资源简介
Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.
代码片段和文件信息
相关资源
- 深度学习难得的深入浅出的教材李宏
- 学习摘要:Methods for interpreting and un
- 图神经网络GNN的一些论文介绍
- 神经网络与深度学习 中文版 PDF
- 吴恩达 Machine Learning Yearning 完整版 中
- DBN源码-深度学习
- Learning Bayesian Networks - Neapolitan R. E..
- 深度学习的迁移模型
- 纯C深度学习库
- 文本分类竞赛调优分享.pdf
- 深度学习之思维导图
- Statistical Foundations of Machine Learning
- 基于HOG-CSLBP与深度学习的跨年龄人脸
- 中科院期末2018深度学习期末考试卷子
- Gaussian Processes for Machine Learning
- Expert C Programming Deep C Secrets (C 专家编
- Foundations of Machine Learning 2nd Edition
- 《The elements of statistical learning》第二
- 用于目标检测和深度学习的飞机图像
- 吴恩达最新deep learing课后作业1-4周全
- Learning Virtual Reality236872
- 网易云课堂和coursera深度学习的中文版
- 基于深度学习的通信信号识别技术研
- Boosting :Foundations and Algorithms
- Reinforcement Learning-An Introduction by Sut
- Packt.TensorFlow.Machine.Learning.Cookbook.201
- 基于深度图像和骨骼数据的人体动作
- labview8实用教程 learning文件
- 深度前馈网路的交通信号检测
- deep learning 概览+时序模型
评论
共有 条评论