by Suf | Nov 15, 2024 | Machine Learning, Programming, PyTorch
Introduction unsqueeze() in PyTorch is a function that adds a dimension of size one to a tensor. While this might sound simple, understanding when and why to use it is crucial for many deep learning tasks, especially when working with neural networks and preparing...
by Suf | Nov 14, 2024 | Machine Learning, Programming, PyTorch
Understanding gather() in PyTorch: A Beginner-Friendly Guide Table of Contents Introduction What is gather()? Syntax of gather() How the dim Argument Works Row-wise Selection with dim=1 Common Pitfalls and Errors Column-wise Selection with dim=0 The Book Shelf Analogy...
by Suf | Nov 14, 2024 | Machine Learning, PyTorch
This guide provides an in-depth look at creating custom loss functions in PyTorch, a skill valuable for those working with deep learning frameworks. Whether developing innovative models or exploring new functionalities, mastering custom loss functions in PyTorch...
by Suf | Nov 13, 2024 | Machine Learning, PyTorch
Contents Introduction Gradients in Neural Networks Backpropagation and Gradient Descent Without zero_grad() With zero_grad() Plotting Losses Monitoring Loss Summary When training neural networks in PyTorch, calling zero_grad() is essential before backpropagating...
by Suf | Nov 13, 2024 | Data Science, Machine Learning, PyTorch
PyTorch provides a variety of tensor operations, and understanding the differences between torch.mm, torch.matmul, and torch.mul is essential when working with tensor computations. Although they might look similar, these functions serve different purposes and operate...
by Suf | Apr 7, 2022 | Programming, Python, TensorFlow, Tips
The simplest way to convert a TensorFlow Tensor to a Numpy array is to use the numpy() method. For example, numpy_array = tensor.numpy(). This tutorial will go through how to convert a TensorFlow Tensor to a NumPy array for both TensorFlow 2.x and 1.x with the help of...
