.......................................................................................... 14 Workflow of ANN ........................................................................................ 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 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

Assume that we are working on a model for a home-automation device. Figure 3-4 shows the high level workflow of such a device. The model continuously classifies audio signals into one of the four classes, fourth class (none) indicates the absence of an acceptable keyword in the input signal. Figure 3-4: Workflow of a home-automation device which detects three spoken words: hello weather and time. The output 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

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

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

number-crunching at the heart of deep learning. AlexNet1 was one of the earliest models to rely on Graphics Processing Units (GPUs) for training, which could 1 Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton "Imagenet classification with deep convolutional neural networks." Advances in neural information processing systems 25 (2012): 1097-1105. do linear algebra operations such as multiplying two matrices together ImageNet dataset was a big boon in this aspect. It has more than 1 million labeled images, where each image belongs to 1 out of 1000 possible classes. This helped with creating a testbed for researchers to

high quality image of a cat. The cat on the right is a lower quality compressed image. Source Both the cat images in figure 2-2 might serve their purpose equally well, but the compressed image is an order smaller. Discrete Cosine Transform (DCT), is a popular algorithm which is used in the JPEG format for image compression, and the MP3 format for audio. DCT breaks down the given input data into independent components transmitting images back to earth. However, transmission costs make it infeasible to send the original image. Can we compress the transmission, and decompress it on arrival? If so, what would be the ideal tradeoff

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

(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 ImageNet数据集发布，并发起ImageNet挑战赛：要求研究人员从100万个样本中训练模型，以区分1000个不同 类别的对象。ImageNet数据集由斯坦福教授李飞飞小组的研究人员开发，利用谷歌图像搜索（Google Image Search）对每一类图像进行预筛选，并利用亚马逊众包（Amazon Mechanical Turk）来标注每张图片的相关 类别。这种规模是前所未有的。这项被称为ImageNet的挑战赛推动了计算机视觉和机器学习研究的发展，挑 昂的许多线性代 数层传递数据。这也是为什么在20世纪90年代至21世纪初，优化凸目标的简单算法是研究人员的首选。然而， 用GPU训练神经网络改变了这一格局。图形处理器（Graphics Processing Unit，GPU）早年用来加速图形处 理，使电脑游戏玩家受益。GPU可优化高吞吐量的4 × 4矩阵和向量乘法，从而服务于基本的图形任务。幸运 的是，这些数学运算与卷积层的计算惊人地相似

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 这种算法固然简单直接，但是面对大规模、高维度数据的优化问题时计算效率极低， 基本不可行。梯度下降算法(Gradient Descent)是神经网络训练中最常用的优化算法，配合 强大的图形处理芯片 GPU(Graphics Processing Unit)的并行加速计算能力，非常适合优化海 量数据的神经网络模型，自然也适合优化这里的神经元线性模型。这里先简单地应用梯度 下降算法，来解决神经元模型预测的问题。由于梯度下降算法是深度学习的核心算法之

and channel configurations can also be parameterized using hyperparameters. For example, when using image data augmentation with rotation, we can treat the angle of rotation as a hyper-parameter. Think of chapter 3. # Dataset image size IMG_SIZE = 264 def resize_image(image, label): image = tf.image.resize(image, [IMG_SIZE, IMG_SIZE]) image = tf.cast(image, tf.uint8) return image, label train_ds = train_ds train_ds.map(resize_image) val_ds = val_ds.map(resize_image) test_ds = test_ds.map(resize_image) Note that the create_model() function here has two additional parameters: learning_rate and dropout_rate