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 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 applicable 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

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

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

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 Suitable’. Since in this scenario we have only a few examples it is easy to manually assign them a label identifying which class a given animal belongs to. Puppies and cats would make instant favorites will find it cute. Refer to Table 4-2 for our assigned labels. Animal Embedding (cute, dangerous) Label: (suitable for the petting zoo)? dog (0.85, 0.05) Suitable cat (0.95, 0.05) Suitable snake (0.01

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 ’ ) ; ylabel ( ’ Height in 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

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 size IMG_SIZE = 264 def resize_image(image, label): image = tf.image.resize(image, [IMG_SIZE, IMG_SIZE]) image = tf.cast(image, tf.uint8) return image, label train_ds = train_ds.map(resize_image) val_ds

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 the Keras 65 training data with shape, (number_sample, 28, 28) and its digit label with shape, (number_samples, ). Second tuple, (x_test, y_test) represent test data with same shape which does the evaluation of the model. It has three main arguments,  Test data  Test data label  verbose - true or false Let us evaluate the model, which we created in the previous chapter

classify a given point x0 ∈ Rn according to sign(ωT x + b). Specifically, given a point x0 ∈ Rn, its label y is defined as y0 = sign(ωT x0 + b), i.e. y0 = � 1, ωT x0 + b ≥ 0 −1, otherwise (2) Given any 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

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