Deep Learning Crash Course

Häftad, Engelska, 2026

AvGiovanni Volpe,Benjamin Midtvedt,Jesus Pineda,Jesús Pineda,Henrik Klein Moberg,Harshith Bachimanchi,Joana B. Pereira,Carlo Manzo

699 kr

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Deep Learning Crash Course goes beyond the basics of machine learning to delve into modern techniques and applications of great interest right now, and whose popularity will only grow in the future. The book covers topics such as generative models (the technology behind deep fakes), self-supervised learning, attention mechanisms (the tech behind ChatGPT), graph neural networks (the tech behind AlphaFold), and deep reinforcement learning (the tech behind AlphaGo). This book bridges the gap between theory and practice, helping readers gain the confidence to apply deep learning in their work.

Produktinformation

Utgivningsdatum2026-01-06
Mått230 x 20 x 180 mm
Vikt1 085 g
FormatHäftad
SpråkEngelska
Antal sidor672
FörlagNo Starch Press,US
ISBN9781718503922

Tillhör följande kategorier

Systemvetenskap och AI inom Data och IT
Programmeringsböcker inom Data och IT

Giovanni Volpe, head of the Soft Matter Lab at the University of Gothenburg and recipient of the Göran Gustafsson Prize in Physics, has published extensively on deep learning and physics and developed key software packages including DeepTrack, Deeplay, and BRAPH. Benjamin Midtvedt and Jesús Pineda are core developers of DeepTrack and Deeplay. Henrik Klein Moberg and Harshith Bachimanchi apply AI to nanoscience and holographic microscopy. Joana B. Pereira, head of the Brain Connectomics Lab at the Karolinska Institute, organizes the annual conference Emerging Topics in Artificial Intelligence. Carlo Manzo, head of the Quantitative Bioimaging Lab at the University of Vic, is the founder of the Anomalous Diffusion Challenge.

IntroductionChapter 1: Building and Training Your First Neural NetworkChapter 2: Capturing Trends and Recognizing Patterns with Dense Neural NetworksChapter 3: Processing Images with Convolutional Neural NetworksChapter 4: Enhancing, Generating, and Analyzing Data with AutoencodersChapter 5: Segmenting and Analyzing Images with U-NetsChapter 6: Training Neural Networks with Self-Supervised LearningChapter 7: Processing Time Series and Language with Recurrent Neural NetworksChapter 8: Processing Language and Classifying Images with Attention and TransformersChapter 9: Creating and Transforming Images with Generative Adversarial NetworksChapter 10: Implementing Generative AI with Diffusion ModelsChapter 11: Modeling Molecules and Complex Systems with Graph Neural NetworksChapter 12: Continuously Improving Performance with Active LearningChapter 13: Mastering Decision-Making with Deep Reinforcement LearningChapter 14: Predicting Chaos with Reservoir ComputingConclusionIndex

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