Objective:Perform image classification using pretrained networks. Use transfer learning to train customized classification networks.

• 公关etrained networks
• Image datastores
• Transfer learning
• 网络评估

Objective:Gain insight into how a network is operating by visualizing image data as it passes through the network. Apply this technique to different kinds of images.

• Activations
• Feature extraction for machine learning

Objective:Build convolutional networks from scratch. Understand how information is passed between network layers and how different types of layers work.

• Training from scratch
• 神经网络
• Convolution layers and filters

Objective:Understand how training algorithms work. Set training options to monitor and control training.

• Network training
• Training progress plots
• Validation

Objective:选择和实施修改培训算法选项，网络架构或培训数据以提高网络性能。

• Training options
• Directed acyclic graphs
• Augmented datastores

Objective:Create convolutional networks that can predict continuous numeric responses.

• Transfer learning for regression
• Evaluation metrics for regression networks

Objective:Train networks to locate and label specific objects within images.

• Image application workflow
• 对象检测

Objective:Build and train networks to perform classification on ordered sequences of data, such as time series or sensor data.

• 长短期内存网络
• Sequence classification
• 序列预处理
• Categorical sequences

Objective:使用反复网络创建预测序列。

• 序列顺序分类
• 序列预测