rapid growth. We will establish our motivation behind seeking efficiency in deep learning models. We will also introduce core areas of efficiency techniques (compression techniques, learning techniques, automation Our hope is that even if 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 models is rate-limited by their efficiency. While efficiency can be an overloaded term, let us investigate two primary aspects: Training Efficiency Training Efficiency involves benchmarking the model

bring significant efficiency gains during the training phase, which is the focus of this chapter. We start this chapter with an introduction to sample efficiency and label efficiency, the two criteria Our journey of learning techniques also continues in the later chapters. Learning Techniques and Efficiency Data Augmentation and Distillation are widely different learning techniques. While data augmentation breadth as efficiency? To answer this question, let’s break down the two prominent ways to benchmark the model in the training phase namely sample efficiency and label efficiency. Sample Efficiency Sample

quality with a small number of labels. As we described in chapter 3’s ‘Learning Techniques and Efficiency’ section, labeling of training data is an expensive undertaking. Factoring in the costs of training a new task: 1. Data Efficiency: It relies heavily on labeled data, and hence achieving a high performance on a new task requires a large number of labels. 2. Compute Efficiency: Training for new tasks Model reuse by itself also is a powerful attribute of this scheme, and lends itself to compute efficiency since only have to train the model on a small number of examples, saving training time compute

shorter.” Blaise Pascal In the last chapter, we discussed a few ideas to improve the deep learning efficiency. Now, we will elaborate on one of those ideas, the compression techniques. Compression techniques Tensorflow and Tensorflow Lite. An Overview of Compression One of the simplest approaches towards efficiency is compression to reduce data size. For the longest time in the history of computing, scientists representation of one or more layers in a neural network with a possible quality trade off. The efficiency goals could be the optimization of the model with respect to one or more of the footprint metrics

evaluate the prediction of the algorithm / Model (once the machine learn) and to cross check the efficiency of the learning process.  Compile the model: Compile the algorithm / model, so that, it begin by understanding the model evaluation. Model Evaluation Evaluation is a process during development of the model to check whether the model is best fit for the given problem and corresponding data

improve model deployability by proposing novel ways to reduce model footprint and improve inference efficiency while preserving the problem solving capabilities of their giant counterparts. In the first chapter training of the attention model will follow right after. Finally, we will compare their training efficiency and quality metrics. As always, the code is available as a Jupyter notebook here for you to experiment

This difference means that preprocessing the inputs will significantly increase gradient descent’s efficiency. In your program, scale both types of inputs by their standard deviations and set their means to

illinois.edu/~angelia/L13_constrained_gradient.pdf) ... Existing generic QP solvers is of low efficiency, especially in face of a large training set Feng Li (SDU) SVM December 28, 2021 15 / 82 Convex

set method, gradient projection method. Unfortunately, the existing generic QP solvers is of low efficiency, especially in face of a large training set. 2.2 Preliminary Knowledge of Convex Optimization

Founder (Slack) We have talked about a variety of 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