1、培訓過程中，如有部分內容理解不透或消化不好，可免費在以后培訓班中重聽；
        2、培訓結束后,授課老師留給學員聯系方式,保障培訓效果,免費提供課后技術支持。
        3、培訓合格學員可享受免費推薦就業機會。

Getting Started

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

Restricted Boltzmann Machines
Convolutional Nets (ConvNets)
Long Short-Term Memory Units (LSTMs)
Denoising Autoencoders
Recurrent Nets and LSTMs
Multilayer Neural Nets

Deep-Belief Network
Deep AutoEncoder
Stacked Denoising Autoencoders
Tutorials

Using Recurrent Nets in DL4J
MNIST DBN Tutorial
Iris Flower Tutorial
Canova: Vectorization Lib for ML Tools
Neural Net Updaters: SGD, Adam, Adagrad, Adadelta, RMSProp
Datasets

Datasets and Machine Learning
Custom Datasets
CSV Data Uploads
Scaleout

Iterative Reduce Defined
Multiprocessor / Clustering
Running Worker Nodes
Text

DL4J's NLP Framework
Word2vec for Java and Scala
Textual Analysis and DL
Bag of Words
Sentence and Document Segmentation
Tokenization
Vocab Cache
Advanced DL2J

Build Locally From Master
Contribute to DL4J (Developer Guide)
Choose a Neural Net
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