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深度学习课程
Deep Learning Course

原始链接: https://fleuret.org/dlc/

要获取 DLCL 库，请按照下列步骤操作： 1.克隆GitHub存储库：https://github.com/DLCL/pytorch-deeplearningcourse.git 2. 使用“cd”命令更改为克隆的文件夹，后跟文件夹名称。 例如：cd pytorch-deeplearningcourse 3. 安装上述存储库中的requirements.txt 文件中指定的依赖项。 您可能需要 apt-get 命令的管理权限； 如有必要，请在命令前加上 sudo 前缀。 示例： pip install -rrequirements.txt 或 pip3.install -rrequirements.txt 。 如有疑问，请参阅以下文档。 为了确保获得足够的学习成果，对入门级数学有深入的了解将是有益的，包括基本的线性代数概念，如向量、矩阵以及离散和连续分布的空间（概率和统计）。 熟悉常见的编程语言（特别是 Python）也很有用，因为本课程中的说明使用了大量用 Python 语法编写的示例。 有关数值计算成本分析（通常称为算法复杂性）的知识，以及通过傅里叶变换和小波技术进行信号处理的基础知识，可以增强 DLCL 讲座期间的理解和记忆能力。 此外，关于每节课教学场景中使用的预定义参数的先验知识可以提高对讲座材料的认知回忆。

在线有各种资源可用于学习机器学习和深度学习。 然而，值得注意的是，虽然这些资源提供了宝贵的见解和知识，但获得专业知识需要承诺并坚持应用所教授的原则。 一种选择是前面提到的斯坦福课程，涵盖机器学习和深度学习。 另一个值得注意的资源是《深度学习》，这是由 François Chollet 和 Ian Goodfellow 撰写的一本书，他们也帮助开发了 Google Brain。 Andrew Ng 和 Justin Zhu 的斯坦福讲座提供了另一种可以通过视频格式轻松访问的选项。 此外，Sébastien Raschka 还提供了名为“Python 机器学习”的实用指南。 虽然每门课程都提供了独特的方法，但都提供了对基本概念和实际实施技术的全面理解。 最终，选择首选方法取决于个人喜好和需求。 然而，与任何技能一样，掌握来自于持续的努力和练习。
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原文

You can find here slides, recordings, and a virtual machine for François Fleuret's deep-learning courses 14x050 of the University of Geneva, Switzerland.

This course is a thorough introduction to deep-learning, with examples in the PyTorch framework:

  • machine learning objectives and main challenges,
  • tensor operations,
  • automatic differentiation, gradient descent,
  • deep-learning specific techniques,
  • generative, recurrent, attention models.

You can check the pre-requisites.

This course was developped initialy at the Idiap Research Institute in 2018, and taught as EE-559 at École Polytechnique Fédérale de Lausanne until 2022. The notes for the handouts were added with the help of Olivier Canévet.

Thanks to Adam Paszke, Jean-Baptiste Cordonnier, Alexandre Nanchen, Xavier Glorot, Andreas Steiner, Matus Telgarsky, Diederik Kingma, Nikolaos Pappas, Soumith Chintala, and Shaojie Bai for their answers or comments.

In addition to the materials available here, I also wrote and distribute "The Little Book of Deep Learning", a phone-formatted short introduction to deep learning for readers with a STEM background.

The slide pdfs are the ones I use for the lectures. They are in landscape format with overlays to facilitate the presentation. The handout pdfs are compiled without these fancy effects in portrait orientation, with additional notes. The screencasts are available both as in-browser streaming or downloadable mp4 files.

You can get archives with all the pdf files (1097 slides):

and subtitles for the screencasts generated automaticallly with OpenAI's Whisper:

or the individual lectures:

Pre-requisites

  • Linear algebra (vectors, matrices, Euclidean spaces),
  • differential calculus (Jacobian, Hessian, chain rule),
  • Python programming,
  • basics in probabilities and statistics (discrete and continuous distributions, law of large numbers, conditional probabilities, Bayes, PCA),
  • basics in optimization (notion of minima, gradient descent),
  • basics in algorithmic (computational costs),
  • basics in signal processing (Fourier transform, wavelets).

