Keras课程–学习Python深度学习和神经网络

Keras is a neural network API written in Python and integrated with TensorFlow. You can learn how to use Keras in a new video course on the freeCodeCamp.org YouTube channel.

Keras是一个用Python编写并与TensorFlow集成的神经网络API。您可以在freeCodeCamp.org YouTube频道的新视频课程中学习如何使用Keras 。

In this course from deeplizard, you will learn how to prepare and process data for artificial neural networks, build and train artificial neural networks from scratch, build and train convolutional neural networks (CNNs), implement fine-tuning and transfer learning, and more.

在Deeplizard的本课程中，您将学习如何准备和处理人工神经网络的数据，从头开始构建和训练人工神经网络，构建和训练卷积神经网络(CNN)，实现微调和转移学习等。

Each section of the course focuses on a specific concept, and shows how the full implementation is done in code using Keras and Python.

本课程的每个部分都专注于一个特定的概念，并说明如何使用Keras和Python在代码中完成完整的实现。

You will learn to build some networks from scratch. Others will be pre-trained state-of-the-art models that you'll get to fine-tune to the data. Then you'll learn how to deploy models using both front-end and back-end deployment techniques.

您将学习从头开始构建一些网络。其他将是经过预训练的最新模型，您将可以对数据进行微调。然后，您将学习如何使用前端和后端部署技术来部署模型。

Here's the full course syllabus:

这是完整的课程提纲：

第1部分：人工神经网络基础 (Part 1: Artificial Neural Network Basics)

Section 1: Intro to Keras and neural networks

第1节：Keras和神经网络入门

Processing data

处理数据
Building and training neural networks

建立和训练神经网络
Validation and inference

验证与推论
Saving and loading models

保存和加载模型

Section 2: Convolutional Neural Networks (CNNs)

第2节：卷积神经网络(CNN)

Image processing

图像处理
Building and training CNNs

建立和培训CNN
Using CNNs for inference

使用CNN进行推理

Section 3: Fine-tuning and transfer learning

第3节：微调和迁移学习

Intro to fine-tuning and VGG16 model

微调和VGG16模型简介
Implement fine-tuning on VGG16 model

在VGG16模型上实施微调
Using fine-tuned models for inference

使用微调的模型进行推理
Intro to MobileNet

MobileNet简介
Fine-tuning MobileNet on subset of data

在数据子集上微调MobileNet

Section 4: Additional topics

第4节：其他主题

Data augmentation

资料扩充
Keras' image labeling implementation

Keras的图像标签实现
Achieving reproducible results

实现可重复的结果
Learnable parameters

可学习的参数

第2部分：神经网络模型部署 (Part 2: Neural network model deployment)

Section 1: Deployment with Flask

第1节：使用Flask进行部署

Introduction to Flask and web services

Flask和Web服务简介
Build a simple Flask app and web app

构建一个简单的Flask应用程序和Web应用程序
Send and receive data with Flask

使用Flask发送和接收数据
Host neural network with Flask

用Flask托管神经网络
Build neural network web app to interact with Flask service

构建神经网络Web应用程序以与Flask服务进行交互
Integrating data visualization with D3, DC, Crossfilter

将数据可视化与D3，DC，Crossfilter集成
Alternative ways to access neural network from Powershell and Curl

从Powershell和Curl访问神经网络的替代方法
Information privacy and data protection

信息隐私和数据保护