In this course, you learn the essentials of Deep Learning. We start with a brief introduction and illustrate how to set up your software environment. We then review the foundations of artificial neural networks such as the perceptron and multilayer perceptron (MLP) networks. Next, we elaborate on convolutional neural networks illustrated with various examples. We then discuss representational learning and embeddings. Recurrent neural networks are also covered again extensively illustrated with examples. This is followed by a discussion on generative adversarial networks. The course concludes by discussing reinforcement learning.
The course provides a sound mix of both theoretical and technical insights, as well as practical implementation details. These are illustrated by several real-life case studies and examples using Keras and TensorFlow.
The course features 28 Jupyter notebooks containing hands-on examples. A Python tutorial is also provided.
The course features more than 4 hours of video lectures, various multiple choice questions, and lots of references to background literature. A certificate signed by the instructors is provided upon successful completion.
We can also come and teach this course on-site in classroom format. If interested, please mail us at: Bart@BlueCourses.com.
The enrollment fee for this course is EUR 100 (VAT excl.) per participant. Payments are securely handled by PayPal. If you are a company in the European Union, then we can apply VAT reverse charge. For this, please mail your VAT number to Bart@BlueCourses.com. Part of our course revenue is used towards funding organizations involvement in protecting and cleaning our oceans. See our about page to learn more about our mission statement.
Before subscribing to this course, you should have a basic understanding of descriptive statistics (e.g., mean, median, standard deviation, histograms, scatter plots, etc.) and inference (e.g., confidence intervals, hypothesis testing). You should also have followed and completed our Machine Learning Essentials course.
Chapter 1: Introduction
From if-then rules to deep learning
A brief history of deep learning
What will be covered
Chapter 2: Getting ready for deep learning
Setting up your environment
Chapter 3: Foundations of artificial neural networks
Activation and transfer functions
Training a perceptron
A simple iterative approach
☞ A simple perceptron in Python
The XOR problem
Multilayer perceptron (MLP) networks
☞ Automatic differentiation in TensorFlow
Summary so far
☞ Handwritten digits recognition with an MLP
☞ The importance of initialization
Stochastic gradient descent
Chapter 4: Convolutional neural networks
The convolutional architecture
Filters and pooling
☞ Handwritten digits recognition with a CNN
☞ Colored image classification with a CNN
Opening the black box
☞ Interpretability examples with a CNN
☞ Transfer learning with a CNN
☞ Locating objects with a CNN
☞ Deep dreaming example
Artistic style transfer
☞ Artistic style transfer example
Chapter 5: Representational learning
Embeddings in text
☞ Building a word2vec model
☞ Graph embeddings example
☞ Featurization with categorical embeddings
☞ Anomaly detection with auto-encoders
☞ Image denoising with auto-encoders
Chapter 6: Recurrent neural networks
The recurrent architecture
☞ An RNN from scratch
☞ Text classification with an RNN
☞ Text generation with an RNN
Attention and memory
☞ Text classification with attention
☞ Time series forecasting with an LSTM
Revisiting the CNN
☞ Text classification with a CNN
Chapter 7: Generative adversarial networks
The generative adversarial architecture
☞ Generating digits with a GAN
☞ Generating digits with a GAN, revisited
Chapter 8: Reinforcement learning
☞ Q learning example
Deep Q learning
☞ Deep Q learning example
☞ Double deep Q learning example
Chapter 9: Conclusions
Prof. dr. Seppe vanden Broucke
Seppe vanden Broucke is an assistant professor at the department of Business Informatics at UGent (Belgium) and is a lecturer at KU Leuven (Belgium). His research interests include business data mining and analytics, machine learning, process management and process mining. His work has been published in well-known international journals and presented at top conferences. He is also author of the books Beginning Java Programming (Wiley, 2015) and Principles of Database Management (Cambridge University Press, 2018). Seppe's teaching includes Advanced Analytics, Big Data and Information Management courses. He also frequently teaches for industry and business audiences. See seppe.net for further details.