Topics Covered Today
1. Preliminaries
- Basic setup and environment configuration
- Introduction to PyTorch ecosystem
- Understanding computational frameworks
2. Data Manipulation
- Tensor operations and basic manipulations
- NumPy vs PyTorch arrays
- Memory management and device allocation (CPU/GPU)
3. Data Preprocessing
- Data cleaning techniques
- Normalization and standardization
- Feature engineering basics
- Handling missing values
4. Linear Algebra Fundamentals
- Vector operations
- Matrix manipulations
- Dot products and matrix multiplication
- Eigenvalues and eigenvectors basics
5. Calculus Essentials
- Derivatives and gradients
- Chain rule
- Partial derivatives
- Optimization basics
6. Automatic Differentiation
- Computational graphs
- Forward and backward propagation
requires_gradand gradient tracking- Understanding
backward()function
7. Probability and Statistics
- Types of uncertainty (Aleatoric vs Epistemic)
- Basic probability concepts
- Statistical distributions
- Bayes' theorem and applications
8. Documentation
- PyTorch documentation navigation
- Best practices for code documentation
- Using docstrings effectively
Key Learnings
# Example code learned today
x = torch.arange(4.0)
x.requires_grad_(True)
y = 2 * torch.dot(x, x)
y.backward()
print(x.grad) # Understanding gradients
Progress Made
- Understood foundational mathematics behind deep learning
- Implemented basic PyTorch operations
- Learned automatic differentiation concepts
- Grasped statistical concepts in ML context