Chapter 4 - Efficient Architectures “Any sufficiently advanced technology is indistinguishable from magic.” — Arthur C. Clarke, “Hazards of Prophecy: The Failure of Imagination” (1962) “Any technology gain orders of magnitude in terms of footprint or quality, we should consider employing suitable efficient architectures. The progress of deep learning is characterized by the phases of architectural breakthroughs biggest architectural breakthroughs in the field of neural networks. It introduced the idea of stacking layers to learn complex relationships. Convolutional Neural Nets (CNNs) were another important breakthrough

Real-Time Unified Data Layers: A New Era for Scalable Analytics, Search, and AI v 1.1Table of Contents Introduction 1. The Interconnection of Analytics, Search, and AI 2. What is a Real-Time Unified interconnected in how they process, interpret, and extract value from data. Together, they enable efficient data processing, enhance decision-making, and improve user experiences: Analytics transforms raw architecture teams must rethink traditional data infrastructures. The future lies in Real-Time Unified Data Layers—platforms that seamlessly support analytics, search, and AI workloads at scale. These systems break

techniques in the last few chapters to improve efficiency and boost the quality of deep learning models. These techniques are just a small subset of the available techniques. It is often tedious to decide momentum are geared towards model convergence. However, they all work in conjunction to produce better models faster. Let's say that we are optimizing the validation loss, , for a given dataset on a model target classes. import random import tensorflow as tf import numpy as np from tensorflow.keras import layers, losses, optimizers X = tf.random.uniform((20, 5)) Y = tf.squeeze( tf.one_hot(tf.random.uniform((20

Chapter 1 - Introduction to Efficient Deep Learning Welcome to the book! This chapter is a preview of what to expect in the book. We start off by providing an overview of the state of deep learning, its efficiency in deep learning models. We will also introduce core areas of efficiency techniques (compression techniques, learning techniques, automation, efficient models & layers, infrastructure). Our hope you just read this chapter, you would be able to appreciate why we need efficiency in deep learning models today, how to think about it in terms of metrics that you care about, and finally the tools at your

chapter, our focus will be on the techniques that enable us to achieve our quality goals. High quality models have an additional benefit in footprint constrained environments like mobile and edge devices where with samples and labels, distillation transfers knowledge from a large model or ensemble of models to smaller models. The obvious question at this point is: why are we talking about them in the same breadth recall or other performance metrics). We designate a new model training setup to be more sample efficient, if it achieves similar or better performance with fewer data samples when compared to the baseline

parameters. However, this approach has two drawbacks. First, it is hard to determine the parameters or layers that can be removed without significantly impacting the performance. It requires many trials and lead to degradation in quality. In our case, we are concerned about compressing the deep learning models. What do we really mean by compressing though? As mentioned in chapter 1, we can break down the metrics footprint. In the case of deep learning models, the model quality is often correlated with the number of layers, and the number of parameters (assuming that the models are well-tuned). If we naively reduce

Leveraging the power of C++ for efficient machine learning on embedded devices Adrian Stanciu adrian.stanciu.pub@gmail.com CppCon, 2023 1 / 50About me ◮ I am a software engineer from Romania ◮ I have data to make predictions 11 / 50Neural network (NN) 13 / 50Convolutional neural network (CNN) ◮ Efficient in image classification ◮ A convolutional layer can apply filters to detect: ◮ Edges ◮ Shapes ◮ 50Conclusions ◮ Code isn’t enough... data matters ◮ More diverse data leads to better models ◮ Building accurate models is an expert job ◮ Running on-device inference is straightforward ◮ Running on-device

with an eye towards conceptual understanding as well as practically 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 ensures the decoded value deviates less from the original value and can help improve the quality of our models. Did we get you excited yet? Let’s learn about these techniques together! Model Compression Using of removing (pruning) weights during the model training to achieve smaller models. Such models are called sparse or pruned models. The simplest form of pruning is to zero out a certain, say p, percentage

learning which has been instrumental in the success of natural language models like BERT. Self-Supervised learning helps models to quickly achieve impressive quality with a small number of labels. As you can achieve while retaining the same labeling costs i.e., training data-efficient (specifically, label efficient) models. We will describe the general principles of Self-Supervised learning which are tasks requires new models to be trained from scratch. For models that share the same domain, it is likely that the first few layers learn similar features. Hence training new models from scratch for these