. . . . . . . . . . . . . . . 59 5.2.1 Dense [source] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 5.2.2 Activation [source] . . . . . . . . . . . . . . . . . . . . . . . Dropout [source] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 5.2.4 Flatten [source] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 5.2.5 Input [source] . . . . . . . . . . . . . . . . 61 5.2.6 Reshape [source] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 5.2.7 Permute [source] . . . . . . . . . . . . . . . . . . . . . . . . .

functionality. PyTorch also includes standard defined neural network layers, deep learning optimizers, data loading utilities, and multi-gpu, and multi-node support. Functions are executed immediately instead nvcr.io/nvidia/ pytorch:-py3 Note: If you use multiprocessing for multi-threaded data loaders, the default shared memory segment size with which the container runs might not be enough To pull data and model descriptions from locations outside the container for use by PyTorch or save results to locations outside the container, mount one or more host directories as Docker® data volumes

there might not be a single algorithm that works perfectly, and there is a large amount of unseen data that the algorithm needs to process. Unlike traditional algorithm problems where we expect exact optimal certainty the exact content that you would end up clicking on, at that particular moment, with more data and sophisticated algorithms, these models can be trained to be fairly accurate over a longer term Availability of labelled data Even if one has enough compute, and sophisticated algorithms, solving classical machine learning problems relies on the presence of sufficient labeled data. With deep learning

recap, learning techniques can help us meet our model quality goals. Techniques like distillation and data augmentation improve the model quality, without increasing the footprint of the model (size, latency As we described in chapter 3’s ‘Learning Techniques and Efficiency’ section, labeling of training data is an expensive undertaking. Factoring in the costs of training human labelers on a given task, and significantly improve the quality you can achieve while retaining the same labeling costs i.e., training data-efficient (specifically, label efficient) models. We will describe the general principles of Self-Supervised

Keras i Keras ii About the Tutorial Keras is an open source deep learning framework for python. It has been developed by an artificial intelligence researcher of algorithms, inspired from the model of human brain. Deep learning is becoming more popular in data science fields like robotics, artificial intelligence(AI), audio & video recognition and image recognition Theano, etc., for creating deep learning models. Overview of Keras Keras runs on top of open source machine libraries like TensorFlow, Theano or Cognitive Toolkit (CNTK). Theano is a python library

models and multimodal models are pretrained on large-scale multilingual and multimodal data and post-trained on quality data for aligning to human preferences. Qwen is capable of natural language understanding 首先使用 ChatML 模板对其进行格式化。例如： data = [] for msg in messages: msg = c['messages'] text = tokenizer.apply_chat_template(msg, tokenize=False, add_generation_ �→prompt=False) data.append(text.strip()) 其中每个 named Qwen..."} ] 然后只需通过一行代码运行校准过程： model.quantize(tokenizer, quant_config=quant_config, calib_data=data) 最后，保存量化模型： 14 Chapter 1. 文档 Qwen model.save_quantized(quant_path, safetensors=True, shard_size="4GB")

aspects of the training pipeline like data augmentation, layer and channel configurations can also be parameterized using hyperparameters. For example, when using image data augmentation with rotation, we can might also have additional parameters which could be searched as well. transformation parameters in data augmentation layer contribute to performance improvements while others like learning rate, batch layers.Dense(size, activation='relu'), layers.Dense(5, activation='softmax') ]) Our model, input data and the hyperparameter trial set is ready. Let's go ahead and train the model, each time choosing

In the first chapter, we briefly introduced learning techniques such as regularization, dropout, data augmentation, and distillation to improve quality. These techniques can boost metrics like accuracy precision, recall, etc. which often are our primary quality concerns. We have chosen two of them, namely data augmentation and distillation, to discuss in this chapter. This is because, firstly, regularization and dropout are fairly straight-forward to enable in any modern deep learning framework. Secondly, data augmentation and distillation can bring significant efficiency gains during the training phase, which

Overview of Compression One of the simplest approaches towards efficiency is compression to reduce data size. For the longest time in the history of computing, scientists have worked tirelessly towards popular example of lossless data compression algorithm is Huffman Coding, where we assign unique strings of bits (codes) to the symbols based on their frequency in the data. More frequent symbols are assigned and the path to that symbol is the bit-string assigned to it. This allows us to encode the given data in as few bits as possible, since the most frequent symbols will take the least number of bits to

import Image from torch import nn from torch.nn import functional as F from torch.utils import data from torchvision import transforms 目标受众 本书面向学生（本科生或研究生）、工程师和研究人员，他们希望扎实掌握深度学习的实用技术。因为我们 从头开始解 编写了一个“学习”程序。如果我们用一个巨大的带标签的数 据集，它很可能可以“学习”识别唤醒词。这种“通过用数据集来确定程序行为”的方法可以被看作用数据 编程（programming with data）。比如，我们可以通过向机器学习系统，提供许多猫和狗的图片来设计一个 “猫图检测器”。检测器最终可以学会：如果输入是猫的图片就输出一个非常大的正数，如果输入是狗的图片 就会输出一个非常小的负数 学习的一个主要分支，本节稍后的内容将对其 进行更详细的解析。 1.2 机器学习中的关键组件 首先介绍一些核心组件。无论什么类型的机器学习问题，都会遇到这些组件： 1. 可以用来学习的数据（data）； 2. 如何转换数据的模型（model）； 3. 一个目标函数（objective function），用来量化模型的有效性； 4. 调整模型参数以优化目标函数的算法（algorithm）。