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

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

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

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

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

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

features in the input. Recurrent Neural Nets (RNNs) facilitated learning from the sequences and temporal data. These breakthroughs contributed to bigger and bigger models. Although they improved the quality of you see, books you read, food you enjoy and so on), without the need of knowing all the encyclopedic data about them. When working with deep learning models and inputs such as text, which are not in numerical high-dimensional data into low-dimension, while retaining the properties from the high-dimensional representation. It is useful because it is often computationally infeasible to work with data that has a large

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 ModelScope • Qwen1.5 Collection 加入社区，加入 Discord 和 微信群 。很期待见到你们！ 快速开始 1 Qwen 2 快速开始 CHAPTER1 文档 1.1 安装 要快速上手 Qwen1.5，您可以从 Hugging Face 安装 transformers 库，并使用 Qwen1.5 Collection 中的模型。 我们建议您安装最新版本的 transformers 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

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）。

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：