Deep Learning Course: Fundementals and applications
This course covers the fundamentals and applications of deep learning (DL). It is designed to quickly get learners up to speed with applied DL as practiced in industry. The course is best for people who are comfortable reading and writing intermediate Python code. It is a mix of lectures and hands-on activities. Materials are both in PyTorch and TensorFlow/Keras. It can be delivered in 1/2 day, 1 day, 3 days, or longer.
Topics
- General introduction to deep learning
- Linear algebra review
- Machine learning review
- What is a neural network?
- Multi-Layer Perceptron (MLP)
- Backpropagation, aka backprop
- Stochastic gradient descent (SGD)
- Convolutional Neural Network (CNN)
- Data augmentation
- Seminal deep learning architectures
- Fine-tuning
- Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), and Gated Recurrent Unit (GRU)
- Generative Adversarial Networks (GANs)
- Word embeddings
- Applications: Image classification, natural language processing (NLP)
I would be delighted to chat with you about delivering this course (or developing others) for your organization.
Send me email