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

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 depicts the sliding window of size 5, the hidden target word, model inputs, and the label for a given sample text in the CBOW task. 7 GloVe - https://nlp.stanford.edu/projects/glove 6 Mikolov, Tomas, Kai depicts the sliding window of size 5, the hidden target word, model inputs, and the label for a given sample text in the Skipgram task. Let’s get to solving the CBOW task8 step by step and train an embedding

decision boundary to differentiating positive data samples from negative data samples. Given a test data sample, we will make a more confident decision if its margin (with respect to the decision hy- perplane) 1/∥ω∥ maximized, while the resulting dashed lines satisfy the following condition: for each training sample (x(i), y(i)), ωT x(i) +b ≥ 1 if y(i) = 1, and ωT x(i) + b ≤ 1 if y(i) = −1. This is a quadratic programming 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

labels from negative labels We make more confident decision if larger margin is given, i.e., the data sample is further away from the hyperplane There exist a infinite number of hyperplanes, but which one 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 December 28, 2021 40 / 82 Feature Mapping Consider the following binary classification problem Each sample is represented by a single feature x No linear separator exists for this data Feng Li (SDU) SVM

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 blue region. In other words, it doesn't learn from the past trials. Wouldn't it be nice if we could sample more in the favorable regions? The next search strategy does exactly that! Bayesian Optimization evaluated on the target dataset and their performance is recorded. The best performing model in a random sample of models from is selected for mutation. After the mutation, the child's performance is recorded

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 learning to optimize the model  activation represent the activation function. Let us consider sample input and weights as below and try to find the result:  input as 2 x 2 matrix [ [1, 2], [3, 4] sample_weight_mode=None, weighted_metrics=None, target_tensors=None) The important arguments are as follows:  loss function  Optimizer  metrics A sample code to

tobytes()) return compressed_w To demonstrate the effect of sparsity on compression, we create a sample 2D weight matrix with randomly initialized float values. We also define a sparsity_rate variable improvements, we feel that sparsity will be one of the leading compression techniques used for model efficiency in the coming time. Clustering is also a very powerful compression technique, yet implementing

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

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