其中等变性、不变性的含义请见【Deep Learning:Foundations and Concepts】归纳偏好、权重衰减、共享参数、残差链接、模型平均,局部性即邻近像素的颜色可能十分相似。层次性可以理解为,假设我们要识别人脸,那么就先要识别眼睛、鼻子嘴巴;而要识别眼睛,又要识别虹膜;要识别虹膜,又要识别其边缘。如此的不断嵌套,称为层
We would also like to thank our editor Paul Drougas and many others at Springer, as well as the copy editor Jonathan Webley, for their support during the production of the book.\nWe would like to say a special thank-you to Markus Svens茅n, who provided immense helpBlanka N. Horvath...
【Deep Learning:Foundations and Concepts】概率论、信息论、拉格朗日乘子法 W11H08Z “莫听穿林打叶声,何妨吟啸且徐行” 来自专栏 · Notes of DLFC & PRML 几乎所有的机器学习应用都需要解决不确定性问题,不确定性主要可以分为两种 epistemic/systematic uncertainty:这是由于数据集大小有限导致的 aleatoric/...
Part 1 focuses on introducing the main concepts of deep learning.Part 2provides historical background and delves into the training procedures, algorithms and practical tricks that are used in training for deep learning.Part 3covers sequence learning, including recurrent neural networks, LSTMs, and en...
We conclude that deep learning concepts show much potential as a tool for evolutionary art, and future results will improve as deep CNN models are better understood.doi:10.1007/978-3-030-16667-0_1Fazle TanjilBrian J. RossSpringer, Cham
The motivation is in using the capacity of modern deep learning techniques to automatically learn musical styles from arbitrary musical corpora and then to generate musical samples from the estimated distribution, with some degree of control over the generation. This article provides a survey of music...
In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achieving ou
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Deep learning algorithms have been utilized to achieve enhanced performance in pattern-recognition tasks. The ability to learn complex patterns in data has tremendous implications in immunogenomics. T-cell receptor (TCR) sequencing assesses the diversity of the adaptive immune system and allows for mode...
DEEP LEARNING In hierarchicalFeature Learning, we extract multiple layers of non-linear features and pass them to a classifier that combines all the features to make predictions. We are interested in stacking such very deep hierarchies of non-linear features because we cannot learn complex features ...