Packt Publishing - ebooki
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Machine Learning Engineering with Python. Manage the lifecycle of machine learning models using MLOps with practical examples - Second Edition
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Deep Learning with TensorFlow and Keras. Build and deploy supervised, unsupervised, deep, and reinforcement learning models - Third Edition
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Deep Learning for Time Series Cookbook. Use PyTorch and Python recipes for forecasting, classification, and anomaly detection
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Forecasting Time Series Data with Prophet. Build, improve, and optimize time series forecasting models using Meta's advanced forecasting tool - Second Edition
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Principles of Data Science. A beginner's guide to essential math and coding skills for data fluency and machine learning - Third Edition
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Python Deep Learning. Understand how deep neural networks work and apply them to real-world tasks - Third Edition
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Et al. Because not all research deserves a Nobel Prize
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Time Series Indexing. Implement iSAX in Python to index time series with confidence
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Machine Learning with R. Learn techniques for building and improving machine learning models, from data preparation to model tuning, evaluation, and working with big data - Fourth Edition
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Data Augmentation with Python. Enhance deep learning accuracy with data augmentation methods for image, text, audio, and tabular data
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Computer Vision on AWS. Build and deploy real-world CV solutions with Amazon Rekognition, Lookout for Vision, and SageMaker
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Applied Geospatial Data Science with Python. Leverage geospatial data analysis and modeling to find unique solutions to environmental problems
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Hands-On Simulation Modeling with Python. Develop simulation models for improved efficiency and precision in the decision-making process - Second Edition
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Large Scale Machine Learning with Python. Click here to enter text
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Mastering Articulate Storyline. Learn how to push Articulate Storyline to its limits and create breath taking stories by deepening your understanding of the product's capabilities!
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Machine Learning with R. R gives you access to the cutting-edge software you need to prepare data for machine learning. No previous knowledge required – this book will take you methodically through every stage of applying machine learning