by Suf | Dec 19, 2024 | Machine Learning, PyTorch
In deep learning, parameters are the backbone of every neural network. Whether you’re building a simple classifier or a complex deep learning model, understanding how to manage parameters effectively in PyTorch is crucial for success. This comprehensive guide...
by Suf | Dec 10, 2024 | Data Science, Machine Learning, Python, PyTorch
What is Tensor Transposition? Tensor transposition is a fundamental operation in deep learning that rearranges the dimensions of a tensor. In PyTorch, understanding transpose operations is crucial for tasks like data preprocessing, model architecture design, and...
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...
