in data science fields like robotics, artificial intelligence(AI), audio & video recognition and image recognition. Artificial neural network is the core of deep learning methodologies. Deep learning machine. If anaconda is not installed, then visit the official link, https://www.anaconda.com/distribution/ and choose download based on your OS. Create a new conda environment Launch anaconda prompt keras/keras.json. keras.json { "image_data_format": "channels_last", "epsilon": 1e-07, "floatx": "float32", "backend": "tensorflow" } Here,  image_data_format represent the data format

probability Random variables and probability distributions Joint probability distribution Independence Conditional probability distribution Bayes’ Theorem ... ... Feng Li (SDU) GDA, NB and EM September 27 X(s) = x}) Feng Li (SDU) GDA, NB and EM September 27, 2023 8 / 122 Probability Distribution Probability distribution for discrete random variables Suppose X is a discrete random variable X : S → A pX(x) = 1 Feng Li (SDU) GDA, NB and EM September 27, 2023 9 / 122 Probability Distribution (Contd.) Probability distribution for continuous random variables Suppose X is a continuous random variable X

target label is a composite of the inputs that were combined. A combination of a dog with a hamster image (figure 3-5) is assigned a composite [dog, hamster] label! 2 A whale’s tail fins are called flukes problems. Figure 3-5: A mixed composite of a dog (30%) and a hamster (70%). The label assigned to this image is a composite of the two classes in the same proportion. Thus, the model would be expected to predict a dataset Nx the size? What are the constraining factors? An image transformation recomputes the pixel values. The rotation of an RGB image of 100x100 requires at least 100x100x3 (3 channels) computations

respectively. We now introduce Bayesian inference by taking image recognition as an example. Our aim is to identify if there is a cat in a given image. We assume X = [X1, X2, · · · , Xn]T is a random variable representing the feature vector of the given image, and Y ∈ {0, 1} is a random variable representing if there is a cat in the given image. Now, given an image x = [x1, x2, · · · , xn]T , out goal is to calculate = x) is the probability that the image is labeled by y given that the image can be represented by feature vector x, P(X = x | Y = y) is the probability that the image has its feature vector being x given

humans can intuitively grasp similarities between different objects. For instance, when we see an image of a dog or a cat, it is likely that we would find them both to be cute. However, a picture of a snake following goals: a) To compress the information content of high-dimensional concepts such as text, image, audio, video, etc. to a low-dimensional representation such as a fixed length vector of floating perspective of training the model, it is agnostic to what the embedding is for (a piece of text, audio, image, video, or some abstract concept). Here is a quick recipe to train embedding-based models: 1. Embedding

two patches from a training example and train the model to predict their relative position in the image (refer to figure 6-4 (a)). They demonstrate that using a network pre-trained in this fashion improves initializing the network. Similarly, another task is to predict the degree of rotation for a given rotated image3 (refer figure 6-4 (b)). The authors report that the network trained in a self-supervised manner this supervised network. 3 Gidaris, Spyros, et al. "Unsupervised Representation Learning by Predicting Image Rotations." arXiv, 21 Mar. 2018, doi:10.48550/arXiv.1803.07728. 2 Doersch, Carl, et al. "Unsupervised

tower_3], axis=1) 3.2.7.2 卷积层上的残差连接 有关残差网络 (Residual Network) 的更多信息，请参阅 Deep Residual Learning for Image Recogni- tion。 from keras.layers import Conv2D, Input # 输入张量为 3 通道 256x256 图像 x = Input(shape=(256 add(MaxPooling2D((2, 2))) vision_model.add(Flatten()) # 现在让我们用视觉模型来得到一个输出张量： image_input = Input(shape=(224, 224, 3)) encoded_image = vision_model(image_input) # 接下来，定义一个语言模型来将问题编码成一个向量。 # 每个问题最长 100 个词，词的索引从 1 到 concatenate([encoded_question, encoded_image]) # 然后在上面训练一个 1000 词的逻辑回归模型： output = Dense(1000, activation='softmax')(merged) # 最终模型： vqa_model = Model(inputs=[image_input, question_input], outputs=output)

experience. In the container, see /workspace/README.md for information about customizing your PyTorch image. For more information about PyTorch, including tutorials, documentation, and examples, see: ‣ PyTorch for NGC containers, when you run a container, the following occurs: ‣ The Docker engine loads the image into a container which runs the software. ‣ You define the runtime resources of the container by in your system depends on the DGX OS version that you installed (for DGX systems), the NGC Cloud Image that was provided by a Cloud Service Provider, or the software that you installed to prepare to run

机中的语音助手、汽车上 的智能辅助驾驶、人脸支付等。下面将从计算机视觉、自然语言处理和强化学习 3 个领域 入手，为大家介绍深度学习的一些主流应用。 1.4.1 计算机视觉 图片识别(Image Classification) 是常见的分类问题。神经网络的输入为图片数据，输出 值为当前样本属于每个类别的概率分布。通常选取概率值最大的类别作为样本的预测类 别。图片识别是最早成功应用深度学习的任务之一，经典的网络模型有 果，具有时间维度信息的 3D 视频理解任务受到越来越多的关注。常见的视频理解任务有 视频分类、行为检测、视频主体抽取等。常用的模型有 C3D、TSN、DOVF、TS_LSTM 等。 图片生成(Image Generation) 是指通过学习真实图片的分布，并从学习到的分布中采样 而获得逼真度较高的生成图片。目前常见的生成模型有 VAE 系列、GAN 系列等。其中 GAN 系列算法近年来取得了巨大的进展，最新 软件， 用户通过安装 Anaconda 软件，可以同时获得 Python 解释器、包管理和虚拟环境等一系列 便捷功能，何乐而不为呢。首先从 https://www.anaconda.com/distribution/#download-section 网址进入 Anaconda 下载页面，选择 Python 最新版本的下载链接即可下载，下载完成后安 装即可进入安装程序。如图 1.22 所示，勾选”Add

(continues on next page) 目录 5 (continued from previous page) import torchvision from PIL import Image from torch import nn from torch.nn import functional as F from torch.utils import data from torchvision • 环境是否变化？例如，未来的数据是否总是与过去相似，还是随着时间的推移会发生变化？是自然变化 还是响应我们的自动化工具而发生变化？ 当训练和测试数据不同时，最后一个问题提出了分布偏移（distribution shift）的问题。接下来的内容将简要 描述强化学习问题，这是一类明确考虑与环境交互的问题。 1.3.4 强化学习 如果你对使用机器学习开发与环境交互并采取行动感兴趣，那么最终可能 as d2l 在统计学中，我们把从概率分布中抽取样本的过程称为抽样（sampling）。笼统来说，可以把分布（distribution） 看作对事件的概率分配，稍后我们将给出的更正式定义。将概率分配给一些离散选择的分布称为多项分布 （multinomial distribution）。 为了抽取一个样本，即掷骰子，我们只需传入一个概率向量。输出是另一个相同长度的向量：它在索引i处的 值是采样结果中i出现的次数。