Introduction to
Deep Learning for Computer Vision

Understand Deep Learning from the ground up: What are neural networks, how do they learn, and how can I optimize them for my task?

Computer Vision Fundamentals: Understand the power of convolutions for automatic feature extraction and construct convolutional neural networks for image classification.

Learn today, apply tomorrow: Learn to build your own neural networks in Python using Tensorflow/Keras.

Learn Best Practices: Ensure your model trains optimally by using data augmentations, memory-efficient training, fast data input pipelines, and optimized model architectures.

Build upon others: Finetune existing models and understand how you can use Transfer Learning to boost your model performance.

You want to understand Deep Learning for Computer Vision from the ground up.

You want an in-depth understanding of modern Computer Vision AI and its applications.

You work with Computer Vision data such as images and videos and want to use AI for your use case.

You already have a basic understanding of Machine Learning concepts and want to push your skills to the next level.

You are looking for a non-technical introduction to Computer Vision AI.

You have no experience in programming.

You only want apply Computer Vision AI, rather than knowing how it works.

You already have a good understanding of Deep Learning or Computer Vision and you look for an advanced training or specific application of Computer Vision AI.

All literature and course materials are included in the price. 

Information on the software and tooling will be shared before the start date.

This is a technical course involving some extent of programming. You will need a laptop.

Online courses are delivered via Zoom or Microsoft Teams.