A beginner’s introduction to Deep Learning I am Samvita from the Business Team of HyperVerge. I joined the team a few months back to help out on User Growth, PR and Marketing. From when I first heard about HyperVerge, I had one question – What is thi…
李宏毅老师的机器学习课程和吴恩达老师的机器学习课程都是都是ML和DL非常好的入门资料,在YouTube.网易云课堂.B站都能观看到相应的课程视频,接下来这一系列的博客我都将记录老师上课的笔记以及自己对这些知识内容的理解与补充.(本笔记配合李宏毅老师的视频一起使用效果更佳!) Lecture 6: Brief Introduction of Deep Learning 本节课主要围绕Deep Learing三步骤: (1)function set (2)goodness of function (…
Coursera课程<Neural Networks and Deep Learning> deeplearning.ai Week1 Introduction to deep learning What is a Neural Network? 让我们从一个房价预测的例子开始讲起. 假设你有一个数据集,它包含了六栋房子的信息.所以,你知道房屋的面积是多少平方英尺或者平方米,并且知道房屋价格.这时,你想要拟合一个根据房屋面积预测房价的函数. 如果使用线性回归进行拟合,那么可以拟合出一条直线.但…
第一周:深度学习引言(Introduction to Deep Learning) 欢迎(Welcome) 深度学习改变了传统互联网业务,例如如网络搜索和广告.但是深度学习同时也使得许多新产品和企业以很多方式帮助人们,从获得更好的健康关注. 深度学习做的非常好的一个方面就是读取 X 光图像,到生活中的个性化教育,到精准化农业,甚至到驾驶汽车以及其它一些方面.如果你想要学习深度学习的这些工具,并应用它们来做这些令人窒息的操作,本课程将帮助你做到这一点.当你完成 cousera 上面的这一系列专项课…
Introduction to Deep Learning Algorithms See the following article for a recent survey of deep learning: Yoshua Bengio, Learning Deep Architectures for AI, Foundations and Trends in Machine Learning, 2(1), 2009 Depth The computations involved in prod…
整个deep learing 系列课程主要包括哪些内容 Intro to Deep learning…
1. Understand the major trends driving the rise of deep learning.2. Be able to explain how deep learning is applied to supervised learning.3. Understand what are the major categories of models (such as CNNs and RNNs), and when they should be applied.…
1.What does the analogy “AI is the new electricity” refer to?  (B) A. Through the “smart grid”, AI is delivering a new wave of electricity. B. Similar to electricity starting about 100 years ago, AI is transforming multiple industries. C. AI is power…
Frequently Asked Questions Congratulations to be part of the first class of the Deep Learning Specialization! This form is here to help you find the answers to the commonly asked questions. We will update it as we receive new questions that we think…
- 通常机器学习,目的是,找到一个函数,针对任何输入:语音,图片,文字,都能够自动输出正确的结果. - 而我们可以弄一个函数集合,这个集合针对同一个猫的图片的输入,可能有多种输出,比如猫,狗,猴子等,而我们通过提供大量的training data给这个函数集合,对集合里的各种函数组合的输出进行比对,最后选出一个能输出最佳结果(结果是猫)的组合,那么因为这个组合已经很能够很准确的识别猫,所以这个组合就能用来检测图片里是否是猫. - 具体来说,下面第一张图,某一个点为一个函数,而整个网络机构为函数集…