Training pipelines, evaluation, deployment, and monitoring at scale.
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By Krishna Tejpratap Singh A machine learning model can perform well during development and still struggle in production. One reason is that the data used to train the model may differ from the data supplied to it when making real-world…
Subtitle: How to compare machine learning models in real-world environments, evaluate business impact, and promote better models without risking production reliability. By Krishna Tejpratap Singh Building a machine learning model that performs well on a training dataset is only one…
Rotary Positional Embedding (RoPE) is one of the most important positional-encoding techniques in modern Transformers, especially because it is used in models such as LLaMA-family models and many other LLM architectures. The key idea is: Instead of adding a positional…
Accurate QT-interval measurement requires reliable identification of QRS onset and T-wave offset. We present QTANet, a single-lead framework that classifies ECG samples as baseline, QRS or T wave and derives QT from the detected landmarks. We used 179 recordings from the…
This research introduces a non-invasive physiological assessment framework to evaluate metrics from facial video sequences via remote photoplethysmography (rPPG) and a deep learning approach. The system inspects facial videos at the frame level to detect minute blood volume fluctuations linked…
How MLOps Works: A Step-by-Step Guide for Beginners Many teams build a model that works well on a laptop. Then it fails in real life. MLOps helps close that gap. This guide shows each step in plain words. If you…
Comparing paired signals and trajectories is commonly framed as distance or alignment estimation, whereas controlled diagnostics may require identifying concurrent transformations. We formulate the supervised prediction of 19 applied-transformation labels, which need not all produce observable changes, and evaluate 5 1D and fifteen…
Go to the end to download the full example code. Credits: OpenAI kernel team Total running time of the script: (15 minutes 18.821 seconds) Download Jupyter notebook: 06-fused-attention.ipynb Download Python source code: 06-fused-attention.py
This work presents a one-class anomaly detection framework for vibration-based condition monitoring in machining processes using a convolutional neural network autoencoder. Three-axis vibration signals acquired during industrial broaching are transformed into time–frequency spectrograms and combined into a compact three-channel…
CNN for Image Classification is an important application of deep learning that enables computers to recognize and classify objects in images. In this project, we build a Convolutional Neural Network (CNN) using PyTorch to classify images from the CIFAR-10 dataset.