implementation and innovation in the field.
-
LEMMA: Rust-Based Neural-Guided Math Solver
Read Full Article: LEMMA: Rust-Based Neural-Guided Math Solver![[P] LEMMA: A Rust-based Neural-Guided Math Problem Solver](https://www.tweakedgeek.com/wp-content/uploads/2026/01/featured-article-8445-300x171.png)
LEMMA is a Rust-based neural-guided math problem solver that has been significantly enhanced with over 450 mathematics rules and a neural network that has grown from 1 million to 10 million parameters. This expansion has improved the model's accuracy and its ability to solve complex problems across multiple domains. The project, which has been in development for seven months, shows promising results and invites contributions from the community. This matters because it represents a significant advancement in AI's capability to tackle complex mathematical problems, potentially benefiting various fields that rely on advanced computational problem-solving.
-
TinyGPT: Python GPT Model Without Dependencies
Read Full Article: TinyGPT: Python GPT Model Without Dependencies
TinyGPT is a simplified, educational deep learning library created to implement a GPT model from scratch in Python without any external dependencies. This initiative aims to demystify the complexities of frameworks like PyTorch by providing a minimal and transparent approach to understanding the core concepts of deep learning. By offering a clearer insight into how these powerful models function internally, TinyGPT serves as a valuable resource for learners eager to comprehend the intricacies of deep learning models. This matters because it empowers individuals to gain a deeper understanding of AI technologies, fostering innovation and learning in the field.
-
Visualizing the Semantic Gap in LLM Inference
Read Full Article: Visualizing the Semantic Gap in LLM InferenceThe concept of "Invisible AI" refers to the often unseen influence AI systems have on decision-making processes. By visualizing the semantic gap in Large Language Model (LLM) inference, the framework aims to make these AI-mediated decisions more transparent and understandable to users. This approach seeks to prevent users from blindly relying on AI outputs by highlighting the discrepancies between AI interpretations and human expectations. Understanding and bridging this semantic gap is crucial for fostering trust and accountability in AI technologies.