深度学习的基础:核心概念和PyTorch

对深度学习有一个直观的认识

你会学到什么
培养对深度学习的直观理解
对深度学习背后的核心数学概念的直观理解
深层神经网络如何在引擎盖下工作的详细视图
计算图(PyTorch和Tensorflow之类的库就是建立在计算图之上的)
使用PyTorch和PyTorch Lightening从头开始构建神经网络
你将准备好探索人工智能的前沿和更先进的神经网络，如CNN，RNNs和Transformers
你将能够理解深度学习专家在文章和采访中谈论的内容
您将能够使用PyTorch开始试验您自己的人工智能项目

MP4 |视频:h264，1280×720 |音频:AAC，44.1 KHz，2声道
语言：英语+中英文字幕（云桥CG资源站 机译） |时长:55节课(9小时39分钟)|大小解压后:2.43 GB


要求
基本的Python编程知识
高中数学
学习深度学习的强烈愿望

描述
你对人工智能(AI)、机器学习和人工神经网络感兴趣吗？

你是不是因为听起来太专业而害怕入门深度学习？

你是不是一直在看深度学习视频，但还是觉得自己没有“领会”到？

我自己也去过！我没有工程背景。我自己学会了编码。但人工智能似乎仍然完全遥不可及。

这门课程旨在为您省去几个月试图解读深度学习的挫折。学完这门课程后，你会觉得自己已经准备好应对人工智能中更高级、更前沿的话题。

在本课程中我们假设尽可能少的先验知识。不需要工程或计算机科学背景(除了基本的Python知识)。深度学习需要的数学你都不知道？没关系。我们将一起一步一步地完成它们。

我们将“重新发明”一个深度神经网络，这样你就会对底层机制有一个深入的了解。这会让你对深度学习感觉更舒服，让你对学科有直观的感受。

我们还将在PyTorch和PyTorch Lightning中从头开始构建一个基本的神经网络，并训练一个用于手写数字识别的MNIST模型。Fundamentals of Deep Learning: Core Concepts and PyTorch


学完这门课程后

你最终会觉得自己对深度学习有了“直觉”的理解，并对进一步扩展自己的知识充满信心。

如果你回到你以前难以理解的热门课程(像吴恩达的课程或杰里米·霍华德的Fastai课程)，你会惊喜地发现你能理解更多。

你将能够理解杰弗里·辛顿(Geoffrey Hinton)等专家在文章中所说的话，或者安德烈·卡帕西(Andrej Karpathy)在特斯拉自主日所说的话。

你将具备良好的实践和理论知识，开始探索更先进的神经网络架构，如卷积神经网络(CNN)，递归神经网络(RNN)，变压器等。并开始您走向人工智能前沿、监督和非监督学习等的旅程。

你可以开始使用PyTorch和监督学习来试验你自己的AI项目

如果你是，本课程非常适合你

对深度学习感兴趣，但苦于核心概念

从非工程背景过渡到工程职业的人

熟悉基础知识，但希望探索更高级的知识。

已经在使用深度学习模型，但想要增强您的理解

Python开发人员，希望推进您的职业发展

这个9.5小时的课程将教会你所有的基本概念以及你所学知识的应用。

那么，是什么阻止了你深入深度学习的神奇世界呢？

这门课程是给谁的
希望第一次学习深度学习的学生
想要最终在直觉层面理解深度学习的初学者
寻求增强对深度学习基础知识的理解的专业人士

Get An Intuitive Understanding of Deep Learning

What you’ll learn
Develop an intuitive understanding of Deep Learning
Visual and intuitive understanding of core math concepts behind Deep Learning
Detailed view of how exactly deep neural networks work beneath the hood
Computational graphs (which libraries like PyTorch and Tensorflow are built on)
Build neural networks from scratch using PyTorch and PyTorch Lightening
You’ll be ready to explore the cutting edge of AI and more advanced neural networks like CNNs, RNNs and Transformers
You’ll be able to understand what deep learning experts are talking about in articles and interviews
You’ll be able to start experimenting with your own AI projects using PyTorch

Requirements
Basic Python programming knowledge
Highschool math
A strong desire to learn Deep Learning

Description
Are you interested in Artificial Intelligence (AI), Machine Learning and Artificial Neural Network?

Are you afraid of getting started with Deep Learning because it sounds too technical?

Have you been watching Deep Learning videos, but still don’t feel like you “get” it?

I’ve been there myself! I don’t have an engineering background. I learned to code on my own. But AI still seemed completely out of reach.

This course was built to save you many months of frustration trying to decipher Deep Learning. After taking this course, you’ll feel ready to tackle more advanced, cutting-edge topics in AI.

In this course

We assume as little prior knowledge as possible. No engineering or computer science background required (except for basic Python knowledge). You don’t know all the math needed for Deep Learning? That’s OK. We’ll go through them all together – step by step.

We’ll “reinvent” a deep neural network so you’ll have an intimate knowledge of the underlying mechanics. This will make you feel more comfortable with Deep Learning and give you an intuitive feel for the subject.

We’ll also build a basic neural network from scratch in PyTorch and PyTorch Lightning and train an MNIST model for handwritten digit recognition.

After taking this course

You’ll finally feel you have an “intuitive” understanding of Deep Learning and feel confident expanding your knowledge further.

If you go back to the popular courses you had trouble understanding before (like Andrew Ng’s courses or Jeremy Howards’ Fastai course), you’ll be pleasantly surprised at how much more you can understand.

You’ll be able to understand what experts like Geoffrey Hinton are saying in articles or Andrej Karpathy is saying during Tesla Autonomy Day.

You’ll be well equipped with both practical and theoretical understanding to start exploring more advanced neural network architectures like Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), transformers, etc. and start your journey towards the cutting edge of AI, Supervised and Unsupervised learning, and more.

You can start experimenting with your own AI projects using PyTorch and Supervised Learning

This course is perfect for you if you are

Interested in Deep Learning but struggling with the core concepts

Someone from a non-engineering background transitioning into an engineering career

Familiar with the basics but wish explore more advanced knowledge.

Already working with Deep Learning models, but want to supercharge your understanding

A Python Developer, looking to advance your career

This 9.5 hour course will teach you all the basic concepts as well as the application of your knowledge. You get 40 downloadable resources, full lifetime access, 30-Day Money-Back Guarantee and a Certificate of Completion.

So what stops you from taking a deep dive into the amazing world of Deep Learning?

Who this course is for
Students who want learn Deep Learning for the first time
Beginners who want to finally understand Deep Learning at an intuitive level
Professionals looking to supercharge their understanding of Deep Learning fundamentals

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