David

生成式预训练 Transformer（GPT）是现代人工智能中的一个重要突破，驱动了许多尖端的自然语言处理（NLP）应用。Generative Pre-trained Transformer (GPT) is a significant breakthrough in modern artificial intelligence, driving many cutting-edge applications in natural language processing (NLP). 在本文中，我们将通过更复杂的代码示例、详细的可视化图表，以及对其他 NLP 模型的比较，深入探讨 GPT 的工作原理、局限性及其在实际应用中的潜力。In this article, we will explore the working principles, limitations, and potential of GPT in practical applications through more complex code examples, detailed visualizations, and comparisons with other NLP

Backpropagation is a fundamental algorithm in training neural networks. Let’s break it down step by step:反向传播是训练神经网络的基本算法。让我们一步步来解析它: Imagine a simple neural network with three layers: input, hidden, and output.想象一个简单的三层神经网络:输入层、隐藏层和输出层。 Each circle represents a neuron, and the lines represent the connections (weights) between neurons.每个圆圈代表一个神经元,线条代表神经元之间的连接(权重)。 Data flows from left to right. Each neuron receives inputs, processes them, and

In the previous blog post, we learned about the basic concepts of cost functions and gradient descent.在上一篇博客中，我们了解了成本函数和梯度下降的基本概念。 Today, we will delve into the core of neural network training – the backpropagation algorithm, and answer some common questions.今天，我们将深入探讨神经网络的训练核心——反向传播算法，并回答一些大家关心的问题。 1. What is Backpropagation? 什么是反向传播？ Backpropagation is an efficient algorithm used to calculate the gradient of the cost

在上一篇博客中，我们了解了神经网络的基本结构和工作原理。 In the previous blog post, we learned about the basic structure and working principles of neural networks. 今天，我们将深入探讨神经网络如何学习，以及两个关键概念：成本函数和梯度下降。 Today, we will delve into how neural networks learn and explore two key concepts: cost functions and gradient descent. 1. 神经网络如何学习？ 1. How do Neural Networks Learn? 想象一下，你是一个新手司机，正在学习如何驾驶汽车。一开始，你可能会犯很多错误，比如转弯太急、刹车太晚等等。但是，通过不断练习和从错误中学习，你会逐渐掌握驾驶技巧，变得越来越熟练。 Imagine you are a novice driver learning how