python可以这样学读后感_《Python深度学习》读后感

Background

Last month, I teamed up with two friends to take part in a competition which requires to recognize bank cards basing on deep learning.

However, I did not know about deep learning at all! Actually, I messed my 校创 last year because I did not understand neural network and I could not build one.

Then I found a book in my shelf called Python深度学习 by N.D.Lewis. It’s a very thin book. And I finished reading it this morning.

Now, you may ask,

Do you know how to do the bank card recognition?

My answer is NO.

Have you built a neural network?

My answer is also NO.

Can you?

I don’t know.

What do you know?

Some terms.

That’s why I decide to call this post 读后感 rather than 教程.

What is Deep Learning & what is DNN?深度学习是机器学习与神经网络、人工智能、图形化建模、优化、模式识别和信号处理等技术融合后产生的一个领域。

简单来说，深度神经网络是一种用多层抽象来表示概念或特征的方式。

Neural Networks usually consist of three parts: an input layer, the hidden layer(s) and an output layer. There are neurons in each layer, which take several inputs, produce an output according to the weighted sum and the activation function, then pass the output forward, which work like the real neurons.(Don’t ask me about this. I haven’t studied Biology for 4 years.)(You can also find some pictures to help you understand.)

When the network is learning, the neurons change their weights to make the output closer to the correct output. Sometimes it can even find the inner connections in the data without the correct output.

In my opinion, DNN is just NN with multiple hidden layers.

It is very different from my understanding of programming. Without machine learning and neural network, we can find or invent the rules to make the programs work as we expect.

However, we can also make the programs produce almost correct outputs even if we can not find the rules or patterns with the help of machine leaning and neural network.

Why is it powerful?隐藏层是DNN的秘密所在。

深度学习的威力来自于用适量的并行非线性步骤对非线性数据进行分类或预测的能力。

如果决策规则可以通过复杂函数进行数学建模，那么就可以使用DNN。重要的是，你不需要事先知道函数的形式。

If you have a lot of data, and you want to find out the inner connections to do classfication or prediction, which do not have to be perfectly precise, DNN is a good choice.

Some techniques

After reading the book, you may draw conclusions like these:

The learning rate should not be too high or too low.

Too many or too few neurons will affect the performance.

Dropout may improve the performance.

Perhaps another activation function works better.

……

There is no optimal choice, we have to find the most suitable model for a specific case in experiments. Experimenting is also called 调参 in Chinese.

So I still know nothing about DNN.

深度学习需要耐心和试验。

数据科学是由偶尔的成功点缀的一系列失败。

The author intends to encourage us to embrace failures. However, I feel more than depressed.

The author mainly use sknn.mlp to develop his neural networks, and he uses python2. But I only have python3 on my laptop…

Anyway, it’s definitely a good book for starters. It’s not difficuit to understand and it’s not so thick that you may feel desperate.

See you Next time.

P.S.

I should have done my lab work and reviewed for the final exam of CE162.

But I feel like updating my blog.

This is my second post in English, I will still use English if I am not desperate.

My next post is probably about F1 2016, a game that I have been playing on my iPad.

python可以这样学读后感_《Python深度学习》读后感相关推荐

python 数组合并排重_并排深度学习：Julia vs Python
python 数组合并排重 Julia could possibly be the biggest threat to Python. For a variety of applications, J ...
基于python的入侵检测系统毕设_基于深度学习的射频指纹的物联网设备入侵检测...
摘要:物联网(IoT)和4G/5G无线网络增加了大量设备和新服务,商用现货(COTS)物联网设备得到了广泛部署.为了确保具备无线传输能力的这些系统的安全运作,射频(RF)监视对于监视它们在RF频谱中的 ...
python图像识别算法识别数量_使用深度学习(CNN)算法进行图像识别工作时，有哪些data augmentation 的奇技淫巧？...
估计很多搞深度学习的都遇到过这个情况:有不错的想法,可以用深度学习模型实现.于是兴致勃勃的上网找相关数据集,结果却发现只有很少一部分图像. 你想起来,很多常见的数据集都有成千上万张井然有序的图像,你还 ...
回归素材(part9)--PYTHON机器学习手册-从数据预处理到深度学习
学习笔记,仅供参考,有错必纠 PYTHON机器学习手册-从数据预处理到深度学习通过正则化减少方差我们可以使用岭回归或者Lasso回归,介绍回归模型的方差.
python人工智能课程实例_python人工智能AI深度学习/机器学习全套课程视频教程+ppt+代码...
这是一套Python/人工智能/AI/机器学习/深度学习全套实战课程,包含视频教程以及文档.源码等,欢迎下载 01. python数据分析与机器学习实战 02.深度学习入门视频课程(上篇) 03.深 ...
深度学习将灰度图着色_通过深度学习为视频着色
深度学习将灰度图着色零本地设置/ DeOldify / Colab笔记本 (Zero Local Setup / DeOldify / Colab Notebook) "Haal Kais ...
深度学习模型建立过程_所有深度学习都是统计模型的建立
深度学习模型建立过程 Deep learning is often used to make predictions for data driven analysis. But what are th ...
深度学习：在图像上找到手势_使用深度学习的人类情绪和手势检测器：第1部分
深度学习:在图像上找到手势情感手势检测 (Emotion Gesture Detection) Has anyone ever wondered looking at someone and tri ...
python小白逆袭大佬_飞桨深造学院百度深造7天打卡第6期：python小白反攻大神的经验,深度,学习,日,第六期,Python,小白逆袭,结营,心得...
先来说说这七天的课程里有哪些具体收获吧!!! Day1-人工智能概述与入门基础第一天只是学习了一些人工智能的基本的有关知识,然后讲了讲python的基本语法(哈哈,对于刚刚学完python的我,第一 ...
正则化的通俗解释_干货|深度学习中的正则化技术概述（附Python+keras实现代码）...
点击上方"AI遇见机器学习",选择"星标"公众号重磅干货,第一时间送达介绍数据科学研究者们最常遇见的问题之一就是怎样避免过拟合.你也许在训练模型的时候也遇 ...

python可以这样学读后感_《Python深度学习》读后感

python可以这样学读后感_《Python深度学习》读后感相关推荐

最新文章

热门文章