but reaches that point in fewer epochs, hence needing fewer samples. Distillation is a learning technique which has been shown to reduce the number of samples that a model needs to see to converge to the let’s go through the different text transformations with code examples. Synonym Replacement is a technique to replace words with their synonyms. It is a simple idea to augment the dataset without compromising candidates) >> ['we', 'enjoyed', 'our', 'short', 'holiday', 'in', 'mexico'] Random Insertion technique inserts a word at a random position in the sentence. The inserted word, typically, is a synonym

for the model to learn anything. You should treat label smoothing as yet another regularization technique. In fact this paper17 goes into details about when label smoothing helps. The original Inception sequences are likely easier), etc. For example Li et al.21 proposed training GPT-3 like models with a technique called sequence length warmup where the model training starts with the input truncated to a limit training progresses. The authors reported a 10x data saving and 17x training time savings with this technique. Pacing the training example difficulty Next, we need a pacing function to tune the difficulty

subjective to the specific problem. In this chapter, we introduce Quantization, a model compression technique that addresses both these issues. We’ll start with a gentle introduction to the idea of compression apples this way. We can call this lossy compression because we lost the odd parts. The choice of the technique depends on several factors like customer preference, consumption delay, or resource availability the desired tradeoff goals. In the next section we introduce Quantization, a popular compression technique which is also used in various fields of computer science in addition to deep learning. Quantization

these techniques together! Model Compression Using Sparsity Sparsity or Pruning refers to the technique of removing (pruning) weights during the model training to achieve smaller models. Such models are saliency score metric is using the magnitude of the weights, which has become a popular pruning technique because of its simplicity and effectiveness. Later on in this chapter, we have a project that relies well-trained neural net by as much as 8x without a drop in classification accuracy. Yet another technique could be momentum based pruning2 which uses the magnitude of the momentum of the weights to evaluate

more efficient model by trimming the number of parameters if needed. An example of a learning technique is Distillation (see Figure 1-10), which helps a smaller model (student) that can be deployed, to to a 40% smaller model (DistillBERT), while retaining 97% of the performance. Another learning technique is Data Augmentation. It is a nifty way of addressing the scarcity of labeled data during training resources, so they have to be carefully used. Automated Hyper-Param Optimization (HPO) is one such technique that can be used to replace / supplement manual tweaking of hyper-parameters like learning rate

has two boolean valued parameters: quantization and clustering. A $$True$$ value means that the technique is turned on and a $$False$$ value means it is turned off. This search space1 has four possible Bayesian Optimization Bayesian Optimization Search (BOS) is a sequential model based search technique where the search is guided by actively estimating the value of the objective function at different objective function looks and plans the next trials based on that knowledge, it is a model-based technique. Moreover, since the selection of trials depends on the results of the past trials, this method

high costs associated with manual embeddings. One example of an automated embedding generation technique is the word2vec family of algorithms6 (apart from others like GloVe7) which can learn embeddings softmax calculation. In the real world, as an efficient approximation, we use the Negative Sampling technique so that we only look at the output probability of the label class (which should be closer to 1.0) reduction, it still may not be suitable for a range of mobile and edge devices. Do you recall a technique that can reduce it further? Yes, Quantization! We will leave it for you as an exercise. Tell us

情况. 常用不平衡处理方法有采样和代价敏感学习 采样欠采样、过采样和综合采样的方法 不平衡数据的处理 7 SMOTE(Synthetic Minority Over-sampling Technique)算法是过采样 中比较常用的一种。算法的思想是合成新的少数类样本，而不是简单地复 制样本。算法过程如图： 不平衡数据的处理 （a）原始样本 （b）选定少类样本 （c）找到靠近?的 清华大学出版社,2019. [8] CHAWLA N V, BOWYER K W, HALL L O, et al. SMOTE: Synthetic Minority Over-sampling Technique[J]. Journal of Artificial Intelligence Research, 2002, 16: 321–357. 33 谢 谢！

lower dimensional subspace which captures the “essence” of the data. The motivation behind this technique is that although the data may appear high dimensional, there may only be a small number of degrees

movie I've seen in years. If y 标签： 1 review: Lars Von Trier is never backward in trying out new technique 15.1.2 预处理数据集 将每个单词作为一个词元，过滤掉出现不到5次的单词，我们从训练数据集中创建一个词表。 train_tokens = d2l.tokenize(train_data[0]