How to Eat PyTorch in 20 Days? π₯π₯
Introduction for the Target Audience πΌ
"Eat PyTorch in 20 Days" is crafted for readers who have a foundational knowledge of machine learning and deep learning. It's particularly suited for those who have utilized libraries like Keras, TensorFlow, or PyTorch to train basic models. The project also offers a video version available on BiliBili, aimed at delivering engaging content with a touch of authentic, local flavor.
Writing Style π
This book serves as a highly user-friendly introductory tool specifically for PyTorch. It aspires to a "Don't let me think" philosophy, striving for simplicity and clarity. While based on the official PyTorch documentation, it goes a step further by optimizing chapter structures and examples, thereby enhancing user accessibility. The content is structured to progressively increase in complexity, designed according to the readers' search habits and the intrinsic hierarchy of PyTorch. The purpose is to make it easy to find relevant examples by function. Examples are kept as simple and structured as possible to enhance readability and applicability, making most of the code snippets readily usable in practical scenarios.
If mastering PyTorch through its official documentation has a difficulty level of 5, this book aims to bring it down to roughly a 2.
Learning Plan β°
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Learning Schedule: Authored in about three months during the author's spare time, most readers can learn all contents within 20 days by dedicating between 30 minutes to two hours each day. Besides serving as a study guide, the book can also function as a practical manual or example repository during project implementations.
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Content Plan: The project schedule is outlined clearly, allowing learners to click on a chapter title to directly access it. Daily learning content is structured by difficulty and expected completion time.
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Learning Environment: All code examples have been verified in a Jupyter notebook environment. It is recommended readers clone the source code via Git for interactive learning experiences. Instructions for acquiring necessary datasets are also provided.
Project Updates
Regular updates to the project include new chapters such as:
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PyTorch and Advertising Recommendations: Tailored for readers interested in advanced advertising recommendation algorithms.
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Extras - PyTorch Related Tools: Introduces supplementary tools to enhance the capabilities of PyTorch, addressing topics such as using Kaggle GPUs, Gradio, and Optuna.
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Torchkeras Tool Library: Updates have integrated torchkeras, featuring utilities like training progress bars, evaluation metrics, and more, supported from respective stable versions.
Encouragement and Contact ππ
Readers are encouraged to inspire the author by starring the project on its repository and sharing the resource with others. For queries or suggestions, joining the discussion group through the "Algorithm Delicacies" public account is recommended, where readers can interact and exchange insights with peers.
A friendly logo and contact points are shared to further enhance reader engagement and connections.