after. Following the lead from the previous chapters, the theory is complemented with programming projects to assist readers to implement these techniques from scratch. Our journey of learning techniques model architecture. Their benefit is clear in the low data situations as demonstrated through the projects. In the next section we will discuss label invariant transformations which transform both the inputs given Model object from the given checkpoint path. We are going to use these callbacks in future projects as well. import os # Now let us create a callback for saving the best checkpoint so far. # It

techniques, infrastructure, and other helpful topics where you can get your hands dirty with practical projects. With that being said, let’s start off on our journey to more efficient deep learning models. apart from being used in production they have also been used in the famous AlphaGo and AlphaZero projects, where DL models beat the best humans as well as other computer bots in games like chess, shogi disposal. In the following chapters, we would go over each of the above areas in more detail along with projects, to get your hands dirty with optimizing machine learning models.

models capable of running on mobile and edge devices. We have also set up a couple of programming projects for a hands-on model optimization experience using these efficient layers and architectures. Let’s inputs, and the label for a given sample text in the CBOW task. 7 GloVe - https://nlp.stanford.edu/projects/glove 6 Mikolov, Tomas, Kai Chen, Greg Corrado, and Jeffrey Dean. "Efficient estimation of word set the stage for your exploration of efficient layers and architectures for your deep learning projects. They can often be combined with other approaches like quantization, distillation, data augmentation

the PyTorch name and marks when accurately referencing the PyTorch Foundation or its software projects. When referring to our marks, please include the following attribution statement: 
 “PyTorch

examples in this book are available at the EDL GitHub repository. The code examples for all the projects are available in the repository in the form of Jupyter notebooks. You can run the notebooks in

can help compress the transmissions some more, so that they can use the saved bandwidth for other projects. The goal in this scenario is to use clustering to outperform our quantization-based solution.

Step 1: Create virtual environment Virtualenv is used to manage Python packages for different projects. This will be helpful to avoid breaking the packages installed in the other environments. So, it

[''], [] data_dir = d2l.download_extract(embedding_name) # GloVe网站：https://nlp.stanford.edu/projects/glove/ # fastText网站：https://fasttext.cc/ with open(os.path.join(data_dir, 'vec.txt'), 'r') as elems[0], [float(elem) for elem in elems[1:]] (continues on next page) 194 https://nlp.stanford.edu/projects/glove/ 195 https://fasttext.cc/ 14.7. 词的相似性和类比任务 679 (continued from previous page) # 跳过标题信息，例如fastText中的首行 import nn from d2l import torch as d2l #@save d2l.DATA_HUB['SNLI'] = ( 'https://nlp.stanford.edu/projects/snli/snli_1.0.zip', '9fcde07509c7e87ec61c640c1b2753d9041758e4') data_dir = d2l.download_extract('SNLI')