1、培训过程中，如有部分内容理解不透或消化不好，可免费在以后培训班中重听；
        2、培训结束后,授课老师留给学员联系方式,保障培训效果,免费提供课后技术支持。
        3、培训合格学员可享受免费推荐就业机会。

Getting Started

Quickstart: Running Examples and DL4J in Your Projects
Comprehensive Setup Guide
Convolutional Neural Networks

Convolutional Net Introduction
Images Are 4-D Tensors?
ConvNet Definition
How Convolutional Nets Work
Maxpooling/Downsampling
DL4J Code Sample
Other Resources
Datasets

Datasets and Machine Learning
Custom Datasets
CSV Data Uploads
Scaleout

Iterative Reduce Defined
Multiprocessor / Clustering
Running Worker Nodes
Advanced DL2J

Build Locally From Master
Use the Maven Build Tool
Vectorize Data With Canova
Build a Data Pipeline
Run Benchmarks
Configure DL4J in Ivy, Gradle, SBT etc
Find a DL4J Class or Method
Save and Load Models
Interpret Neural Net Output
Visualize Data with t-SNE
Swap CPUs for GPUs
Customize an Image Pipeline
Perform Regression With Neural Nets
Troubleshoot Training & Select Network Hyperparameters
Visualize, Monitor and Debug Network Learning
Speed Up Spark With Native Binaries
Build a Recommendation Engine With DL4J
Use Recurrent Networks in DL4J
Build Complex Network Architectures with Computation Graph
Train Networks using Early Stopping
Download Snapshots With Maven
Customize a Loss Function