Machine Learning and Data Mining: 03 Data Representation

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Course "Machine Learning and Data Mining" for the degree of Computer Engineering at the Politecnico di Milano. This lecture overviews the data representation issues in Data Mining.

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Machine Learning and Data Mining: 03 Data Representation

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If there is one crucial thing in building ML models, this would be the data preparation. That is the process of transforming raw data to a state where machine learning algorithms could be run to disclose insights and make predictions. Data preparation involves analysis, depends on the nature of the problem and the particular algorithms. As far as there are knowledge and experience involved, there is no such thing as automation, which makes the role of the data scientist the key to success. ML is trendy and Microsoft already have more than 10 services to support ML. So we will focus on tools like Azure ML Workbench and Python for data preparation, review some common tricks to approach data and experiment in Azure ML Studio.

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GARBAGE COLLECTOR Automatic garbage collection is the process of looking at heap memory, identifying which objects are in use and which are not, and deleting the unused objects. An in use object, or a referenced object, means that some part of your program still maintains a pointer to that object. An unused object, or unreferenced object, is no longer referenced by any part of your program. So the memory used by an unreferenced object can be reclaimed. In a programming language like C, allocating and deallocating memory is a manual process. In Java, process of deallocating memory is handled automatically by the garbage collector.

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Computer Vision 분야에서 CNN은 과연 살아남을 수 있을까요? 안녕하세요 TensorFlow Korea 논문 읽기 모임 PR-12의 317번째 논문 리뷰입니다. 이번에는 Google Research, Brain Team의 MLP-Mixer: An all-MLP Architecture for Vision을 리뷰해보았습니다. Attention의 공격도 버거운데 이번에는 MLP(Multi-Layer Perceptron)의 공격입니다. MLP만을 사용해서 Image Classification을 하는데 성능도 좋고 속도도 빠르고.... 구조를 간단히 소개해드리면 ViT(Vision Transformer)의 self-attention 부분을 MLP로 변경하였습니다. MLP block 2개를 사용하여 하나는 patch(token)들 간의 연산을 하는데 사용하고, 하나는 patch 내부 연산을 하는데 사용합니다. 사실 MLP를 사용하긴 했지만 논문에도 언급되어 있듯이, 이 부분을 일종의 convolution이라고 볼 수 있는데요... 그래도 transformer 기반의 network이 가질 수밖에 없는 quadratic complexity를 linear로 낮춰주고 convolution의 inductive bias 거의 없이 아주아주 simple한 구조를 활용하여 이렇게 좋은 성능을 보여준 점이 멋집니다. 반면에 역시나 data를 많이 써야 한다거나, MLP의 한계인 fixed length의 input만 받을 수 있다는 점은 단점이라고 생각하는데요, 이 연구를 시작으로 MLP도 다시한번 조명받는 계기가 되면 좋을 것 같네요 비슷한 시점에 나온 비슷한 연구들도 마지막에 간략하게 소개하였습니다. 재미있게 봐주세요. 감사합니다! 논문링크: https://arxiv.org/abs/2105.01601 영상링크: https://youtu.be/KQmZlxdnnuY

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Delta Analytics is a 501(c)3 non-profit in the Bay Area. We believe that data is powerful, and that anybody should be able to harness it for change. Our teaching fellows partner with schools and organizations worldwide to work with students excited about the power of data to do good. Welcome to the course! These modules will teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community. Each module focuses on accessible examples designed to teach you about good practices and the powerful (yet surprisingly simple) algorithms we use to model data. To learn more about our mission or provide feedback, take a look at www.deltanalytics.org.

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