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Deep learning : moving toward artificial intelligence with neural networks and machine learning /
Published 2015CONNECT
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Deep learning : a practitioner's approach /
Published 2017Table of Contents: “…A review of machine learning -- Foundations of neural networks and deep learning -- Fundamentals of deep networks -- Major architecture of deep networks -- Building deep networks -- Tuning deep networks -- Tuning specific deep network architectures -- Vectorization -- Using deep learning and DL4J on Spark -- What is artificial intelligence? …”
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Deep learning : from big data to artificial intelligence with R /
Published 2023Table of Contents: “…Front Matter -- From Big Data to Deep Learning -- Processing of Large Volumes of Data -- Reminders of Machine Learning -- Natural Language Processing -- Social Network Analysis -- Handwriting Recognition -- Deep Learning -- Deep Learning for Computer Vision -- Deep Learning for Natural Language Processing -- Artificial Intelligence -- Conclusion -- Annotated Bibliography -- Index…”
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Deep learning : computer vision for beginners using PyTorch.
Published 2023Subjects: “…Deep learning (Machine learning)…”
CONNECT
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Deep learning : crash course 2023.
Published 2023Subjects: “…Deep learning (Machine learning)…”
CONNECT
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Deep learning : fundamentals, methods and applications /
Published 2016Table of Contents: “…Deep Learning using Unconventional Paradigms / Sreenivas Sremath Tirumala -- Deep Learning in Open Source Learning Streams / Thomas Kjaergaard -- Optimal Outcomes at School : A Focus on Theoretical Tenets for Consideration / Huy P. …”
CONNECT
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Deep Learning.
Published 2017Table of Contents: “…Cover ; Preface; Table of Contents ; Module 1; Chapter 1: Deep Learning Overview; Transition of AI; Things dividing a machine and human; AI and deep learning; Summary; Chapter 2: Algorithms for Machine Learning -- Preparing for Deep Learning; Getting started; The need for training in machine learning; Supervised and unsupervised learning; Machine learning application flow; Theories and algorithms of neural networks; Summary; Chapter 3: Deep Belief Nets and Stacked Denoising Autoencoders; Neural networks fall; Neural networks' revenge; Deep learning algorithms; Summary.…”
CONNECT
Electronic eBook