quickly. Even after that it is likely that the model might not be able to capture the intricacies of your task well. Self-Supervised learning helps to significantly improve the quality you can achieve while its efficacy through a colab. Finally, we introduce miscellaneous techniques to help you improve your model’s quality metrics without taking a hit on any of the footprint metrics. These techniques might lends itself to compute efficiency since only have to train the model on a small number of examples, saving training time compute too. A Typical Self-Supervised Learning Recipe We can break-down common self-supervised

using them in your deep learning models. We start with sparsity. If your goal was to optimize your brain for storage, you can often trim a lot of useless trivia without it impacting your life materially materially. This is also how your brain naturally works. You can prune all this extra information and still drive well, eat, sleep etc. To memorize something for the long term, you need to improve recall by repetition large network could be safely removed with minimal performance deterioration. A random removal could work for removing a few weights. However, when pruning a large number of weights, say 60%, we risk the

finally the tools at your disposal to achieve what you want. The subsequent chapters will delve deeper into techniques, infrastructure, and other helpful topics where you can get your hands dirty with practical To illustrate, when you visit Netflix’s homepage, the recommendations that show up are based on your past interests, what is popular with other users at that time, and so on. If you have seen ‘The Office’ which might be popular with other users too. If we train a model to predict the probability based on your behavior and currently trending content, the model will assign a high probability to Seinfeld. While

techniques like data augmentation and distillation, might not be viewed as techniques that help you improve your model footprint. However, given that they lead to an improvement in quality metrics, we can use them print(val_ds.as_numpy_iterator().next()[0].shape) (264, 264, 3) (264, 264, 3) Our dataset is ready. Let’s work on the model. We use a pre-trained ResNet50 model with the top (softmax) layer replaced with a new rotation is one of the most common transformations. Imagine you are taking a picture of a flower using your phone. The orientation of a camera is unlikely to be the same between two successive pictures. Even

PyTorch Tutorial Willie Chang Pranay Manocha Installing PyTorch • ???????????? On your own computer • Anaconda/Miniconda: conda install pytorch -c pytorch • Others via pip: pip3 install torch • ?? recommended, because: • It lets you manage your own Python installation • It installs locally; no admin privileges required • It’s lightweight and fits within your disk quota • Instructions: • wget https://repo if you want. • Our recommendations: • Install: conda/pip3 install jupyter • ???????????? Run on your computer • jupyter notebook • ???????????? Run on Princeton CS server • Pick any 4-digit number, say

Embeddings form a crucial part of modern deep-learning models, and we are excited to explain how they work. In the following section we will explain them through a toy example, but feel free to jump ahead the high-dimensional representation. It is useful because it is often computationally infeasible to work with data that has a large number of features. However, not all features might be equally important reasonable. The purpose of this toy-example is to illustrate how embeddings work, and we encourage you to try and construct your own example to understand it better. represented on the x-axis, and the feature

debugging 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 non-DGX users, see NVIDIA ® GPU Cloud ™ (NGC) container registry installation documentation based on your platform. ‣ Ensure that you have access and can log in to the NGC container registry. Refer to NGC 3. Running PyTorch Before you begin Before you can run an NGC deep learning framework container, your Docker ® environment must support NVIDIA GPUs. To run a container, issue the appropriate command

linear regression. These exercises have been extensively tested with Matlab, but they should also work in Octave, which has been called a “free version of Matlab”. If you are using Octave, be sure to install the usual x0 = 1, so x ∈ R2 ). If you’re using Mat- lab/Octave, run the following commands to plot your training set (and label the axes): figure % open a new f i g u r e window plot (x , y , ’ o ’ ) you will need to remember that the age values from your training data are actually in the second column of x. This will be important when plotting your results later. We implement linear regression for

v/s -5.0)? If we can tolerate some loss of precision, can we use b-bits and save some space? Let us work on a scheme for going from this higher-precision domain (32-bits) to a quantized domain (b-bit values) Mapping from a high precision to a low precision domain. Visually inspecting figure 2-4, can you work out the formula for mapping a given floating-point value (x) to a quantized value (xq). Assume that given x. Logistics We just wanted to take a moment to state that in this book, we have chosen to work with Tensorflow 2.0 (TF) because it has exhaustive support for building and deploying efficient models

maintain the spacing and proportions shown here. Choose the appropriate lockup depending on your specific application. When sizing or scaling the wordmark or lockups, ensure a legible size When designing content for the overall PyTorch brand, leverage these core palettes. These colors work successfully for print and digital communications. When using digitally, please use the hex