通过TensorFlow (或 Theano 、 CNTK ), Keras 可以在 CPU 和 GPU 上无缝运行。在 CPU 上运行时,TensorFlow 本身封装了一个低层次的张量运算库,叫作 Eigen ;在 GPU上运行时,TensorFlow封装了一个高度优化的深度学习运算库,叫作 NVIDIA CUDA 深度神经网络库( cuDNN )。 流程大致:...
Deep Learning with Python - Francois Chollet 下载积分:1000 内容提示: 文档格式:PDF | 页数:542 | 浏览次数:294 | 上传日期:2019-01-03 15:23:56 | 文档星级: 阅读了该文档的用户还阅读了这些文档 231 p. Writing the Heart of Your Story - C. S. Lakin 291 p. Shoot Your Novel - C. S...
Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet,this book builds your understanding through intuitive explanations and practical examples. You’ll explore challeng...
Deep Learning Toolbox Importer for TensorFlow-Keras Models.rar matlab的Deep Learning Toolbox Importer for TensorFlow-Keras Models 支持包,可以在matlab中使用tensorflow的模型 上传者:qq_39545674时间:2020-03-26 Deep-Learning-with-Keras-master.zip
本书由Keras之父、现任Google人工智能研究员的弗朗索瓦•肖莱(François Chollet)执笔,详尽介绍了用Python和Keras进行深度学习的探索实践,包括计算机视觉、自然语言处理、产生式模型等应用。书中包含30多个代码示例,步骤讲解详细透彻。由于本书立足于人工智能的可达性和大众化,读者无须具备机器学习相关背景知识即可展开...
作者:Francois Chollet 出版社:Manning 发行时间:December 4th 2017 来源:下载的 pdf 版本 Goodreads:4.7 (148 Ratings) 豆瓣:9.4(50人评价) Artificial intelligence: Artificial intelligence was born in the 1950s, when a handful of pioneers from the nascent field of computer science started asking whether...
Jupyter notebooks for the code samples of the book "Deep Learning with Python" - fchollet/deep-learning-with-python-notebooks
Deep Learning with Keras-2017.pdf Deep Learning with Python A Hands-on Introduction-2017.pdf Deep Learning With Python-Develop Deep Learning Models on Theano and TensorFlow Using Keras-2017.pdf Deep Learning with Python-Francois_Chollet-En-2018.pdf Deep Learning with Python-Francois_Chollet-中文-Py...
Deep Learning with PyTorch 作者:Eli Stevens/Luca Antiga 出版社:Manning Publications 出版年:2020-6-9 页数:450 定价:USD 49.99 装帧:Paperback ISBN:9781617295263 豆瓣评分 7.6 68人评价 5星 33.8% 4星 33.8% 3星 25.0% 2星 4.4% 1星 2.9% 评价:...
Spiking neural networks (SNNs) incorporating biologically plausible neurons hold great promise because of their unique temporal dynamics and energy efficiency. However, SNNs have developed separately from artificial neural networks (ANNs), limiting the impact of deep learning advances for SNNs. Here, we...