Deep Learning Essentials

About This Course

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.

See this video on convolutional weights visualization to get a free teaser of the course contents.

We can also come and teach this course on-site in classroom format. If interested, please mail us at: Bart@BlueCourses.com.

Price

The enrollment fee for this course is EUR 250 (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.

Requirements

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.

Course Outline

Chapter 1: Introduction

About
From if-then rules to deep learning
Comparison
A brief history of deep learning
What will be covered

Chapter 2: Getting ready for deep learning

Software
Setting up your environment

Chapter 3: Foundations of artificial neural networks

The perceptron
- Concept
- Activation and transfer functions
- Bias
Training a perceptron
- A simple iterative approach
- ☞ A simple perceptron in Python
- Gradient descent
- The XOR problem
Multilayer perceptron (MLP) networks
- Backpropagation
- Automatic differentiation
- ☞ Automatic differentiation in TensorFlow
- Summary so far
- ☞ Handwritten digits recognition with an MLP
Further aspects
- Activation functions
- ReLU
- Initialization
- ☞ The importance of initialization
- Loss functions
- Stochastic gradient descent
- Backpropagation alternatives
- Optimizers
- Learning rate
- Preventing overfitting
- Hyperparameter optimization
Quiz

Chapter 4: Convolutional neural networks

The convolutional architecture
- Concept
- Filters and pooling
- ☞ Handwritten digits recognition with a CNN
Best practices
Dropout
Batch normalization
Data augmentation
☞ Colored image classification with a CNN
Opening the black box
☞ Interpretability examples with a CNN
Further aspects
- Transfer learning
- ☞ Transfer learning with a CNN
- Variants
- ☞ Locating objects with a CNN
- Capsule networks
- Adversarial attacks
Use cases
- Deep dream
- ☞ Deep dreaming example
- Artistic style transfer
- ☞ Artistic style transfer example
Quiz

Chapter 5: Representational learning

Embeddings in text
- Concept
- Word embeddings
- word2vec
- ☞ Building a word2vec model
Use cases
Generalizing embeddings
Further aspects
- Variants
- ☞ Graph embeddings example
- Software
- Categorical embeddings
- ☞ Featurization with categorical embeddings
Auto-encoders
- ☞ Anomaly detection with auto-encoders
- ☞ Image denoising with auto-encoders
Quiz

Chapter 6: Recurrent neural networks

The recurrent architecture
- Concept
- ☞ An RNN from scratch
- Common RNNs
- ☞ Text classification with an RNN
- ☞ Text generation with an RNN
Further aspects
- Variants
- Attention and memory
- ☞ Text classification with attention
- Time series
- ☞ Time series forecasting with an LSTM
- Revisiting the CNN
- ☞ Text classification with a CNN
Quiz

Chapter 7: Generative adversarial networks

The generative adversarial architecture
- Concept
- ☞ Generating digits with a GAN
Challenges
Best practices
- ☞ Generating digits with a GAN, revisited
Further aspects
- Variants
Use cases
Quiz

Chapter 8: Reinforcement learning

Reinforcement learning
- Concept
- Q learning
- ☞ Q learning example
- Deep Q learning
- ☞ Deep Q learning example
Further aspects
- Variants
- ☞ Double deep Q learning example
- Software
Challenges
Quiz

Chapter 9: Conclusions

Course Staff

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.