Pytorch 신간에서 다루고 있는 기술에 대해 공부하고자 한다. Packt홈페이지에서 무료로 동영상을 6일 동안 볼 수 있다. Mastering-Image-Segmentation-With-PyTorch-using-Real-World-Projects https://github.com/PacktPublishing/Mastering-Image-Segmentation-With-PyTorch-using-Real-World-Projects/ GitHub - PacktPublishing/Mastering-Image-Segmentation-With-PyTorch-using-Real-World-Projects Contribute to PacktPublishing/Mastering-Image-Segmentation-Wi..
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1. Ollama LLM 여러 모델 로컬에서 돌릴 수 있도록 만든 시스템 https://github.com/jmorganca/ollama GitHub - jmorganca/ollama: Get up and running with Llama 2 and other large language models locally Get up and running with Llama 2 and other large language models locally - GitHub - jmorganca/ollama: Get up and running with Llama 2 and other large language models locally github.com https://ollama.ai/ Ollama Get up and r..
Deep reinforcement learning - [강사] 지금까지의 과정에서는 가장 기본적인 형태의 강화학습을 살펴보고, 모든 것이 어떻게 작동하는지, 에이전트가 상태에서 학습하는 방식, 각 동작에 대한 동작 값을 Q-Table에서 어떻게 가르치는지를 이해했습니다. 또한 에이전트가 잘 수행하고 더 나은 조치를 취하려면 제대로 학습하기 위해 많은 조치를 시도해야 한다는 것도 알고 있습니다. 이제 에이전트가 Q-Table에서 추적해야 하는 이 많은 작업을 생각해 보십시오. Q-Table은 얼마나 클까요? 예, 아주, 아주 큽니다. 그리고 이것이 바로 딥 러닝이 등장하는 곳이며, 강화 학습을 보다 효과적으로 만들고 더 큰 정보 공간을 더 쉽게 처리할 수 있도록 합니다. 신경망은 에이전트가 환경에 대해 가..
1 Reinforcement Learning: An Introduction, Sutton and Barto, 2nd Edition. This is available for free here and references will refer to the final pdf version available here. http://incompleteideas.net/book/RLbook2018.pdf http://incompleteideas.net/book/the-book-2nd.html Some other additional references that may be useful are listed below: Reinforcement Learning: State-of-the-Art, Marco Wiering an..
시나리오 1: 당신은 고객에 대한 정보를 수집하여 표 형식의 데이터세트에 저장하는 프로젝트를 진행하고 있습니다. 프로젝트의 목표는 고객의 갱신 여부를 예측하는 모델을 만드는 것이며, 모델은 계정 담당자가 사용할 수 있도록 해석 가능성이 높아야 합니다. 고객의 갱신 여부를 예측하는 데 적합한 모델 유형은 무엇입니까? Logistic Regression, Neural Network, Decision Trees Since this is a binary classification problem, many machine learning techniques will work for the problem. However, of these three model families, there are really only ..
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Deciding when Deep Learning is the Right Tool Deep Learning is a powerful tool, but it's not the only one. In general, the way to choose whether or not to use Deep Learning depends on your task, what kind of data you have, and how complex the relationships in the data are. Scenarios in which to use Deep Learning include but are not limited to: Tasks Binary classification: Deep Learning Logistic ..
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Books to Read We believe that you learn best when you are exposed to multiple perspectives on the same idea. As such, we recommend checking out a few of the books below to get an added perspective on Deep Learning. Grokking Deep Learning by Andrew Trask. Use our exclusive discount code traskud17 for 40% off. This provides a very gentle introduction to Deep Learning and covers the intuition more ..
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Introduction So far in this course, we've learned about how neural networks can solve regression problems. Now we're going to apply neural networks to another common machine learning problem: classification. Most everything we've learned up until now still applies. The main difference is in the loss function we use and in what kind of outputs we want the final layer to produce. Binary Classifica..
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Introduction There's more to the world of deep learning than just dense layers. There are dozens of kinds of layers you might add to a model. (Try browsing through the Keras docs for a sample!) Some are like dense layers and define connections between neurons, and others can do preprocessing or transformations of other sorts. In this lesson, we'll learn about a two kinds of special layers, not c..
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