DeepLearning4J (DL4J) Training in Copenhagen, Denmark

This course provides a comprehensive introduction to DeepLearning4J (DL4J), an open-source deep learning framework for Java and Scala. Participants will learn how to build, train, and optimize deep learning models using DL4J, integrate them with enterprise applications, and deploy them for real-world use cases like image recognition, NLP, and predictive analytics.

Introduction to DL4J and Environment Setup

• Overview of DeepLearning4J and its ecosystem
• Setting up DL4J with Java and Maven
• Introduction to ND4J (Numerical Computing for Java)
• Working with DataVec for data preprocessing

Building and Training Deep Learning Models

• Understanding DL4J’s MultiLayerNetwork and ComputationGraph
• Implementing Feedforward Neural Networks (FNNs) in DL4J
• Building Convolutional Neural Networks (CNNs) for image recognition
• Optimizing models using loss functions, activation functions, and optimizers

Advanced Topics and Model Deployment

• Working with Recurrent Neural Networks (RNNs) and LSTMs
• Transfer learning and fine-tuning pre-trained models
• Deploying DL4J models in enterprise applications and REST APIs
• Performance optimization and GPU acceleration in DL4J
• Hands-on: Deploying a DL4J deep learning model in a Java application