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In der Parallelwelt der ITler wurde das Tool und Ökosystem Apache Hadoop quasi mit Big Data beinahe synonym gesetzt. GPT-3 wurde mit mehr als 100 Milliarden Wörter trainiert, das parametrisierte Machine Learning Modell selbst wiegt 800 GB (quasi nur die Neuronen!) Neben SupervisedLearning kam auch Reinforcement Learning zum Einsatz.
Some of the most notable technologies include: Hadoop An open-source framework that allows for distributed storage and processing of large datasets across clusters of computers. It is built on the Hadoop Distributed File System (HDFS) and utilises MapReduce for data processing. Once data is collected, it needs to be stored efficiently.
Machine Learning with Python Machine Learning is a vital component of Data Science, enabling systems to learn from data and make predictions. Python’s rich ecosystem offers several libraries, such as Scikit-learn and TensorFlow, which simplify the implementation of ML algorithms.
These techniques span different types of learning and provide powerful tools to solve complex real-world problems. SupervisedLearningSupervisedlearning is one of the most common types of Machine Learning, where the algorithm is trained using labelled data.
Today, machine learning has evolved to the point that engineers need to know applied mathematics, computer programming, statistical methods, probability concepts, data structure and other computer science fundamentals, and big data tools such as Hadoop and Hive. Python is the most common programming language used in machine learning.
Explore Machine Learning with Python: Become familiar with prominent Python artificial intelligence libraries such as sci-kit-learn and TensorFlow. Begin by employing algorithms for supervisedlearning such as linear regression , logistic regression, decision trees, and support vector machines.
Distributed File Systems Distributed file systems (DFSs), like Hadoop HDFS , are essential for storing and managing large amounts of unstructured data that AI systems need for analysis and training models. They are ideal for big data analytics and ML, thus allowing complete exploration of data and business intelligence.
Machine Learning: Subset of AI that enables systems to learn from data without being explicitly programmed. SupervisedLearning: Learning from labeled data to make predictions or decisions. Unsupervised Learning: Finding patterns or insights from unlabeled data.
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