Caffe Training and Certification in Copenhagen, Denmark

This course provides a comprehensive introduction to Caffe, an efficient deep learning framework widely used for image classification and convolutional neural networks (CNNs). Participants will learn how to set up Caffe, design and train deep learning models, optimize performance, and deploy models. The course includes hands-on exercises with real-world datasets to develop practical expertise.

Introduction to Caffe and Installation

• Overview of Caffe and its deep learning capabilities
• Setting up Caffe on Ubuntu/Windows
• Understanding Caffe’s architecture (Layers, Blobs, Net, Solver)
• Working with Caffe’s Model Zoo and pre-trained networks

Training Deep Learning Models in Caffe

• Understanding Caffe’s model definition files (prototxt format)
• Working with Caffe layers, loss functions, and activation functions
• Setting up Caffe’s Solver and optimization strategies (SGD, Adam, RMSprop)
• Training a CNN from scratch using Caffe

Advanced Topics and Model Deployment

• Fine-tuning pre-trained models for custom datasets
• Implementing data augmentation techniques
• Using Caffe with Python (PyCaffe) for model scripting
• Deploying Caffe models using Caffe2 and ONNX
• Hands-on: Deploying a Caffe-based deep learning model