As data grows exponentially day-by-day, extracting information becomes a tedious activity in itself. Technologies like Hadoop are trying to address some of the concerns, while Solr provides high-speed faceted search. Bringing these two technologies together is helping organizations resolve the problem of information extraction from Big Data by providing excellent distributed faceted search capabilities. Scaling Big Data with Hadoop and Solr is a step-by-step guide that helps you build high performance enterprise search engines while scaling data. Starting with the basics of Apache Hadoop and Solr, this book then dives into advanced topics of optimizing search with some interesting real-world use cases and sample Java code.
Data preprocessing is a critical procedure in many real world machine learning and AI problem. Using weather forecast as example, various data preprocessing such as data normalization, scaling and labeling are needed before the time-series weather information can be used for network training and testing. Use the time series weather data of Seattle (weather.csv) provided in this workshop as the time-series raw data for data preprocessing: Describe and explain the nature of data in each attribute of the time series records. Discuss what kind of data preprocessing methods are needed for each attribute. How about missing record and incorrect data, how can we fix such problems. Write Python program to implement the data processing method. Hint: The normal range and condition of each weather attribute are: Air Pressure 900 - 1200 Precipitation 0 - 300 Temperature -50 - 50 Max >= Min Temp Wind Speed (Grade) 0 - 10 Wind Direction 0 - 360
(x, y), (x_val, y_val) = datasets.mnist.load_data() x = tf.convert_to_tensor(x, dtype=tf.float32) / 255.
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