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

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

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

matplotlib.pyplot as plt For this tutorial, air quality data about ??2 is used, made available by openaq and using the py-openaq package. The air_quality_no2.csv data set provides ??2 values for the measurement respectively Paris, Antwerp and London. In [3]: air_quality = pd.read_csv("data/air_quality_no2.csv", ...: index_col=0, parse_dates=True) ...: In [4]: air_quality.head() Out[4]: station_antwerp station_paris respectively. How to create plots in pandas? I want a quick visual check of the data. In [5]: air_quality.plot() Out[5]: With a DataFrame, pandas

matplotlib.pyplot as plt For this tutorial, air quality data about ??2 is used, made available by openaq and using the py-openaq package. The air_quality_no2.csv data set provides ??2 values for the measurement respectively Paris, Antwerp and London. In [3]: air_quality = pd.read_csv("data/air_quality_no2.csv", ...: index_col=0, parse_dates=True) ...: In [4]: air_quality.head() Out[4]: station_antwerp station_paris Release 1.0.4 How to create plots in pandas? I want a quick visual check of the data. In [5]: air_quality.plot() Out[5]: With a DataFrame, pandas

matplotlib.pyplot as plt For this tutorial, air quality data about ??2 is used, made available by openaq and using the py-openaq package. The air_quality_no2.csv data set provides ??2 values for the measurement respectively Paris, Antwerp and London. In [3]: air_quality = pd.read_csv("data/air_quality_no2.csv", ...: index_col=0, parse_dates=True) ...: In [4]: air_quality.head() Out[4]: station_antwerp station_paris Release 1.0.3 How to create plots in pandas? I want a quick visual check of the data. In [5]: air_quality.plot() Out[5]: With a DataFrame, pandas

matplotlib.pyplot as plt For this tutorial, air quality data about ??2 is used, made available by openaq and using the py-openaq package. The air_quality_no2.csv data set provides ??2 values for the measurement respectively Paris, Antwerp and London. In [3]: air_quality = pd.read_csv("data/air_quality_no2.csv", ...: index_col=0, parse_dates=True) ...: In [4]: air_quality.head() Out[4]: station_antwerp station_paris Release 1.1.1 How to create plots in pandas? I want a quick visual check of the data. In [5]: air_quality.plot() Out[5]: With a DataFrame, pandas

matplotlib.pyplot as plt For this tutorial, air quality data about ??2 is used, made available by openaq and using the py-openaq package. The air_quality_no2.csv data set provides ??2 values for the measurement respectively Paris, Antwerp and London. In [3]: air_quality = pd.read_csv("data/air_quality_no2.csv", ...: index_col=0, parse_dates=True) ...: In [4]: air_quality.head() Out[4]: station_antwerp station_paris Release 1.1.0 How to create plots in pandas? I want a quick visual check of the data. In [5]: air_quality.plot() Out[5]: With a DataFrame, pandas