Machine Learning

Summary

This post provides a practical guide to building common neural network architectures using PyTorch. We’ll explore feedforward networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), LSTMs, transformers, autoencoders, and GANs, along with code examples and explanations.

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1️⃣ Understanding PyTorch’s Neural Network Module

PyTorch provides the torch.nn module to build neural networks. It provides classes for defining layers, activation functions, and loss functions, making it easy to create and manage complex network architectures in a structured way.

Summary

This post provides a comprehensive guide to prompt engineering, the art of crafting effective inputs for Large Language Models (LLMs). Mastering prompt engineering is crucial for maximizing the potential of LLMs and achieving desired results.

Effective prompting is the easiest way to enhance your experience with Large Language Models (LLMs).

The prompts we make are our interface to LLMs. This is how we communicate with them. This is why it is important to understand how to do it well.