effects of transformations visually. The above list is not exhaustive, rather we have used it as a guide to help make better transformation choices. A few other commonly used techniques are contrast augmentation sections preserves the candidate’s profile. Below is an example of a paragraph picked from the Telegram Style page on wikipedia. The first paragraph is the original version. The shuffled version follows it. Barring original and the shuffled sentences convey identical information. Original: “In some ways, "telegram style" was the precursor to the modern language abbreviations employed in "texting" or the use of short

于深度神经网络可以有效地表示多个层次的图像，因此这种分层表示已成功用于各种计算机视觉任务，例如 目标检测（object detection）、语义分割（semantic segmentation）和样式迁移（style transfer）。秉承计算 机视觉中利用分层表示的关键思想，我们将从物体检测的主要组件和技术开始，继而展示如何使用完全卷积 网络对图像进行语义分割，然后我们将解释如何使用样式迁移技术来生成像本书封面一样的图像。最后在结 一个滤波器通常只能改变照片的某个方面。如果要照片达到理想中的风格，可能需要尝试大量不同的组合。 这个过程的复杂程度不亚于模型调参。 本节将介绍如何使用卷积神经网络，自动将一个图像中的风格应用在另一图像之上，即风格迁移（style trans‐ fer）(Gatys et al., 2016)。这里我们需要两张输入图像：一张是内容图像，另一张是风格图像。我们将使用神 经网络修改内容图像，使其在风格上接近风格图像。例如，图13 Image.open('../img/rainier.jpg') d2l.plt.imshow(content_img); style_img = d2l.Image.open('../img/autumn-oak.jpg') d2l.plt.imshow(style_img); 13.12.3 预处理和后处理 下面，定义图像的预处理函数和后处理函数。预处理函数preprocess对输入图像在RGB三个通道分别做标准

Install Docker. ‣ For NVIDIA DGX™ users, see Preparing to use NVIDIA Containers Getting Started Guide. ‣ For non-DGX users, see NVIDIA ® GPU Cloud ™ (NGC) container registry installation documentation that you have access and can log in to the NGC container registry. Refer to NGC Getting Started Guide for more information. The deep learning frameworks, the NGC Docker containers, and the deep learning examples can be found here. For more information about AMP, see the Training With Mixed Precision Guide. Tensor Core Examples The tensor core examples provided in GitHub and NGC focus on achieving the

有关一些简单的用法示例，请参阅 应用模块的文档。 有关如何使用此类预训练的模型进行特征提取或微调的详细示例，请参阅 此博客文章。 VGG16 模型也是以下几个 Keras 示例脚本的基础： • Style transfer • Feature visualization • Deep dream 3.3.18 如何在 Keras 中使用 HDF5 输入？ 你可以使用 keras.utils ast’，输出 4D 张量，尺寸为 (batch, new_rows, new_cols, filters)。 由于填充的原因，rows 和 cols 值可能已更改。 参考文献 • A guide to convolution arithmetic for deep learning • Deconvolutional Networks 5.3.5 Conv3D [source] keras

com/applied-deep-learning-part-3-autoencoders- 1c083af4d798 https://towardsdatascience.com/a-wizards-guide-to-adversarial-autoencoders-part-1- autoencoder-d9a5f8795af4 How to Train? PCA V.S. Auto-Encoders Adversarial AutoEncoders ▪ Distribution of hidden code https://towardsdatascience.com/a-wizards-guide-to-adversarial-autoencoders-part-2- exploring-latent-space-with-adversarial-2d53a6f8a4f9 Adversarial Adversarial AutoEncoders ▪ Give more details after GAN https://towardsdatascience.com/a-wizards-guide-to-adversarial-autoencoders-part-2- exploring-latent-space-with-adversarial-2d53a6f8a4f9 Another Approach:

浅层学到的特征为简单的边缘、角 点、纹理、几何形状、表面等 深层学到的特征则更为复杂抽象，为狗 、人脸、键盘等等 24 2.神经风格迁移 ?(?) = ??content(?, ?) + ??style(?, ?) 两个超参数?和?来确定内容代价和风格代价 给你一个内容图像?，给定一个风格图 片?，而你的目标是生成一个新图片? 25 2.神经风格迁移 • 随机初始化生成图像?，如10 2.神经风格迁移 内容代价函数（Content cost function） ?(?) = ??content(?, ?) + ??style(?, ?) 两个超参数?和?来确定内容代价和风格代价 27 2.神经风格迁移 风格代价函数（Style cost function） 28 2.神经风格迁移 29 一维到三维推广 30 一维到三维推广 31 一维到三维推广 32

well as time-consuming. However, as initial pointers you can refer to this guide for pre-training BERT in Keras, and this guide for some optimizations to make it efficient. Also consider going through the

languages (like Java for Android or C++ for iOS and other platforms) for inference. The authoritative guide for TFLite inference is available on the tensorflow website. def tflite_model_eval(model_content As mentioned earlier, the tflite evaluation is a boiler-plate code. You can refer to the TFLite guide for more details. We start the model conversion by creating a converter object using the from_keras_model()

__init__ ▪ implement forward() Step1. Step2. Step3. nn.ReLU v.s. F.relu() ▪ class-style API ▪ function-style API Train 下一课时 激活函数与GPU Thank You.

Convolution Moving window Several kernels Animation https://medium.freecodecamp.org/an-intuitive-guide-to-convolutional-neural- networks-260c2de0a050 Notation Input_channels: Kernel_channels: 2 ch Kernel_size: