0 PyTorch Release 22.08 PyTorch RN-08516-001_v23.07 | 93 ‣ Jupyter Core 4.6.1 ‣ Jupyter Notebook 6.0.3 ‣ JupyterLab 2.3.2, including Jupyter-TensorBoard ‣ JupyterLab Server 1.0.6 ‣ Jupyter-TensorBoard 0 PyTorch Release 22.07 PyTorch RN-08516-001_v23.07 | 100 ‣ Jupyter Core 4.6.1 ‣ Jupyter Notebook 6.0.3 ‣ JupyterLab 2.3.2, including Jupyter-TensorBoard ‣ JupyterLab Server 1.0.6 ‣ Jupyter-TensorBoard 0 PyTorch Release 22.06 PyTorch RN-08516-001_v23.07 | 107 ‣ Jupyter Core 4.6.1 ‣ Jupyter Notebook 6.0.3 ‣ JupyterLab 2.3.2, including Jupyter-TensorBoard ‣ JupyterLab Server 1.0.6 ‣ Jupyter-TensorBoard

on your computer • jupyter notebook • ???????????? Run on Princeton CS server • Pick any 4-digit number, say 1234 • ???????????? hostname -s • ???????????? jupyter notebook --no-browser --port=1234 • 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 https://github.com/szagoruyko/pytorchviz References • Important References: • For setting up jupyter notebook on princeton ionic cluster • https://oncomputingwell.princeton.edu/2018/05/jupyter-on-the-cluster/

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 738 16 附录：深度学习工具 741 16.1 使用Jupyter Notebook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 741 xiv 16.1.1 在本地编辑和运行代码 运行和停止实例 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 749 16.2.4 更新Notebook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 749 16.3 使用Amazon EC2实例 https://discuss.d2l.ai/ 7 https://discuss.d2l.ai/t/2086 8 目录 安装 我们需要配置一个环境来运行 Python、Jupyter Notebook、相关库以及运行本书所需的代码，以快速入门并 获得动手学习经验。 安装 Miniconda 最简单的方法就是安装依赖Python 3.x的Miniconda8。如果已安装conda，则可以跳过以下步骤。访

python module for demonstrating compression. The code for this exercise is available as a Jupyter notebook here. %%capture import gzip import operator, random import numpy as np import tensorflow as original segmentation project in chapter four. The code for this project is available as a Jupyter notebook here. def create_model_for_pruning(m, prunables, info=True): def apply_pruning_to_conv_blocks(block): clustering with a real example. The code for the next few exercises is available here as a Jupyter notebook. Using clustering to compress a 1-D tensor. Let us first implement the Within-Cluster-Sum-of-Squares

th_AVX ��������� TensorFlow Jupyter Notebook ������� (venv) $ pip install jupyter (venv) $ python –m ipykernel install --user --name=venv � Jupyter Notebook ��� TensorFlow “Hello TensorFlow” Try tensorflow/tensorflow:nightly-jupyter 4. Start a TensorFlow Docker container $ docker run -it -p 8888:8888 -v $(notebook-examples-path):/tf/notebooks tensorflow/tensorflow:nightly-jupyter “Hello TensorFlow” Try it

Our goal is to classify a given piece of text into one of the fourteen categories. The Jupyter notebook is available here for you to play with. We have already downloaded the dataset in the dbpedia_csv params: 0 We will spare you the training logs here, but you are welcome to inspect them in the notebook directly. Figure 4-12 shows that the CNN models perform better than BOW as they benefit from the compare their training efficiency and quality metrics. As always, the code is available as a Jupyter notebook here for you to experiment. Let’s get started with loading the dataset. import tensorflow as tf

??(?, ?) (3) ???(?1 + ?2, ?) = ???(?1, ?) + ???(?2, ?) 50 Python 的环境的安装 ⚫Anaconda ⚫Jupyter notebook ⚫Pycharm 详细教程：https://zhuanlan.zhihu.com/p/59027692 3. 机器学习的背景知识-Python基础 51 Python 的环境的安装 com/distribution/ 通常选3.7版本，64位 可以用默认安装，右图两个选择框都勾上 52 Python 的环境的安装 ⚫Jupyter notebook 在cmd环境下，切换到代码的 目录，输入命令： jupyter notebook之后就可以 启动jupyter botebook编辑器 ，启动之后会自动打开浏览器 ，并访问http://localhost:8088 ，默认跳转到

??(?, ?) (3) ???(?1 + ?2, ?) = ???(?1, ?) + ???(?2, ?) 51 Python 的环境的安装 ⚫Anaconda ⚫Jupyter notebook ⚫Pycharm 详细教程：https://zhuanlan.zhihu.com/p/59027692 3. 机器学习的背景知识-Python基础 52 Python 的环境的安装 anaconda.com/distribution/ 通常选64位 可以用默认安装，右图两个选择框都勾上 53 Python 的环境的安装 ⚫Jupyter notebook 在cmd环境下，切换到代码的 目录，输入命令： jupyter notebook之后就可以 启动jupyter botebook编辑器 ，启动之后会自动打开浏览器 ，并访问http://localhost:8088 ，默认跳转到

pre-trained ResNet50 model and fine tune it. The code for this project is available as a Jupyter notebook here. Tensorflow provides easy access to this dataset through the tensorflow-datasets package. We will discuss some of them in detail in this section. The code is available here as a Jupyter notebook for you to experiment. The following code snippet sets up the modules, functions and variables Project: Distillation of a Speech Model. The code for this project is available here as a Jupyter notebook. Let’s start off with doing the regular imports: import numpy as np import tensorflow_datasets

Jupyter framework or with other cloud services. The solution to this specific exercise is in this notebook. Solution: With the logistics out of the way, let’s look at how to solve this exercise. We use Loading and Processing the MNIST Dataset Before we start, the code is available as a Jupyter notebook here. Now let’s take a look at the load_data() function in the following code snippet. It uses