Documentation

You may have to look at the Python, Jupyter notebook, and PyTorch documentations at

Practical session prologue

Helper Python prologue for the practical sessions: dlc_practical_prologue.py

Argument parsing

This prologue parses command-line arguments as follows

usage: dummy.py [-h] [--full] [--tiny] [--seed SEED]
[--cifar] [--data_dir DATA_DIR]

DLC prologue file for practical sessions.

optional arguments:
-h, --help           show this help message and exit
--full               Use the full set, can take ages (default
False)
--tiny               Use a very small set for quick checks
(default False)
--seed SEED          Random seed (default 0, 


Loading data



The prologue provides the function



load_data(cifar = None, one_hot_labels = False, normalize = False, flatten = True)




which downloads the data when required, reshapes the images to 1d
vectors if flatten
is True, and narrows to a small subset of
samples if --full is not selected.



It returns a tuple of four tensors: train_data,
train_target, test_data, and test_target.



If cifar is True, the data-base used is CIFAR10, if it
is False, MNIST is used, if it is None, the argument
--cifar is taken into account.



If one_hot_labels is True, the targets are converted to 2d
torch.Tensor with as many columns as there are classes, and
-1 everywhere except the coefficients [n, y_n], equal to 1.



If normalize is True, the data tensors are normalized
according to the mean and variance of the training one.



If flatten is True, the data tensors are flattened
into 2d tensors of dimension N × D, discarding the image structure
of the samples. Otherwise they are 4d tensors of dimension N × C
× H × W.



Minimal example



import dlc_practical_prologue as prologue

train_input, train_target, test_input, test_target = prologue.load_data()

print('train_input', train_input.size(), 'train_target', train_target.size())
print('test_input', test_input.size(), 'test_target', test_target.size())




prints



* Using MNIST
** Reduce the data-set (use --full for the full thing)
** Use 1000 train and 1000 test samples
train_input torch.Size([1000, 784]) train_target torch.Size([1000])
test_input torch.Size([1000, 784]) test_target torch.Size([1000])










A Virtual Machine (VM) is a software that simulates a complete
computer. The one we provide here includes a Linux operating
system and all the tools needed to use PyTorch from a web
browser
(e.g. Mozilla
Firefox or Google
Chrome).



Installation



    

  1. Download and install Oracle's VirtualBox,
    2. 

  2. download the virtual machine OVA package (1.68Gb), and
    3. 

  3. open the latter in VirtualBox with File → Import Appliance.
    4. 




You should now see an entry in the list of VMs. The first time
it starts, it provides a menu to choose the keyboard layout you
want to use (you can force the configuration later by running
the command sudo set-kbd).



If the VM does not start and VirtualBox complains that the
VT-x is not enabled, you have to activate the virtualization
capabilities of your CPU in the BIOS of your computer.



Using the VM



The VM automatically starts
a JupyterLab on port 8888 and
exports that port to the host. This means that you can access this
JupyterLab with a web browser on the machine running VirtualBox at
http://localhost:8888/
and use Python notebooks, view files, start terminals, and edit source
files. Typing !bye in a notebook
or bye in a terminal will shutdown the
VM.



You can run a terminal and a text editor from inside the Jupyter
notebook for exercises that require more than the notebook
itself. Source files can be executed by running in a terminal the
Python command with the source file name as argument. Both can be done
from the main Jupyter window with:
  • New → Text File to create the source code, or selecting the file and clicking Edit to edit an existing one.
  • New → Terminal to start a shell from which you can run Python.

This VM also exports an ssh port to the port 2022 on the host, which allows to log in with standard ssh clients on Linux and OSX, and with applications such as PuTTY on Windows. The default login is 'dave' and password 'dummy', same password for the root account.

Remarks

Note that performance for computation will be very poor compared to installing PyTorch natively on your machine. In particular, the VM does not take advantage of a GPU if you have one.

Finally, please also note that this VM is configured in a convenient but highly non-secured manner, with easy to guess passwords, including for the root, and network-accessible non-protected Jupyter notebooks.

This VM is built on a Linux Debian, with miniconda, PyTorch, MNIST, CIFAR10, and many Python utility packages installed.

My own materials on this page are licensed under the Creative Commons BY-NC-SA 4.0 International License.

More simply: I am okay with this material being used for regular academic teaching, but definitely not for a book / youtube loaded with ads / whatever monetization model I am not aware of.

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