DeepLearning For Finance
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Introduction to Deep Learning
Introduction to Deep Learning
From Traditional Models to Deep Learning
The Multi-Layer Perceptron (MLP)
Automatic Differentiation: The Engine of Deep Learning
Computation Backends & Keras 3
GPUs and Deep Learning: When Hardware Matters
Keras Fundamentals: Models & Layers
Keras Matrix Operations: The Building Blocks
Activation Functions: Adding Non-linearity
Model Training Fundamentals
Travaux Pratiques
TP1 Corrected: Building Neural Networks - From Simple to Custom Implementations
TP1 Corrected: Building Neural Networks - From Simple to Custom Implementations
Recurrent Neural Networks
Recurrent Neural Networks
Sequential Data Processing: From MLPs to RNNs
Long Short-Term Memory Networks (LSTM)
Modern RNN Architectures
RNN Limitations: Computational Challenges
Travaux Pratiques
TP: Recurrent Neural Networks for Time Series Prediction
TP Corrected: Recurrent Neural Networks for Time Series Prediction
Training a Neural Network
Training a Neural Network
Understanding the Training Loop
Understanding Optimizers
Understanding Callbacks
Training Parameters and Practical Considerations
Travaux Pratiques
TP: Using Deep Learning Frameworks for General Optimization
tp_general_optimization_corrected.html
TP: Impact of Callbacks on Training
Essential Building Blocks of Modern Neural Networks
Essential Building Blocks of Modern Neural Networks
Residual Connections and Gating Mechanisms
Convolutional Layers: From Images to Time Series
Neural Network Embeddings: Learning Meaningful Representations
Attention Mechanisms: Learning What to Focus On
Encoder-Decoder Architectures
Travaux Pratiques
Practical Assignment: Building a Transformer-Based Architecture for Time Series Forecasting
Practical Assignment: Building a Transformer-Based Architecture for Time Series Forecasting
Projets
Projets
Code source
Projets
Remi Genet
2025-04-03
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