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试用DataCamp for Business课程描述
Learn to Use Convolutional Neural Networks in Python
Image model often requires deep learning methods that use data to train neural network algorithms to do various machine learning tasks. Convolutional neural networks (CNNs) are particularly powerful neural networks that you'll use to classify different types of objects for the analysis of images. This four-hour course will teach you how to construct, train, and evaluate CNNs using Keras.Turning images into data and teaching neural networks to classify them is a challenging element of deep learning with extensive applications throughout business and research, from helping an eCommerce site manage inventory more easily to allowing cancer researchers to quickly spot dangerous melanoma.
Discover Keras CNNs
The first chapter of this course covers how images can be seen as data, and how you can use Keras to train a neural network to classify objects found in images.The second chapter will cover convolutions, a fundamental part of CNNs. You’ll learn how they operate on image data and learn how to train and tweak your Keras CNN using test data. Later chapters go into more detail and teach you how to create a deep learning network.
Build Your Own Keras Neural Network
You’ll end the course by learning the different ways that you can track how well a CNN is doing and how you can improve their performance. At this point, you’ll be able to build Keras neural networks, optimize them, and visualize their responses across a range of applications.先决条件
Introduction to Deep Learning with Keras1
Image Processing With Neural Networks
Convolutional neural networks use the data that is represented in images to learn. In this chapter, we will probe data in images, and we will learn how to use Keras to train a neural network to classify objects that appear in images.
2
Using Convolutions
Convolutions are the fundamental building blocks of convolutional neural networks. In this chapter, you will be introducted to convolutions and learn how they operate on image data. You will also see how you incorporate convolutions into Keras neural networks.
3
Going Deeper
Convolutional neural networks gain a lot of power when they are constructed with multiple layers (deep networks). In this chapter, you will learn how to stack multiple convolutional layers into a deep network. You will also learn how to keep track of the number of parameters, as the network grows, and how to control this number.
4
Understanding and Improving Deep Convolutional Networks
There are many ways to improve training by neural networks. In this chapter, we will focus on our ability to track how well a network is doing, and explore approaches towards improving convolutional neural networks.
Image Modeling with Keras
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