similar support on ARM processors as well as on specialized DSPs like the Qualcomm Hexagon. We started out this section with two main objectives. The first one was to reduce the model size which is fulfilled model. Figure 2-14 shows the accuracy plot of the model on the training and the test datasets. We started out with a goal to create a smaller model without compromising the accuracy which we have achieved It wasn’t a surprise that the idea of compression crept into the deep learning field as well. We started this chapter with a gentle introduction of compression using huffman coding and jpeg compression

been the primary focus of sparsity research. However, in the last few years, some researchers have started to explore activation sparsity as well. Activation sparsity involves sparsifying activation maps (approx.) inference performance gains for ResNets and MobileNets. More recently, the researchers have started to combine these two forms to achieve both accuracy and latency gains. Weight Sharing using Clustering

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 import tensorflow_datasets as tfds from tensorflow latencies, quality and footprint metrics between regular and depthwise separable convolutions. We started out with a goal to create a mobile friendly model to predict segmentation masks for pet images. We

torch; print(torch.cuda.is_available())' Useful Links Miniconda Documentation PyTorch: Getting Started Install TensorFlow

learning and neural network framework. This tutorial is intended to make you comfortable in getting started with the Keras framework concepts. Prerequisites Before proceeding with the various types

registry: ‣ 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 Ensure 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