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Computer Science
Artificial Intelligence
Deep Learning
PyTorch Library
1. Introduction to PyTorch
2. Tensors: The Foundation
3. Tensor Operations and Manipulation
4. Automatic Differentiation
5. Neural Network Construction
6. Data Handling and Processing
7. Model Training and Optimization
8. Model Persistence and Deployment
9. Advanced PyTorch Features
10. PyTorch Ecosystem Integration
Tensor Operations and Manipulation
Indexing and Slicing
Basic Indexing
Single Element Access
Multi-dimensional Indexing
Slicing Operations
Range Slicing
Step Slicing
Negative Indexing
Advanced Indexing
Boolean Indexing
Fancy Indexing
Index Tensors
Conditional Selection
torch.where()
torch.masked_select()
Tensor Shape Manipulation
Reshaping Operations
torch.reshape()
view() Method
Automatic Size Inference
Dimension Manipulation
torch.squeeze()
torch.unsqueeze()
Adding and Removing Dimensions
Transposition and Permutation
torch.transpose()
torch.permute()
Matrix Transposition
Flattening Operations
torch.flatten()
Multi-dimensional Flattening
Expansion and Repetition
torch.expand()
torch.repeat()
Broadcasting vs Expansion
Joining and Splitting Tensors
Concatenation Operations
torch.cat()
Concatenation Along Different Axes
Stacking Operations
torch.stack()
Creating New Dimensions
Splitting Operations
torch.split()
torch.chunk()
Uneven Splits
Gathering and Scattering
torch.gather()
torch.scatter()
Index-based Operations
Mathematical Operations
Element-wise Arithmetic
Addition and Subtraction
Multiplication and Division
Power and Root Operations
Modular Arithmetic
Trigonometric Functions
Basic Trigonometric Functions
Inverse Trigonometric Functions
Hyperbolic Functions
Exponential and Logarithmic Functions
torch.exp()
torch.log()
torch.log10()
torch.log2()
Rounding and Ceiling Functions
torch.round()
torch.floor()
torch.ceil()
torch.trunc()
Statistical Functions
torch.abs()
torch.sign()
torch.clamp()
Reduction Operations
Sum Operations
torch.sum()
Dimension-specific Sums
Cumulative Sums
Mean and Average
torch.mean()
Weighted Averages
Variance and Standard Deviation
torch.var()
torch.std()
Bessel's Correction
Min and Max Operations
torch.min()
torch.max()
torch.argmin()
torch.argmax()
Product Operations
torch.prod()
torch.cumprod()
Norm Calculations
torch.norm()
Different Norm Types
Comparison Operations
Element-wise Comparisons
torch.eq()
torch.ne()
torch.gt()
torch.ge()
torch.lt()
torch.le()
Logical Operations
torch.logical_and()
torch.logical_or()
torch.logical_not()
torch.logical_xor()
Sorting Operations
torch.sort()
torch.argsort()
torch.topk()
Linear Algebra Operations
Matrix Multiplication
torch.matmul()
@ Operator
torch.mm()
Batch Matrix Multiplication
Vector Operations
torch.dot()
Cross Product
Vector Norms
Matrix Decomposition
torch.svd()
QR Decomposition
Cholesky Decomposition
Eigenvalue Operations
torch.eig()
torch.symeig()
Linear System Solving
torch.solve()
Matrix Inversion
Determinant Calculation
Broadcasting
Broadcasting Rules
Dimension Compatibility
Size Compatibility
Broadcasting in Practice
Automatic Broadcasting
Manual Broadcasting
Common Broadcasting Patterns
Vector-Matrix Operations
Batch Operations
Broadcasting Limitations
Memory Considerations
Performance Implications
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2. Tensors: The Foundation
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4. Automatic Differentiation