deep-learning

Recently, I got tired of depending on paid cloud models for every coding experiment. Cloud models are great. They are fast, convenient, and usually very capable. But they also come with the usual baggage: cost, rate limits, internet dependency, privacy questions, and that small feeling that every serious coding workflow is rented from someone else's GPU. So I started exploring local LLMs properly…

We have many threads on AI, which are mostly AI/LLM, e.g,. ChatGPT, Claude, etc. It is important to draw a distinction between AI/LLM and AI/ML/DL, where ML = Machine Learning and DL = Deep Learning. AI is a broad technology; the AI/ML/DL is being developed to handle large data sets, and even... Read more

IntroductionWe previously investigated color constancy in photorealistic virtual reality (VR) and developed a Deep Neural Network (DNN) that predicts reflectance from rendered images.MethodsWe combine both approaches to compare and study a model and human performance with respect to established color constancy mechanisms: local surround, maximum flux and spatial mean. Rather than evaluating the m…

Scientific Reports, Published online: 05 May 2026; doi:10.1038/s41598-026-43204-5 Transformer-assisted hierarchical deep reinforcement learning for energy and spectrum efficient MIMO-MC-CDMA in 6G networks

Scientific Reports, Published online: 05 May 2026; doi:10.1038/s41598-026-49227-2 A novel diabetic retinopathy detection from fundus images using hybrid quantum convolutional neural network models

Nature Communications, Published online: 04 May 2026; doi:10.1038/s41467-026-72413-9 Rocket introduces a self-play RL framework for automated hyperparameter optimization, handling mixed types without priors. It scales large datasets via reward approximation, achieving expert-level performance while cutting time and cost in real-world deployments.

Solving multiplayer games with function approximation The post Playing Connect Four with Deep Q-Learning appeared first on Towards Data Science .

IntroductionWeather classification plays a crucial role in applications such as environmental monitoring, disaster management, and smart city infrastructure. Accurate and efficient classification of weather conditions from images remains a challenging task due to variations in illumination, texture, and atmospheric conditions.MethodsThis study proposes an efficient deep learning framework for mul…

Accurate detection of pediatric fractures in radiographs remains challenging due to subtle visual cues and the high prevalence of false-positive detections produced by automated systems. To address this limitation, we propose a lightweight region-of-interest (Region of Interest) adjudication framework that operates as a second-stage verification module to refine detector-generated candidates. The…

This work presents a hybrid deep learning approach for identifying students who are likely to experience academic difficulties in virtual learning environments. The proposed framework is evaluated on the Open University Learning Analytics Dataset (OULAD) and combines two complementary types of information: temporal patterns of learner activity captured using Bidirectional Long Short-Term Memory (…

Chest X-ray (CXR) interpretation is essential for diagnosing pulmonary diseases, yet manual reading remains slow and prone to human error, especially in high-volume or resource-limited settings. To address delayed diagnoses and improve clinical efficiency, this study introduces (HyRA-CXR), a hybrid residual–attention convolutional neural network for automated CXR classification. The proposed mode…

How I Built a Multi-Model AI Council That Runs on a Mac Mini I run 4 AI agents (Claude Code, OpenClaw, Hermes/DeepSeek, LM Studio) on a single Mac Mini M4 with 32GB RAM. They share memory through Obsidian + ChromaDB, communicate via ACP bridge, and delegate tasks using a tiered hierarchy. Here's what actually works and what breaks. The Stack Orchestrator : DeepSeek V4 Pro (API) — plans, delegates…

An AI system may spot pancreatic cancer long before it becomes visible. A new artificial intelligence system called REDMOD may be able to spot pancreatic cancer long before doctors can see it. The model identifies faint tissue changes linked to pancreatic ductal adenocarcinoma, the most common and deadliest form of the disease, according to research [...]

Scientific Reports, Published online: 03 May 2026; doi:10.1038/s41598-026-48085-2 A multi-modal deep learning framework for enhanced breast cancer diagnosis using mammograms and clinical data

Scientific Data, Published online: 02 May 2026; doi:10.1038/s41597-026-07344-7 A synthetic dataset for time series super-resolution with deep learning

Building Jungle Grid: Real AI Workloads You Can Run Without Manually Picking GPUs GPU infrastructure sounds simple when described from the outside. You pick a GPU. You run a container. You wait for the result. That is the clean version. The real version is messier. You think about VRAM. You think about provider availability. You think about regions. You think about whether the image will actually…

AI wildlife deepfakes are flooding social media, even targeting celebrity eagles Jackie and Shadow. Experts warn the “AI slop" poses real-world danger for animals and people.

Introduction ===================================================== Real-time object recognition has been a long-standing challenge in computer vision, particularly in environments where data is scarce and latency is a concern. Traditional approaches to object recognition rely heavily on pre-trained models, which may not generalize well to new environments with limited data. In recent years, resea…

On April 29, 2026, DeepSeek officially launched the gray-scale testing of its "Image Recognition Mode." For users who've been relying on the pure-text version of DeepSeek for the past year, this news is akin to a blind person regaining sight. From now on, when you upload a photo to DeepSeek, it no longer just "sees a file name" — it genuinely understands image content. It can identify the stylist…