2021

  • Introduction to Deep Learning -- 170 Video Lectures from Adaptive Linear Neurons to Zero-shot Classification with Transformers
    I just sat down this morning and organized all deep learning related videos I recorded in 2021. I am sure this will be a useful reference for my future self, but I am also hoping it might be useful for one or the other person out there. PS: All code examples are in PyTorch :)
  • Datasets for Machine Learning and Deep Learning -- Some of the Best Places to Explore
    随着学期的如火如荼,我最近与我的深度学习课程分享了这套数据集存储库。但是,我认为除了使用此列表来寻找有趣的学生课程项目的灵感外,这些也是为您的模型寻找其他Bechmark数据集的好地方。
  • 书评:深度学习与PyTorch——Practical Deep Learning Guide With a Computer Vision Focus and an Interesting Structure
    在2020年8月发行后,与Pytorch进行深度学习一直坐在我的架子上,然后我终于有机会在这个寒假期间阅读它。事实证明,这是放松假期后的一点点生产力的完美随和的阅读材料。正如上周承诺的那样,这是我的想法。
  • How I Keep My Projects Organized
    Since I started my undergraduate studies in 2008, I have been obsessed with productivity tips, notetaking solutions, and todo-list management. Over the years, I tried many, many workflows and hundreds of (mostly digital) tools to keep my life, projects, and notes organized. Occasionally, I exchange ideas with friends and colleagues, and upon request, I talked about my workflow a couple of times on Twitter. After today's 2021-edition of this discussion, I thought that writing a quick and informal blogpost makes sense, making it easier to read and having a quick reference if someone asks about it again :).

2020

2019

  • What's New in the 3rd Edition
    A brief summary of what's new in the 3rd edition of Python Machine Learning.
  • 我在威斯康星大学麦迪逊分校的第一年和一个很棒的学生项目画廊
    不久前,在2018年夏天,我很高兴能在获得我的博士学位后加入威斯康星大学麦迪逊分校的统计系。经过〜5个长期且富有成效的岁月。现在,在决赛周后的两个学期之后,我终于找到了一些安静的日子,回顾自那时以来发生的事情。在这篇文章中,我分享了简短的反思以及我的学生正在从事的一些激动人心的项目。

2018

  • Model evaluation, model selection, and algorithm selection in machine learning Part IV - Comparing the performance of machine learning models and algorithms using statistical tests and nested cross-validation
    This final article in the series *Model evaluation, model selection, and algorithm selection in machine learning* presents overviews of several statistical hypothesis testing approaches, with applications to machine learning model and algorithm comparisons. This includes statistical tests based on target predictions for independent test sets (the downsides of using a single test set for model comparisons was discussed in previous articles) as well as methods for algorithm comparisons by fitting and evaluating models via cross-validation. Lastly, this article will introduce *nested cross-validation*, which has become a common and recommended a method of choice for algorithm comparisons for small to moderately-sized datasets.
  • 通过半逆转神经网络生成性别中立的面部图像以增强隐私
    我认为,对于最近的项目,与更普遍的受众(包括同事和学生)分享他们的简短简明摘要是很好的。因此,我挑战自己要使用少于1000个单词,而不会因细节细节和技术术语而分心。在这篇文章中,我主要介绍了我最近与[Iprobe Lab](http://iprobe.cse.msu.edu)合作的一些研究,该研究属于开发方法的广泛类别,以隐藏特定信息在面部图像中。这篇文章中讨论的研究是关于“最大化隐私的同时保存公用事业”。

2016

  • Model evaluation, model selection, and algorithm selection in machine learning Part III - Cross-validation and hyperparameter tuning
    Almost every machine learning algorithm comes with a large number of settings that we, the machine learning researchers and practitioners, need to specify. These tuning knobs, the so-called hyperparameters, help us control the behavior of machine learning algorithms when optimizing for performance, finding the right balance between bias and variance. Hyperparameter tuning for performance optimization is an art in itself, and there are no hard-and-fast rules that guarantee best performance on a given dataset. In Part I and Part II, we saw different holdout and bootstrap techniques for estimating the generalization performance of a model. We learned about the bias-variance trade-off, and we computed the uncertainty of our estimates. In this third part, we will focus on different methods of cross-validation for model evaluation and model selection. We will use these cross-validation techniques to rank models from several hyperparameter configurations and estimate how well they generalize to independent datasets.
  • Model evaluation, model selection, and algorithm selection in machine learning Part II - Bootstrapping and uncertainties
    In this second part of this series, we will look at some advanced techniques for model evaluation and techniques to estimate the uncertainty of our estimated model performance as well as its variance and stability. Then, in the next article, we will shift the focus onto another task that is one of the main pillar of successful, real-world machine learning applications -- Model Selection.
  • Model evaluation, model selection, and algorithm selection in machine learning Part I - The basics
    机器学习已成为我们生活的核心部分 - 作为消费者,客户以及希望作为研究人员和从业者!无论我们是将预测建模技术应用于我们的研究还是业务问题,我相信我们有一个共同点:我们想做出良好的预测!将模型拟合到我们的培训数据是一回事,但是我们如何知道它可以很好地概括地看不见的数据?我们如何知道它不简单地记住我们喂养的数据,也没有对未来样本,以前从未见过的样本做出良好的预测?首先,我们如何选择一个好的模型?也许另一种学习算法可以更好地解决手头的问题?模型评估当然不仅是我们机器学习管道的终点。

    Before we handle any data, we want to plan ahead and use techniques that are suited for our purposes. In this article, we will go over a selection of these techniques, and we will see how they fit into the bigger picture, a typical machine learning workflow.

2015

  • Writing 'Python Machine Learning' – A Reflection on a Journey
    It's been about time. I am happy to announce that "Python Machine Learning" was finally released today! Sure, I could just send an email around to all the people who were interested in this book. On the other hand, I could put down those 140 characters on Twitter (minus what it takes to insert a hyperlink) and be done with it. Even so, writing "Python Machine Learning" really was quite a journey for a few months, and I would like to sit down in my favorite coffeehouse once more to say a few words about this experience.
  • Python,机器学习和语言战争 - 高度主观的观点
    最近对我来说,这确实是一段旅程。关于“您为什么选择Python进行机器学习?”的问题问题。我想是时候写我的脚本了。在本文中,我真的不是要告诉您为什么您或其他任何人都应该使用Python。但是请阅读您是否对我的意见感兴趣。
  • Single-Layer Neural Networks and Gradient Descent
    This article offers a brief glimpse of the history and basic concepts of machine learning. We will take a look at the first algorithmically described neural network and the gradient descent algorithm in context of adaptive linear neurons, which will not only introduce the principles of machine learning but also serve as the basis for modern multilayer neural networks in future articles.
  • Principal Component Analysis in 3 Simple Steps
    主成分分析(PCA)是一种简单而流行且有用的线性转换技术,用于许多应用程序,例如股票市场预测,基因表达数据的分析等等。在本教程中,我们将看到PCA不仅仅是一个“黑匣子”,我们将以3个基本步骤来解开其内部内容。
  • Implementing a Weighted Majority Rule Ensemble Classifier in scikit-learn
    Here, I want to present a simple and conservative approach of implementing a weighted majority rule ensemble classifier in scikit-learn that yielded remarkably good results when I tried it in a kaggle competition. For me personally, kaggle competitions are just a nice way to try out and compare different approaches and ideas -- basically an opportunity to learn in a controlled environment with nice datasets.

2014