
posit::conf(2023) Workshop: Machine Learning and Deep Learning with Python
Register now: http://pos.it/conf Instructor: Sebastian Raschka Workshop Duration: 1-Day Workshop In this workshop, you will learn the machine and deep learning fundamentals using a modern open-source stack. We’ll start with a brief introduction to Python’s scientific computing libraries, including NumPy, Pandas, and Matplotlib, which provide the foundation for data analysis and visualization. From there, we will dive into the scikit-learn API, a user-friendly, open-source library for machine learning in Python. You will learn how to use it to create machine learning classifiers and apply tree-based models like random forests, gradient boosting, and XGBoost. In the second part of this workshop, we will also cover deep learning concepts and introduce PyTorch, the most widely used deep learning research library. You will also learn about training multi-layer neural networks efficiently using multi-GPU and mixed-precision techniques. Finally, we will explore how to use a pretrained large language transformer with scikit-learn and fine-tune it on a custom downstream task using PyTorch. By the end of this workshop, you will have a good understanding of the fundamental principles of machine learning and be able to construct advanced classification pipelines for tabular data using scikit-learn. Additionally, you will gain experience in image classification and natural language processing techniques using PyTorch and be able to implement them in your own predictive modeling projects effectively
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Transcript#
This transcript was generated automatically and may contain errors.
Hi, my name is Sebastian and here are the top three things that you will get out of this workshop. So first, I will teach you about machine learning with Scikit-Learn and then I will introduce PyTorch for deep learning. Both Scikit-Learn and PyTorch are the most widely used machine learning and deep learning libraries out there in both academia and industry. So you get exposure to the most popular open source libraries for both machine learning and deep learning.
Second, you will learn to use these tools in a professional manner. I have experience in both teaching machine learning and deep learning classes in a statistics department and teaching deep learning and machine learning in industry contexts. So I will combine these two strengths where we cover both the best practices for code implementations, but then also do this rigorously.
Third, I will also expose you to the cutting edge tools that are currently being used. For instance, you are probably all familiar with large language models. So in this course, we will also learn how we tune and how we use large language models ourselves, which will give you access to the most latest machine learning models out there.
