python深度学习入门_Python深度学习入门

python深度学习入门

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Deep learning is a type of machine learning that’s growing at an almost frightening pace. Nearly every projection has the deep learning industry expanding massively over the next decade. This market research report, for example, expects deep learning to grow 71x in the US and more than that globally over the next ten years. There’s never been a better time than now to get started.

深度学习是一种以几乎令人恐惧的速度增长的机器学习。 在接下来的十年中,几乎所有的预测都将使深度学习行业大规模发展。 例如, 这份市场研究报告预计,在未来十年中,深度学习在美国的增长将是全球的71倍,并将超过全球的增长。 从没有比现在更好的时间开始了。

To make that start easier for you, we’ve just launched a new course: Deep Learning Fundamentals.

为了让您更轻松地开始学习我们刚刚开设了新课程: 深度学习基础知识 。

This course is designed to give you an introduction to neural networks and deep learning. You’ll start with the theories behind these concepts, and gen familiar with representing linear and logistic regression models as graphs. Then you’ll start digging deeper into topics like nonlinear activation functions and work on improving your models by adding hidden layers of neurons, using the scikit-learn package in Python. Finally, you’ll build a deep learning model that’s capable of looking at images of handwritten numbers and identifying/classifying them correctly.

本课程旨在为您介绍神经网络和深度学习。 您将从这些概念背后的理论开始,并熟悉将线性和逻辑回归模型表示为图形的方法。 然后,您将开始更深入地研究诸如非线性激活函数之类的主题,并使用Python中的scikit-learn包通过添加神经元的隐藏层来改进模型。 最后,您将建立一个深度学习模型,该模型能够查看手写数字的图像并正确识别/分类它们。

为什么要深入学习 (Why you should dive into deep learning)

增加您的收入 (Boost your earnings)

Although salaries for general data scientists are already excellent, as specialists, machine learning and deep learning engineers can command even higher rates. According to Indeed.com data from the US, for example, machine learning engineer salaries average around 13% higher than data scientist salaries.

尽管一般数据科学家的薪水已经很高,但作为专家,机器学习和深度学习工程师的薪水甚至更高。 例如,根据来自美国的Indeed.com数据 ,机器学习工程师的薪水平均比数据科学家的薪水高约13%。

Having some deep learning skills can also help your resume stand out from the herd when it comes to applying for data science jobs, even if you haven’t yet reached the level of deep learning specialist.

拥有一些深度学习技巧也可以帮助您的简历在申请数据科学工作时脱颖而出,即使您尚未达到深度学习专家的水平。

对深度学习的需求正在增长 (Demand for deep learning is growing)

There’s no doubt that machine learning is a fast-growing field, and within it, deep learning is also growing at a breakneck pace. Specific market projections vary from firm to firm to firm, but everybody agrees on the general trend: demand for deep learning is headed through the roof.

毫无疑问,机器学习是一个快速发展的领域,在其中,深度学习也在以惊人的速度增长。 具体的市场预期有所不同,从公司到坚定 ,以坚定的 ,但大家都同意的总趋势:深学习需求是通过屋顶领导。

节省时间 (It saves time)

If you’ve messed with other forms of machine learning, you know that feature engineering – converting your input’s parameters into “features” your algorithm can read – can be a fairly difficult and time-intensive process. But the neural networks used in deep learning are designed to do that conversion automatically. So, for example, instead of having to figure out how to pull color data, histograms, and other metrics from a set of images, you can just run the raw images through your neural network and it will do the work for you!

如果您对其他形式的机器学习感到困惑,您就会知道功能工程(将输入的参数转换为算法可以读取的“功能”)可能是一个相当困难且耗时的过程。 但是深度学习中使用的神经网络旨在自动进行转换。 因此,例如,不必费解如何从一组图像中提取颜色数据,直方图和其他指标,您只需通过神经网络运行原始图像,它将为您完成工作!

That’s making it sound easy, of course, and it isn’t; the challenge is getting the network to the point where it’s capable of doing that work for you. But that means you’ll spend more time working with your algorithms and less time fiddling with features.

当然,这听起来很容易,但事实并非如此。 挑战在于使网络达到能够为您完成这项工作的地步。 但这意味着您将花费更多的时间来处理算法,而花更少的时间去摆弄功能。

专注于保持灵活性 (Specialize while staying flexible)

Building a specialty can help you find work in any field, but it can also put you into a position where you’re doomed to be doing the same thing every day because your speciality is only appealing to a limited number of companies who are all doing the same sort of thing. Thankfully, that’s not the case with deep learning, which is in demand across a wide swath of industries and is being put to use to solve problems ranging from image recognition to translation to robotics.

建立专业可以帮助您在任何领域找到工作,但是也可以使您注定要每天都做同样的事情,因为您的专业只会吸引数量有限的公司同样的事情。 值得庆幸的是,深度学习并非如此,深度学习在众多行业中都需要,并且被用于解决从图像识别到翻译到机器人技术的问题。

好有趣! (It’s fun!)

Career advantages aside, let’s not forget that deep learning is just plain cool. You can use it to get machines to do everything, from automatically colorizing old photos to destroying the world’s greatest chess players without actually teaching them how to do those things.

除了职业优势,别忘了深度学习简直太酷了。 您可以使用它来使机器执行所有操作,从自动为旧照片着色到摧毁世界上最伟大的国际象棋棋手,而无需实际教他们如何做那些事情。

Ready to dive into the deep? The first mission of the new course is completely free so everybody can try it out, but you will need a Premium subscription to complete the course.

准备好深入了解了吗? 新课程的第一个任务是完全免费的,因此每个人都可以尝试,但是您需要高级订阅才能完成本课程。

翻译自: https://www.pybloggers.com/2018/12/an-intro-to-deep-learning-in-python/

python深度学习入门

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