21 articles tagged with "deep learning"
Explore all content related to deep learning. Find tutorials, guides, tips, and insights from our collection of articles on this topic.
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Struggling with low Facial Expression Recognition (FER+) accuracy? Follow my journey from a frustrating 52% to a robust 88% by fixing key issues in data, models, and training.
Struggling with the FER+ dataset? Discover my step-by-step method to achieve 89% validation accuracy using a Vision Transformer, data augmentation, and more.
Remember the LSTM vs. Transformer debate? In 2025, the winner is clear. I'm breaking down the 3 key reasons I—and the industry—ditched LSTMs for good.
Are LSTMs obsolete in 2025? We dive into the brutal truth, comparing them to Transformers and revealing where they still shine in a world of LLMs.
LSTMs or Transformers? The debate is over. Get 5 practical, up-to-date rules for 2025 to choose the right model for your NLP or time-series project.
I built the original YOLOv1 from scratch in 2025. This deep dive reveals 5 hard-won lessons on loss functions, data augmentation, and why it matters now.
Dive deep into the original YOLOv1 paper. Uncover 3 crucial PyTorch implementation secrets for 2025 that tutorials often miss, from vectorized loss to modern data pipelines.
Unlock real-time object detection! Our 7-step guide walks you through building and training YOLOv1 from scratch in PyTorch. Perfect for mastering the fundamentals in 2025.
Tired of failing tests due to floating-point errors? Master tensor precision with our 2025 guide on 3 pro comparison techniques: atol, rtol, and cosine similarity.
Tired of cryptic tensor mismatch errors? Master our 5-step precision debugging framework for 2025 to fix shape issues in PyTorch or TensorFlow quickly and confidently.
Ready to build powerful anomaly detectors in 2025? This step-by-step tutorial guides you through using Anomalib to train and deploy advanced models. Get started!
Dive into Spiking Neural Networks (SNNs) with our ultimate 2025 guide. Learn how SNNs work, why they're more energy-efficient, and their future applications.
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