Jupyter Notebooks are interactive computational environments that combine code execution, rich text, mathematics, plots, and media in a single document.
Perfect for Deep Learning:
• Interactive experimentation
• Immediate visual feedback
• Document your thinking process
• Share reproducible research
Industry Standard:
Used by researchers at Google Brain, OpenAI, and DeepMind for prototyping and sharing deep learning experiments.
Key Features for ML
📝
Code and Markdown Cells
Mix executable code with formatted documentation
📊
Inline Visualization
Plots and images appear directly in the notebook
⚡
GPU Support
Access CUDA-enabled GPUs for training
🎛️
Interactive Widgets
Create sliders and controls for experiments
Jupyter Notebook Interface
[1] Markdown
# Training a Neural Network
Let's build a simple neural network using TensorFlow...
[2] Code
import tensorflow as tf
model = tf.keras.Sequential([...])
model.compile(optimizer='adam', ...)