Resources
Topic PDFs (Extended - 10 slides each)
Download individual topic presentations with full content (Learning Goal, Key Concept, Formulas, Practice Problems, Takeaways):
| Part 1: Foundations | Part 2: Building Blocks | Part 3: Architecture | Part 4: Learning | Part 5: Application |
|---|---|---|---|---|
| 01. Biological Neuron | 05. Activation Functions | 09. Network Architecture | 13. Loss Landscape | 17. Market Data |
| 02. Single Neuron | 06. Linear Limitation | 10. Forward Propagation | 14. Gradient Descent | 18. Prediction Results |
| 03. Problem Visualization | 07. Sigmoid Saturation | 11. Decision Boundary | 15. Overfitting | 19. Confusion Matrix |
| 04. Neuron Decision Maker | 08. Boundary Evolution | 12. Feature Hierarchy | 16. Learning Rate | 20. Trading Backtest |
Download Complete Presentation (PDF)
Books
Introductory
- “Neural Networks and Deep Learning” by Michael Nielsen - Free online book with interactive visualizations
- “Deep Learning” by Goodfellow, Bengio, Courville - Comprehensive textbook (free online)
- “Hands-On Machine Learning” by Aurelien Geron - Practical approach with code examples
Finance Applications
- “Advances in Financial Machine Learning” by Marcos Lopez de Prado - Practical ML for finance
- “Machine Learning for Asset Managers” by Marcos Lopez de Prado - Focused on portfolio management
- “Deep Learning for Finance” by Jannes Klaas - Neural networks in financial applications
Online Courses
Free
- 3Blue1Brown Neural Networks - Excellent visual explanations on YouTube
- Stanford CS229 - Machine learning course materials online
- Fast.ai - Practical deep learning for coders
Paid/Certificate
- Coursera: Deep Learning Specialization by Andrew Ng
- Coursera: Machine Learning for Trading by Georgia Tech
- edX: Principles of Machine Learning by Microsoft
Tools and Libraries
Python Libraries
- NumPy - Numerical computing foundation
- scikit-learn - Classic machine learning, including MLPClassifier
- TensorFlow/Keras - Industry-standard deep learning
- PyTorch - Research-focused deep learning
Visualization
- Matplotlib - Python plotting library
- TensorBoard - Training visualization for TensorFlow
- Weights & Biases - Experiment tracking
Financial Data
- yfinance - Yahoo Finance data API
- Alpha Vantage - Stock data API
- Quandl - Financial and economic data
Interactive Tools
Neural Network Playgrounds
- TensorFlow Playground (playground.tensorflow.org) - Visualize neural network training
- ConvNetJS - Neural network demo in browser
- NN-SVG - Draw neural network diagrams
Mathematics
- Desmos - Graphing calculator for activation functions
- WolframAlpha - Compute derivatives and integrals
Key Formulas Reference
Activation Functions
| Function | Formula | Range | Derivative Max |
|---|---|---|---|
| Sigmoid | 1/(1+e^-z) | (0,1) | 0.25 |
| ReLU | max(0,z) | [0,inf) | 1 |
| Tanh | (e^z-e^-z)/(e^z+e^-z) | (-1,1) | 1 |
Training
| Concept | Formula |
|---|---|
| Weighted sum | z = Wx + b |
| Gradient descent | w := w - eta * dL/dw |
| Binary cross-entropy | L = -[ylog(p) + (1-y)log(1-p)] |
Evaluation
| Metric | Formula |
|---|---|
| Accuracy | (TP+TN)/(TP+TN+FP+FN) |
| Precision | TP/(TP+FP) |
| Recall | TP/(TP+FN) |
| F1 Score | 2PrecisionRecall/(Precision+Recall) |
Glossary of Terms
| Term | Definition |
|---|---|
| Activation function | Non-linear transformation applied after weighted sum |
| Backpropagation | Algorithm to compute gradients for all weights |
| Batch | Subset of training data used in one update |
| Bias | Learnable offset term in neuron computation |
| Cross-entropy | Loss function for classification |
| Epoch | One complete pass through training data |
| Gradient | Derivative indicating direction of steepest increase |
| Hidden layer | Layer between input and output |
| Learning rate | Step size for gradient descent |
| Loss | Measure of prediction error |
| Neuron | Basic computational unit |
| Overfitting | Memorizing training data, poor generalization |
| ReLU | Rectified Linear Unit activation |
| Sigmoid | S-shaped activation mapping to (0,1) |
| Softmax | Multi-class probability output |
| Underfitting | Model too simple to capture patterns |
| Weight | Learnable parameter controlling input influence |
Getting Help
- GitHub Issues: Report problems or ask questions
- Stack Overflow: Tag questions with [neural-network] and [machine-learning]
- Reddit: r/learnmachinelearning, r/MachineLearning
Attribution
This course material is provided by Digital-AI-Finance under the MIT License.
All charts and visualizations are original works created with matplotlib.
(c) Joerg Osterrieder 2025