Deep Learning With Keras 简明教程

Deep Learning with Keras - Introduction

近年来，在人工智能（AI）领域，深度学习已成为热门词汇。多年来，我们使用机器学习（ML）为机器赋予智能。近年来，深度学习因其在预测方面优于传统 ML 技术而变得更加流行。

Deep Learning has become a buzzword in recent days in the field of Artificial Intelligence (AI). For many years, we used Machine Learning (ML) for imparting intelligence to machines. In recent days, deep learning has become more popular due to its supremacy in predictions as compared to traditional ML techniques.

深度学习本质上是指使用大量数据训练人工神经网络（ANN）。在深度学习中，网络会自己学习，因此需要大量数据用于学习。而传统机器学习本质上是一组分析数据并从中学习的算法。然后，他们利用这种学习做出明智的决策。

Deep Learning essentially means training an Artificial Neural Network (ANN) with a huge amount of data. In deep learning, the network learns by itself and thus requires humongous data for learning. While traditional machine learning is essentially a set of algorithms that parse data and learn from it. They then used this learning for making intelligent decisions.

现在，谈论 Keras，它是一个高级神经网络 API，运行在 TensorFlow 之上——一个端到端的开源机器学习平台。使用 Keras，您可以轻松定义复杂的人工神经网络架构，以对您的海量数据进行实验。Keras 还支持 GPU，这对于处理海量数据和开发机器学习模型至关重要。

Now, coming to Keras, it is a high-level neural networks API that runs on top of TensorFlow - an end-to-end open source machine learning platform. Using Keras, you easily define complex ANN architectures to experiment on your big data. Keras also supports GPU, which becomes essential for processing huge amount of data and developing machine learning models.

在本教程中，您将学习在构建深度神经网络中使用 Keras。我们将研究用于教学的实际示例。手头的问题是使用经过深度学习训练的神经网络识别手写数字。

In this tutorial, you will learn the use of Keras in building deep neural networks. We shall look at the practical examples for teaching. The problem at hand is recognizing handwritten digits using a neural network that is trained with deep learning.

为了让您对深度学习更加兴奋，以下是在此处关于深度学习的谷歌趋势的屏幕截图：

Just to get you more excited in deep learning, below is a screenshot of Google trends on deep learning here −

screenshot google trends

正如您在图表中看到的，过去几年对深度学习的兴趣一直在稳步增长。计算机视觉、自然语言处理、语音识别、生物信息学、药物设计等许多领域已成功应用了深度学习。本教程将让您快速入门深度学习。

As you can see from the diagram, the interest in deep learning is steadily growing over the last several years. There are many areas such as computer vision, natural language processing, speech recognition, bioinformatics, drug design, and so on, where the deep learning has been successfully applied. This tutorial will get you quickly started on deep learning.

因此请继续往下读！

So keep reading!