Deep Neural Networks Virtual Lab

Build, visualize, and experiment with deep learning architectures

🎯

🌟 Enhanced Training

Full training with hyperparameters

🏗️

Network Builder

Build custom DNNs interactively

📊

Layer Visualization

See data flow through layers

🎲

Dropout

Prevent overfitting with dropout

⚖️

Batch Normalization

Stabilize training with BatchNorm

📈

Learning Rate Scheduler

Optimize training dynamics

⚠️

Overfitting Demo

Understand bias-variance tradeoff

🧠 Enhanced DNN Training - Interactive Deep Learning

Train a deep neural network with full control over hyperparameters and watch the training process in real-time.

⚙️ Hyperparameters

Learning Rate: 0.01

Controls convergence speed

Batch Size: 32

Samples per update

Epochs: 50

Training iterations

Hidden Layers: 2

Network depth

Neurons per Layer: 64

Network width

Dropout: 0.2

Prevents overfitting

🎮 Training Controls

Speed:

Epoch 0 / 500.0% Complete

Train Loss

Val Loss

Train Accuracy

Val Accuracy

📊 Training Metrics

Loss Over Epochs

Accuracy Over Epochs

Layer Weights & Gradients (Epoch 0)

📋 Training History

Epoch	Train Loss	Val Loss	Train Acc	Val Acc

📐 Training Equations

Forward Propagation:

a^{[l]} = g^{[l]}(W^{[l]} a^{[l-1]} + b^{[l]})

Loss Function (Cross-Entropy):

\mathcal{L} = -\frac{1}{m} \sum_{i=1}^{m} [y^{(i)} \log(\hat{y}^{(i)}) + (1-y^{(i)}) \log(1-\hat{y}^{(i)})]

Gradient Descent Update:

W^{[l]} := W^{[l]} - \alpha \frac{\partial \mathcal{L}}{\partial W^{[l]}}