Learn how to build a digit recognition model from scratch using PyTorch! This beginner-friendly deep learning project walks you through loading the MNIST dataset, creating a neural network, training ...
Introduction: The COVID-19 pandemic accelerated global online education, which faces “shallow learning” challenges. Deep learning is key to student competencies. Based on sociocultural theory, this ...
Conceptual render of the Digitally Enabled Efficient Propeller (D.E.E.P) (Credit: Enki Marine) Consortium members at the project kick-off meeting at DEEP Manufacturing’s HQ in Bristol (Credit: D.E.E.P ...
Abstract: This senior thesis develops a real-time handwritten digit identification system using a Raspberry Pi 3B+ with a camera module, leveraging a lightweight CNN optimized with MNIST. The project ...
• Architecture: 4-layer CNN (convolutional layers with 32, 64, 128, and 256 filters) → Max pooling → Dropout → Fully connected layers. • Training: Dataset: MNIST (28×28 grayscale digits).
Objective: This study aimed to evaluate the effectiveness of deep-learning models using transrectal ultrasound (TRUS) video clips in predicting prostate cancer. Methods: We manually segmented TRUS ...
This project implements a CNN-based image classification model using the MNIST dataset to recognize handwritten digits from 0 to 9. It is built using TensorFlow, trained in Google Colab, and ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results
Feedback