learning models today, how to think about it in terms of metrics that you care about, and finally the tools at your disposal to achieve what you want. The subsequent chapters will delve deeper into techniques pareto-frontier. Our goal with efficient deep learning is to have a collection of algorithms, techniques, tools, and infrastructure that work together to allow users to train and deploy pareto-optimal models that is illustrated in Figure 1-6. As mentioned earlier, with this book we’ll strive to build a set of tools and techniques that can help us make models pareto-optimal and let the user pick the right tradeoff

instruction" }, { "from": "gpt", "value": "model response" } ], "system": "system prompt (optional)", "tools": "tool description (optional)" } ] 2. 在 data/dataset_info.json 文件中提供您的数据集定义，并采用以下格式： 1.12. 有监督微调 json", "formatting": "sharegpt", "columns": { "messages": "conversations", "system": "system", "tools": "tools" }, "tags": { "role_tag": "from", "content_tag": "value", "user_tag": "user", "assistant_tag": import os import json5 import urllib.parse from qwen_agent.agents import Assistant from qwen_agent.tools.base import BaseTool, register_tool llm_cfg = { # Use the model service provided by DashScope: 'model':

Tensors • Autograd • Modular structure • Models / Layers • Datasets • Dataloader • Visualization Tools like • TensorboardX (monitor training) • PyTorchViz (visualise computation graph) • Various other

���� The vectorized version is useful and efficient when you’re working with numerical computing tools like Matlab/Octave. If you are familiar with matrices, you can prove to yourself that the two forms

Enhancement Input “Auto Enhance” on iPhone “Auto Tone” in Lightroom Ours Existing Photo Editing Tools Retinex-based Methods • LIME: [TIP 17] • WVM: [CVPR 16] • JieP: [ICCV 17] Learning-based Methods

Learning models: (a) lower model size, and (b) lower inference latency. We already have the necessary tools for achieving (a), the lower model size. Let us see how we can apply what we learnt for quantizing

era). Techniques like Principal Components Analysis, Low-Rank Matrix Factorization, etc. are popular tools for dimensionality reduction. We will explain these techniques in further detail in chapter 6. A

Notebook中编辑它。进行更改并检查它们 是否正常。假设我们已经修改了文件~/d2l-en/chapter_appendix_tools/how-to-contribute.md中的一个拼 写错误。你可以检查你更改了哪些文件。 此时，Git将提示chapter_appendix_tools/how-to-contribute.md文件已被修改。 mylaptop:d2l-en me$ git status changes in working directory) modified: chapter_appendix_tools/how-to-contribute.md 16.5. 为本书做贡献 765 在确认这是你想要的之后，执行以下命令： git add chapter_appendix_tools/how-to-contribute.md git commit -m 'fix typo in git