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课程大纲
- 机器学习的局限性
- 机器学习、非线性映射
- 神经网络
- 非线性优化,随机/MiniBatch 梯度不错
- 反向传播
- 深度稀疏编码
- 稀疏自动编码器 (SAE)
- 卷积神经网络 (CNN)
- 成功:描述符匹配
- 基于立体声的障碍物
- 回避 Robotics
- 池化和不变性
- 可视化/反卷积网络
- 循环神经网络 (RNN) 及其优化
- NLP的应用
- RNNs继续说道,
- 无麻线优化
- 语言分析:词/句向量、解析、情感分析等。
- 概率图形模型
- 霍普菲尔德网,玻尔兹曼机
- 深度置信网络、堆叠式RBM
- 在视频中的NLP、姿势和活动识别中的应用
- 最新进展
- 大规模学习
- 神经图灵机
要求
对Machine Learning有很好的理解。至少对深度学习有一定的理论知识。
28 小时
客户评论 (4)
I was benefit from the passion to teach and focusing on making thing sensible.
Zaher Sharifi - GOSI
课程 - Advanced Deep Learning
Doing exercises on real examples using Keras. Mihaly totally understood our expectations about this training.
Paul Kassis
课程 - Advanced Deep Learning
The exercises are sufficiently practical and do not need a high knowledge in Python to be done.
Alexandre GIRARD
课程 - Advanced Deep Learning
The global overview of deep learning