D2L Courses

09/2021 - 12/2021

Practical Machine Learning

CS329P, Stanford

Alex Smola, Qingqing Huang, Mu Li

Applying Machine Learning (ML) to solve real problems accurately and robustly requires more than just training the latest ML model. First, you will learn practical techniques to deal with data. This matters since real data is often not independently and identically distributed. It includes detecting covariate, concept, and label shifts, and modeling dependent random variables such as the ones in time series and graphs. Next, you will learn how to efficiently train ML models, such as tuning hyper-parameters, model combination, and transfer learning. Last, you will learn about fairness and model explainability, and how to efficiently deploy models. This is a full semester class that teaches both statistics, algorithms and code implementations. Homeworks and the final project emphasize solving real problems.

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03/2021 - 08/2021

Introduction to Deep Learning in Chinese

Virtual

Mu Li

不论是在学术突破还是在工业应用, 深度学习是人工智能在近十年里进展最为迅速的领域。然而,深度学习模型复杂、参数繁多、而且新模型层出不穷,这给学习带来了难度。

本课程将从零开始教授深度学习。同学们只需要有基础的Python编程和数学基础。我们将覆盖四大类模型:多层感知机、卷积神经网络、循环神经网络、和注意力机制。在此之上,我们将介绍深度学习中的两大应用领域—计算机视觉和自然语言处理—中的典型任务。

本课程的一大特点是不仅讲述模型算法,同时会将每一处细节都讲述如何用PyTorch进行实现。这样同学们可以在真实数据上获得第一手经验。我们将举办四次课程竞赛,让同学们实践学习到的知识如何解决实际问题。

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10/2020

Dive into Deep Learning

GPU Technology Conference (GTC)

Rachel Hu, Aston Zhang

This is a half day tutorial we gave at GTC, covering an introduction of the D2L project, fundamental of convolutional neural networks, and an brief introduction to natural .language processing.

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10/2019

Dive into Deep Learning in 1 Day

Open Data Science Conference

Alex Smola

Did you ever want to find out about deep learning but didn’t have time to spend months? New to machine learning? Do you want to build image classifiers, NLP apps, train on many GPUs or even on many machines? If you’re an engineer or data scientist, this course is for you. This is about the equivalent of a Coursera course, all packed into one day.

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01/2019 - 05/2019

Introduction to Deep Learning

STAT 157, UC Berkeley

Alex Smola, Mu Li

This is a full semester course providing a practical introduction to deep learning, including theoretical motivations and how to implement it in practice. As part of the course we will cover multilayer perceptrons, backpropagation, automatic differentiation, and stochastic gradient descent. Moreover, we introduce convolutional networks for image processing, starting from the simple LeNet to more recent architectures such as ResNet for highly accurate models. Secondly, we discuss sequence models and recurrent networks, such as LSTMs, GRU, and the attention mechanism. Throughout the course we emphasize efficient implementation, optimization and scalability, e.g. to multiple GPUs and to multiple machines. The goal of the course is to provide both a good understanding and good ability to build modern nonparametric estimators. The entire course is based on Jupyter notebooks to allow you to gain experience quickly.

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