Don'ts Leverage the color palettes and keep things simple, ensuring there is a strong contrast between the symbol and the background. Don’t use colors that aren’t in the approved color palette or or primary brand color, please use it sparingly. We prefer to apply PyTorch Orange as a deliberate accent. To achieve the best AA compliance color contrast, PyTorch has a special color palette to best applying color in the digital environment; web, app, and social media posts, please reference the digital RGB or hex code equivalent. When printing, please use CMYK or the listed Pantone code. For

English to Spanish translation model. Let’s dig deeper into each of these categories using examples and code samples. Label Invariant Transformations Label invariant transformations transform samples such These values are clipped to 255. We will discuss some examples of image transformations below. The code samples are provided to bridge the theory and practice gap. We have prepared a few helper functions: uint8) img = cv2.imdecode(img_array, cv2.IMREAD_COLOR) img = cv2.resize(img, (IMG_SIZE, IMG_SIZE), cv2.INTER_AREA) return cv2.cvtColor(img, cv2.COLOR_BGR2RGB).astype(int) def show_image(image): # Display

AutoTokenizer device = "cuda" # the device to load the model onto # Now you do not need to add "trust_remote_code=True" model = AutoModelForCausalLM.from_pretrained( "Qwen/Qwen1.5-7B-Chat", torch_dtype="auto", device_map="auto" AutoTokenizer 借助 TextStreamer ，chat 的流式模式变得非常简单。下面我们将展示一个如何使用它的示例： ... # Reuse the code before `model.generate()` in the last code snippet from transformers import TextStreamer streamer = TextStreamer(tokenizer AutoTokenizer device = "cuda" # the device to load the model onto # Now you do not need to add "trust_remote_code=True" model = AutoModelForCausalLM.from_pretrained( "Qwen/Qwen1.5-7B-Chat", torch_dtype="auto", device_map="auto"

plt.plot(estimates[:, i].numpy(), label=("P(die=" + str(i + 1) + ")")) d2l.plt.axhline(y=0.167, color='black', linestyle='dashed') d2l.plt.gca().set_xlabel('Groups of experiments') d2l.plt.gca().set_ylabel('Estimated AlexNet和LeNet的架构非常相似，如 图7.1.2所示。注意，本书在这里提供的是一个稍微精简版本的AlexNet， 去除了当年需要两个小型GPU同时运算的设计特点。 89 https://code.google.com/archive/p/cuda‐convnet/ 250 7. 现代卷积神经网络 图7.1.2: 从LeNet（左）到AlexNet（右） AlexNet和LeNet的设计理念非常相似，但也存在显著差异。 show_trace_2d(f, results): #@save """显示优化过程中2D变量的轨迹""" d2l.set_figsize() d2l.plt.plot(*zip(*results), '-o', color='#ff7f0e') x1, x2 = torch.meshgrid(torch.arange(-5.5, 1.0, 0.1), torch.arange(-3.0, 1.0, 0.1), indexing='ij')

538-pixel TIFF image named bird large.tiff. It looks like the picture below. In a straightforward 24-bit color representation of this image, each pixel is represented as three 8-bit numbers (ranging from 0 to K-means to reduce the color count to k = 16. That is, you will compute 16 colors as the cluster centroids and replace each pixel in the image with its nearest cluster centroid color. Because computing cluster the means will be initialized to the same color (i.e. black). Depending on your implementation, all of the pixels in the photo that are closest to that color may get assigned to one of the means, leaving

Anaconda to my PATH environment variable”一项，这样可以通过命令行方式调用 Anaconda 程序。如图 1.23 所示，安装程序 询问是否连带安装 VS Code 软件，选择 Skip 即可。整个安装流程约持续 5 分钟，具体时间 预览版202112 第 1 章 人工智能绪论 18 需依据计算机性能而定。 图 1.22 Anaconda 语言编写程序的方式非常多，可以使用 ipython 或者 ipython notebook 方式 交互式编写代码，也可以利用 Sublime Text、PyCharm 和 VS Code 等综合 IDE 开发中大型 项目。本书推荐使用 PyCharm 编写和调试，使用 VS Code 交互式开发，这两者都可以免费 使用，用户自行下载安装，并配置好 Python 解释器即可。限于篇幅，这里不再赘述。 预览版202112 gca(projection='3d') ax.xaxis.set_pane_color((1.0, 1.0, 1.0, 0.0)) ax.yaxis.set_pane_color((1.0, 1.0, 1.0, 0.0)) ax.zaxis.set_pane_color((1.0, 1.0, 1.0, 0.0)) # 绘制权值矩阵范围 surf

set_xlabel ( ’ count ’ ) ax . set_ylabel ( ’ cor rect (%) ’ ) plt . plot ( count , correctCurve , color=’ red ’ , linewidth =2.0 , l i n e s t y l e=’− ’ ) plt . show () 我们可以得到结果（我训练了很多次，有时候训练 1000 轮以后的正确率只有

flow_from_directory keras.preprocessing.image.flow_from_directory(directory, target_size=(256,256), color_mode='rgb', classes=None, class_mode='categorical', batch_size=32, shuffle=True, seed=None, 图像，都将被包含在生成器中。更多细节，详见 此脚本。 • target_size: 整数元组 (height, width)，默认：(256, 256)。所有的图像将被调整到的尺 寸。 • color_mode: “grayscale”, “rbg” 之一。默认：“rgb”。图像是否被转换成 1 或 3 个颜色通道。 • classes: 可选的类的子目录列表（例如 ['dogs', 'ca

learning follow right after. The quantization section delves into the implementation details using code samples. We finish with a hands-on project that will walk you through the process of applying quantization contains the symbol-code mapping is transmitted along with the encoded data. Figure 2-1: Huffman Encoding & Huffman Tree. Source When decoding the encoded data, we look up the code from the lookup table back. Since the codes are unique for each symbol (in fact, they are prefix codes: no code is a prefix of some other code, which eliminates ambiguity when decoding), we can easily construct the original sequence

/Miniconda3-latest-Linux-x86_64.sh • After Miniconda is installed: conda install pytorch -c pytorch Writing code • Up to you; feel free to use emacs, vim, PyCharm, etc. if you want. • Our recommendations: • Install: 1234:localhost:1234 __@__.cs.princeton.edu • First blank is username, second is hostname Jupyter Notebook VS Code • Install the Python extension. • ???????????? Install the Remote Development extension. • Python Jupyter notebooks by delimiting cells/sections with #%% • Debugging PyTorch code is just like debugging any other Python code: see Piazza @108 for info. Also try Jupyter Lab! Why talk about libraries