deep-learning

We’ve all been there: waking up feeling like a zombie despite getting eight hours of sleep. While wearables give us data, they often fail to give us foresight . What if you could predict your stress levels 24 hours in advance? 🚀 In this tutorial, we are going to tackle HRV prediction (Heart Rate Variability) using a state-of-the-art Temporal Convolutional Network (TCN) . By leveraging the Oura Ri…

In this article, we will explore how to implement an LSTM using PyTorch and Lightning . For more details about LSTMs, there is a separate series of articles available here . Imports To begin, we first import the required modules. import torch import torch.nn as nn import torch.nn.functional as F Introducing a New Optimizer We also introduce a new optimizer: from torch.optim import Adam Adam is us…

Nature Communications, Published online: 21 June 2026; doi:10.1038/s41467-026-74716-3 Single-molecule fluorescence imaging struggles to distinguish signals from different molecular events when intensity profiles appear similar. Here, the authors introduce smDeepFLUOR, a deep learning framework that classifies spatiotemporal fluorescence signals with high accuracy across diverse molecular events, …

The Quest Begins (The "Why") Ever felt like you’re stuck in a loop watching those “AI will make you rich” headlines flash across your feed? I was there, coffee in hand, scrolling through yet another blog promising a “sure‑fire” stock‑picking neural net that supposedly turned $100 into a fortune overnight. It sounded like the holy grail—like Neo finally seeing the Matrix code and knowing exactly w…

Scientific Reports, Published online: 20 June 2026; doi:10.1038/s41598-026-40210-5 Development of an anemia detection model in emergency departments using lip region images based on medical knowledge and deep learning technology

Why Custom Inference in DeepStream? The post Building a Custom GStreamer Plugin for NVIDIA DeepStream appeared first on Towards Data Science .

Two converging crises—uncontrolled operational expenditure and significant environmental hazards—have been aggravated by the exponential scaling of cloud infrastructure. This has led to a greater focus on the efficiency of Financial Operations (FinOps). Current monitoring methods exhibit a major flaw: passive dashboards require constant human monitoring and lead to alert fatigue, whereas deep lea…

Depression is characterized by high prevalence, high recurrence, high disability and high mortality, which seriously affects people’s work and life. Among various behavioral biomarkers, speech-based features have gained increasing attention in depression detection due to their non-invasive nature, affordability, and rich capacity for conveying affective states. However, conventional depression re…

ObjectiveTo develop a multimodal deep learning–based classification model for adolescent bipolar depression (ABD) with verbal auditory hallucinations (AVHs).MethodsA retrospective analysis was conducted on 47 untreated ABD patients within 30 days, between January 2024 and August 2025. Comprehensive clinical data were collected, including sex, age, age at onset, years of education, and the presenc…

Build-in-public. This is the real architecture behind running ~50 local AI agents on 6GB of VRAM — one GPU lock, an eviction watchdog, a resource governor, and a model router. Originally posted on my blog. The question I get most often is some version of "there's no way you run that many agents on a 6GB laptop GPU." The honest answer: not the way you're picturing it. I don't run 50 models at once…

Nature Communications, Published online: 18 June 2026; doi:10.1038/s41467-026-74059-z Predicting protein splitability is pivotal for engineering functional variants. Here the authors present SplitSeek-Pro, a deep learning model integrating sequence and 3D features to achieve accurate residue-resolution split site prediction for protein design.