Building Transformer Models with PyTorch 2.0 : NLP, computer vision, and speech processing with PyTorch and Hugging Face
Prem Timsina
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Optimizing AI and Machine Learning Solutions : Your ultimate guide to building high-impact ML/AI solutions
Mirza Rahim Baig
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Algorithms of Anxiety : Fear in the Digital Age
Anthony (Flinders University) Elliott
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Large Language Model-Based Solutions : How to Deliver Value with Cost-Effective Generative AI Applications
Shreyas Subramanian
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Maschinelles Lernen fur Dummies
Jorn Fischer
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Add to BasketModern Computer Vision with PyTorch : A practical roadmap from deep learning fundamentals to advanced applications and Generative AI
V Kishore Ayyadevara
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Google Machine Learning and Generative AI for Solutions Architects : ?Build efficient and scalable AI/ML solutions on Google Cloud
Kieran, O.C.D. Kavanagh
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Modern Graph Theory Algorithms with Python : Harness the power of graph algorithms and real-world network applications using Python
Colleen M. Farrelly
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Deep Learning for Time Series Cookbook : Use PyTorch and Python recipes for forecasting, classification, and anomaly detection
Vitor Cerqueira
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Active Machine Learning with Python : Refine and elevate data quality over quantity with active learning
Margaux Masson-Forsythe
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A Machine Learning Technique for Estimating Cuffless Blood Pressure, Utilizing Voice Marker
Vaishali Shamsingh Rajput
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Analyzing Thunderstorm Incidences with Machine Learning and Meteorological Data
Kanchan Bala
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Detecting and Countering False Information through Incremental Learning Methodology
Yashoda Narayanprasad Barve
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Secrets Of Machine Learning: How It Works And What It Means For You
Tom (-) Kohn
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Emerging ICT Technologies and Cybersecurity : From AI and ML to Other Futuristic Technologies
Kutub Thakur
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Machine Learning for Cyber-Physical Systems : Selected papers from the International Conference ML4CPS 2023
Oliver Niggemann
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Reinforcement Learning for Reconfigurable Intelligent Surfaces : Assisted Wireless Communication Systems
Alice Faisal
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MultiMedia Modeling : 30th International Conference, MMM 2024, Amsterdam, The Netherlands, January 29 – February 2, 2024, Proceedings, Part V
Stevan Rudinac
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Maschinelles Lernen fur die Ingenieurwissenschaften : Einfuhrung in physikalisch informierte, erklarbare Lernverfahren fur KI in technischen Anwendungen
Marcus J Neuer
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Reinforcement Learning : Aktuelle Ansatze verstehen – mit Beispielen in Java und Greenfoot
Uwe Lorenz
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Gaussian Processes for Machine Learning
Carl Edward (University of Cambridge) Rasmussen
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Advances in Financial Machine Learning
Marcos Lopez de Prado
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