or do not necessarily care about the loss in quality. Figure 2-2: On the left is a high quality image of a cat. The cat on the right is a lower quality compressed image. Source Both the cat images for loss in quality. The JPEG and MP3 formats are able to achieve a 10-11x compression without any perceptible loss in quality. However, further compression might lead to degradation in quality. In our case prediction latency, RAM consumption and the quality metrics, such as accuracy, F1, precision and recall as shown in table 2-1. Footprint Metrics Quality Metrics ● Model Size ● Inference Latency on

optimize for). Naturally, there is a trade-off between the two metrics. It is likely that higher quality models are deeper, hence will have a higher inference latency. Figure 1-4: Pareto Optimal Models In case we find models where we cannot get a better quality while holding the latency constant, or we cannot get better latency while holding quality constant, we call just models pareto-optimal, and the deeper, let’s visualize two sets of closely connected metrics that we care about. First, we have quality metrics like accuracy, precision, recall, F1, AUC, etc. Then we have footprint metrics like model

couple of windows and a balcony. Similarly, to gain orders of magnitude in terms of footprint or quality, we should consider employing suitable efficient architectures. The progress of deep learning is temporal data. These breakthroughs contributed to bigger and bigger models. Although they improved the quality of the solutions, the bigger models posed deployment challenges. What good is a model that cannot since it is a binary classification task. An important caveat is that the model quality naturally depends on the quality of the embedding table. In the petting zoo example, we manually created the embeddings

recap, learning techniques can help us meet our model quality goals. Techniques like distillation and data augmentation improve the model quality, without increasing the footprint of the model (size, latency latency, etc). And as we have described earlier, some of these improved quality metrics can be traded off for a smaller footprint as desired. Continuing with the theme of chapter 3, we will start this natural language models like BERT. Self-Supervised learning helps models to quickly achieve impressive quality with a small number of labels. As we described in chapter 3’s ‘Learning Techniques and Efficiency’

you'll go.” ― Dr. Seuss Model quality is an important benchmark to evaluate the performance of a deep learning model. A language translation application that uses a low quality model would struggle with consumer employs a high quality model with a reasonable translation accuracy would garner better consumer support. In this chapter, our focus will be on the techniques that enable us to achieve our quality goals. High High quality models have an additional benefit in footprint constrained environments like mobile and edge devices where they provide the flexibility to trade off some quality for smaller footprints. In

which 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 are not necessarily the least important. In practice, unstructured sparsity achieves better model quality metrics than structured sparsity, for the same number of weights pruned. Phew! It feels like we the denominator in the compression ratio expression increases. However, with the increase in , the quality of the representation typically improves as each centroid has a smaller range to cover, therefore

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 available techniques. It (a model) by turning the knobs (the hyperparameters) until we are satisfied with the sound (model quality and footprint) that each string produces. Unlike the guitar which has a few knobs, the hyperparameter the chosen values for the hyperparameters. The objective refers to the metric to score the trial quality. Each trial can use a maximum of max_epochs resources and tuner = kt.Hyperband( hypermodel=build_hp_model

s More Results 70 71 72 Summary 73 • End-to-end & fully scalable • New SPMC layer • High-quality & fast speed ???????????????????????? ???????????? ????????????0 ???????????? ME ??????????? framework for deblurring. Without assumptions on blur models. Visually and quantitatively high-quality results with fewer parameters. Summary 119 Thanks

218/notebooks/money_predict.ipynb 将文本转化为 生活化语音 47 种语音 24 种语言 低延迟、实时 全托管 Polly: 生活化的语音服务 Voice Quality & Pronunciation 1. 自动化、精准的文本处理 2. 智能化的且易于理解 3. 将语义加入文本当中 4. 定制化的发音 文章、博客 训练材料 Chatbots (Lex)

multimodal models are pretrained on large-scale multilingual and multimodal data and post-trained on quality data for aligning to human preferences. Qwen is capable of natural language understanding, text generation