by Suf | Nov 25, 2024 | Data Science, Machine Learning, PyTorch
Introduction Autoencoders are neural networks designed to compress data into a lower-dimensional latent space and reconstruct it. They are useful for tasks like dimensionality reduction, anomaly detection, and generative modeling. In this tutorial, we implement a...
by Suf | Nov 25, 2024 | GPU, Machine Learning, Programming, Python, PyTorch
Introduction PyTorch is a versatile and widely-used framework for deep learning, offering seamless integration with GPU acceleration to significantly enhance training and inference speeds. This guide walks you through setting up PyTorch to utilize a GPU, using Google...
by Suf | Nov 23, 2024 | Data Science, Machine Learning, PyTorch
Table of Contents Introduction The MNIST Challenge Prerequisites Dataset Overview Exploratory Data Analysis (EDA) Building the Model Training the Model Model Evaluation Results Visualization Error Analysis Conclusion and Future Work Introduction Logistic regression is...
by Suf | Nov 21, 2024 | Machine Learning, Programming, PyTorch
Introduction ReLU (Rectified Linear Unit) revolutionized deep learning with its simplicity and efficiency, becoming the go-to activation function for neural networks. Defined as f(x) = max(0, x), ReLU activates only positive inputs, solving issues like vanishing...
by Suf | Nov 17, 2024 | Machine Learning, Programming, Python, PyTorch
Contents Introduction Understanding the Error Common Causes Example to Reproduce Error Inception v3 Specific Requirements Complete Working Solution Best Practices Introduction When working with pre-trained models in PyTorch, particularly convolutional neural networks,...
by Suf | Nov 17, 2024 | Machine Learning, Programming, PyTorch
Contents Introduction Reproducing the Error Fixing the Error Visualizing the Batch Dimension Why Batch Dimensions Are Important Common Mistakes to Avoid Debugging Tensor Shapes Further Reading Summary Introduction One common error when working with pre-trained PyTorch...
