Data Science: Deep Learning and Neural Networks in Python

本课程将帮助您开始使用深度构建您的第一个人工神经网络学习技巧。继我之前关于逻辑回归的课程之后，我们采用了这个基本构建块，并使用 Python 和 Numpy 一开始就构建了完整的非线性神经网络。本课程的所有材料都是免费的。

我们使用 softmax 函数将之前的二元分类模型扩展到多个类别，并使用第一原理推导出非常重要的训练方法，称为“反向传播”。我将向您展示如何在 Numpy 中编写反向传播代码，首先是“慢速方式”，然后是使用 Numpy 功能的“快速方式”。

接下来，我们使用 Google 的新 TensorFlow 库实现一个神经网络。

如果您有兴趣开始成为深度学习大师的旅程，或者您对机器学习和数据科学感兴趣，那么您应该学习本课程。我们超越了逻辑回归和线性回归等基本模型，我向您展示了一些可以自动学习特征的东西。

本课程为您提供了许多实际示例，以便您真正了解深度学习如何应用于任何事物。在整个课程中，我们将进行一个课程项目，该项目将向您展示如何在给定用户数据的情况下预测用户在网站上的行为，例如该用户是否在移动设备上、他们查看的产品数量、他们停留的时间在您的网站上，他们是否是回访者，以及他们访问的时间。

课程结束时的另一个项目向您展示了如何使用深度学习进行面部表情识别。想象一下，仅凭一张图片就能预测某人的情绪！

在了解了基本原理之后，我将简要概述神经网络的一些最新发展——稍微修改的架构及其用途。

笔记：

如果您已经了解 softmax 和反向传播，并且想跳过理论并使用更高级的技术和 GPU 优化加快速度，请查看我关于此主题的后续课程数据科学：实用深度学习概念Theano 和 TensorFlow。

我还有其他课程涵盖更高级的主题，例如卷积神经网络、受限玻尔兹曼机、自动编码器等等！但是在继续学习更高级的科目之前，您希望对本课程中的材料感到非常满意。

本课程侧重于“如何构建和理解”，而不仅仅是“如何使用”。阅读一些文档后，任​​何人都可以在 15 分钟内学会使用 API。这不是关于“记住事实”，而是关于通过实验“亲眼看到”。它将教您如何可视化模型内部发生的事情。如果您想要的不仅仅是对机器学习模型的肤浅了解，那么本课程适合您。

MP4 | Video: h264, 1280×720 | Audio: AAC, 44100 Hz
Language: English | Size: 2.03 GB | Duration: 11h 9m

What you’ll learn
Learn how Deep Learning REALLY works (not just some diagrams and magical black box code)
Learn how a neural network is built from basic building blocks (the neuron)
Code a neural network from scratch in Python and numpy
Code a neural network using Google’s TensorFlow
Describe different types of neural networks and the different types of problems they are used for
Derive the backpropagation rule from first principles
Create a neural network with an output that has K > 2 classes using softmax
Describe the various terms related to neural networks, such as “activation”, “backpropagation” and “feedforward”
Install TensorFlow

Requirements
Basic math (calculus derivatives, matrix arithmetic, probability)
Install Numpy and Python
Don’t worry about installing TensorFlow, we will do that in the lectures.
Being familiar with the content of my logistic regression course (cross-entropy cost, gradient descent, neurons, XOR, donut) will give you the proper context for this course
Description
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 scratch

Other 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 arithmetic

probability

Python coding: if/else, loops, lists, dicts, sets

Numpy coding: matrix and vector operations, loading a CSV file

Be familiar with basic linear models such as linear regression and logistic regression

WHAT 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)

Who this course is for:
Students interested in machine learning – you’ll get all the tidbits you need to do well in a neural networks course
Professionals who want to use neural networks in their machine learning and data science pipeline. Be able to apply more powerful models, and know its drawbacks.