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

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略 41 ChatGPT得益于通用（基础）模型所构建 AI 系统的新范式 资料来源：《On the Opportunities and Risks of Foundation Models 》论文 ◼ 基础模型（Foundation Model）在广泛的应用中整合构建机器学习系统的方法，它为许多任务提供了强大的杠杆作用 ✓ 基础模型是在深度神经网络和自我监督学习的基础上演化而来。基础模型 的改进， 有证据表明，日志丢失与许多下游任务密切相关，随着规模的增长，日 志丢失呈现平稳的改善趋势。 资料来源：《On the Opportunities and Risks of Foundation Models 》论文 图：基础模型可以集中来自各种模态的所有数据的信息， 然后这一模型可以适用于广泛的下游任务 ChatGPT核心技术优势 43 参考文献 1. IAN GO

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 -U scikit-learn Seaborn Seaborn is an amazing library that allows you to easily visualize your data. Use the below command to install: pip install seaborn You could see the message similar as specified json { "image_data_format": "channels_last", "epsilon": 1e-07, "floatx": "float32", "backend": "tensorflow" } Here,  image_data_format represent the data format.  epsilon

metrics=['accuracy']) # 生成虚拟数据 import numpy as np data = np.random.random((1000, 100)) labels = np.random.randint(2, size=(1000, 1)) # 训练模型，以 32 个样本为一个 batch 进行迭代 model.fit(data, labels, epochs=10, batch_size=32) compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy']) # 生成虚拟数据 import numpy as np data = np.random.random((1000, 100)) labels = np.random.randint(10, size=(1000, 1)) # 将标签转换为分类的 one-hot one_hot_labels = keras.utils.to_categorical(labels, num_classes=10) # 训练模型，以 32 个样本为一个 batch 进行迭代 model.fit(data, one_hot_labels, epochs=10, batch_size=32) 3.1.5 例子 这里有几个可以帮助你开始的例子！ 在 examples 目录 中，你可以找到真实数据集的示例模型：

tensor 理解为是“data”。 我们需要先导入 pytorch，顺便导入 numpy： import torch import numpy as np 现在我们尝试将 list 或者 np.array 转换为 pytorch 的数组： data1 = [ [ 1 , 2 ] , [ 3 , 4 ] ] data_tensor = torch . tensor ( data1 ) print print ( data_tensor . shape ) np_array1 = np . array ( data1 ) data_tensor = torch . from_numpy( np_array1 ) print ( data_tensor . shape ) 输出都是： torch . Size ( [ 2 , 2 ] ) 对于二维 tensor 之间的相乘，@ 和 .matmul 7 y = data_tensor @ data_tensor .T print (y) y = data_tensor ∗ data_tensor print (y) 输出分别是： [ [ 5 , 11] , [11 , 2 5 ] ] [ [ 5 , 11] , [11 , 2 5 ] ] tensor 可以转化为 numpy： np_array2 = data_tensor

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

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）。

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

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