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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-science-deep-learning-in-python/
课程评论：没有评论
课程名称：数据科学：Python中的深度学习与神经网络 课程概述：您是否曾想过OpenAI的ChatGPT、GPT-4、DALL-E、Midjourney和Stable Diffusion等人工智能技术是如何运作的？这门课程将带您入门理解这些颠覆性应用的基础知识。您将学习如何使用深度学习技术构建您的第一个人工神经网络。 在这门课程中，我们将从逻辑回归的基础知识出发，直接使用Python和Numpy构建完整的非线性神经网络。课程将介绍如何扩展之前的二分类模型到多类模型，使用softmax函数，并从基本原理推导出重要的训练方法“反向传播”。我会向您展示如何用Numpy编写反向传播，先是“慢速度”，然后是利用Numpy特性“快速完成”。 接下来，我们将使用谷歌的新TensorFlow库实现一个神经网络。这门课程适合想要开始深度学习之旅的人，或是对机器学习和数据科学感兴趣的学习者。我们将超越逻辑回归和线性回归的基本模型，探索自动学习特征的方法。课程中提供了许多实用案例，帮助您了解深度学习在各种应用中的使用。 整个课程中，我们将进行一个课程项目，展示如何根据用户在网站上的行为预测用户行为，比如用户是否使用移动设备、查看的产品数量、停留时间、是否是回访用户以及访问时间等。课程结束时的另一个项目将展示如何利用深度学习进行面部表情识别——设想您可以仅凭一张照片预测一个人的情绪！ 在掌握基础知识后，我将简要介绍神经网络中一些最新的发展，如稍微修改过的架构及其应用。注意：如果您已经了解softmax和反向传播，并希望通过更高级的技术和GPU优化来加快学习进程，可以查看我后续的课程《数据科学：Theano和TensorFlow中的实用深度学习概念》。 我还有其他课程涵盖更高级的话题，如卷积神经网络、限制玻尔兹曼机、自编码器等！然而，在深入更高级的主题之前，您需要对这门课程的内容感到非常自信。这门课程着重于“如何构建和理解”，而不仅仅是“如何使用”。任何人都可以在15分钟内通过阅读文档学会使用API。这门课程关注的是通过实验来看清模型内部的运作过程。如果您希望对机器学习模型有更深入的理解，这门课程将非常适合您。 课程建议前提：微积分（求导）、矩阵运算、概率、Python编码（if/else、循环、列表、字典、集合）、Numpy编码（矩阵和向量操作，加载CSV文件）。需要熟悉基本线性模型，如线性回归和逻辑回归。 建议的课程学习顺序：请参见“机器学习和人工智能先修路线图”讲座（在我任何课程的FAQ中均可获得，包括免费的Numpy课程）。
Ever wondered how AI technologies like OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion really work? In this course, you will learn the foundations of these groundbreaking applications.This course will get you started in building your FIRST artificial neural network using deep learning techniques. Following my previous course on logistic regression, we take this basic building block, and build full-on non-linear neural networks right out of the gate using Python and Numpy. All the materials for this course are FREE.We extend the previous binary classification model to multiple classes using the softmax function, and we derive the very important training method called "backpropagation" using first principles. I show you how to code backpropagation in Numpy, first "the slow way", and then "the fast way" using Numpy features.Next, we implement a neural network using Google's new TensorFlow library.You should take this course if you are interested in starting your journey toward becoming a master at deep learning, or if you are interested in machine learning and data science in general. We go beyond basic models like logistic regression and linear regression and I show you something that automatically learns features.This course provides you with many practical examples so that you can really see how deep learning can be used on anything. Throughout the course, we'll do a course project, which will show you how to predict user actions on a website given user data like whether or not that user is on a mobile device, the number of products they viewed, how long they stayed on your site, whether or not they are a returning visitor, and what time of day they visited.Another project at the end of the course shows you how you can use deep learning for facial expression recognition. Imagine being able to predict someone's emotions just based on a picture!After getting your feet wet with the fundamentals, I provide a brief overview of some of the newest developments in neural networks - slightly modified architectures and what they are used for.NOTE:If you already know about softmax and backpropagation, and you want to skip over the theory and speed things up using more advanced techniques along with GPU-optimization, check out my follow-up course on this topic, Data Science: Practical Deep Learning Concepts in Theano and TensorFlow.I have other courses that cover more advanced topics, such as Convolutional Neural Networks, Restricted Boltzmann Machines, Autoencoders, and more! But you want to be very comfortable with the material in this course before moving on to more advanced subjects.This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you."If you can't implement it, you don't understand it"Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand".My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratchOther courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times...Suggested Prerequisites:calculus (taking derivatives)matrix arithmeticprobabilityPython coding: if/else, loops, lists, dicts, setsNumpy coding: matrix and vector operations, loading a CSV fileBe familiar with basic linear models such as linear regression and logistic regressionWHAT ORDER SHOULD I TAKE YOUR COURSES IN?:Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)
