> ## Documentation Index
> Fetch the complete documentation index at: https://newtorch.aboneda.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Study Plan 2

# PyTorch API Complete Study Guide

## Study Path Organization

### Phase 1: Fundamentals (2-3 weeks)

**Goal**: Master basic tensor operations and understand PyTorch's core concepts

#### Week 1: Tensor Basics

* **Day 1-2**: Tensor Creation
  * `torch.tensor`, `torch.zeros`, `torch.ones`, `torch.arange`, `torch.linspace`
  * `torch.eye`, `torch.empty`, `torch.full`
  * `torch.from_numpy`, `torch.as_tensor`, `torch.asarray`
* **Day 3-4**: Tensor Manipulation
  * `torch.cat`, `torch.stack`, `torch.split`, `torch.chunk`
  * `torch.reshape`, `torch.transpose`, `torch.permute`
  * `torch.squeeze`, `torch.unsqueeze`, `torch.flatten`
* **Day 5-7**: Indexing & Slicing
  * `torch.index_select`, `torch.masked_select`, `torch.gather`, `torch.scatter`
  * Boolean indexing, advanced indexing
  * `torch.where`, `torch.nonzero`

#### Week 2: Mathematical Operations

* **Day 1-2**: Pointwise Operations
  * Arithmetic: `add`, `sub`, `mul`, `div`, `pow`
  * Trigonometric: `sin`, `cos`, `tan`, `asin`, `acos`, `atan`
  * Exponential: `exp`, `log`, `sqrt`, `sigmoid`, `tanh`
* **Day 3-4**: Reduction Operations
  * `torch.sum`, `torch.mean`, `torch.std`, `torch.var`
  * `torch.max`, `torch.min`, `torch.argmax`, `torch.argmin`
  * `torch.prod`, `torch.median`, `torch.mode`
* **Day 5-7**: Comparison & Logical Operations
  * Comparison: `eq`, `ne`, `gt`, `ge`, `lt`, `le`
  * Logical: `logical_and`, `logical_or`, `logical_not`, `logical_xor`
  * Bitwise: `bitwise_and`, `bitwise_or`, `bitwise_xor`

#### Week 3: Linear Algebra Basics

* **Day 1-3**: Matrix Operations
  * `torch.mm`, `torch.matmul`, `torch.bmm`
  * `torch.dot`, `torch.vdot`, `torch.outer`, `torch.inner`
  * Broadcasting rules
* **Day 4-7**: Basic Linear Algebra
  * `torch.linalg.norm`, `torch.linalg.det`
  * `torch.linalg.inv`, `torch.linalg.solve`
  * `torch.trace`, matrix properties

***

### Phase 2: Neural Networks (3-4 weeks)

**Goal**: Build and train neural networks from scratch

#### Week 4: Neural Network Fundamentals

* **Day 1-2**: Module System
  * `torch.nn.Module` architecture
  * `torch.nn.Parameter`, `torch.nn.Buffer`
  * Forward and backward passes
* **Day 3-4**: Basic Layers
  * `torch.nn.Linear`
  * `torch.nn.Conv2d`, `torch.nn.Conv1d`, `torch.nn.Conv3d`
  * `torch.nn.MaxPool2d`, `torch.nn.AvgPool2d`
* **Day 5-7**: Activation Functions
  * `torch.nn.ReLU`, `torch.nn.LeakyReLU`, `torch.nn.ELU`
  * `torch.nn.Sigmoid`, `torch.nn.Tanh`
  * `torch.nn.GELU`, `torch.nn.SiLU`, `torch.nn.Softmax`

#### Week 5: Advanced Layers

* **Day 1-3**: Normalization
  * `torch.nn.BatchNorm1d/2d/3d`
  * `torch.nn.LayerNorm`, `torch.nn.GroupNorm`
  * `torch.nn.InstanceNorm1d/2d/3d`
* **Day 4-7**: Recurrent Networks
  * `torch.nn.RNN`, `torch.nn.LSTM`, `torch.nn.GRU`
  * `torch.nn.RNNCell`, `torch.nn.LSTMCell`, `torch.nn.GRUCell`
  * Sequence modeling

#### Week 6: Transformers & Attention

* **Day 1-4**: Transformer Architecture
  * `torch.nn.Transformer`
  * `torch.nn.TransformerEncoder/Decoder`
  * `torch.nn.TransformerEncoderLayer/DecoderLayer`
* **Day 5-7**: Attention Mechanisms
  * `torch.nn.MultiheadAttention`
  * `torch.nn.functional.scaled_dot_product_attention`
  * Self-attention, cross-attention

#### Week 7: Loss Functions & Optimization

* **Day 1-3**: Loss Functions
  * Regression: `MSELoss`, `L1Loss`, `SmoothL1Loss`, `HuberLoss`
  * Classification: `CrossEntropyLoss`, `NLLLoss`, `BCELoss`, `BCEWithLogitsLoss`
  * Embedding: `CosineEmbeddingLoss`, `TripletMarginLoss`
* **Day 4-7**: Optimizers
  * `torch.optim.SGD`, `torch.optim.Adam`, `torch.optim.AdamW`
  * `torch.optim.RMSprop`, `torch.optim.Adagrad`
  * Learning rate schedulers

***

### Phase 3: Automatic Differentiation (1-2 weeks)

**Goal**: Master autograd and gradient computation

#### Week 8: Autograd Deep Dive

* **Day 1-3**: Gradient Computation
  * `torch.autograd.backward`, `torch.autograd.grad`
  * `Tensor.backward()`, `Tensor.grad`
  * Computational graphs
* **Day 4-5**: Gradient Contexts
  * `torch.no_grad()`, `torch.enable_grad()`
  * `torch.set_grad_enabled()`, `torch.inference_mode()`
  * When to use each context
* **Day 6-7**: Custom Autograd Functions
  * `torch.autograd.Function`
  * `forward()` and `backward()` methods
  * Custom gradient implementation

***

### Phase 4: Data Loading & Processing (1 week)

**Goal**: Efficiently load and preprocess data

#### Week 9: Data Utilities

* **Day 1-3**: Datasets
  * `torch.utils.data.Dataset`
  * `torch.utils.data.TensorDataset`
  * Custom dataset creation
* **Day 4-7**: Data Loading
  * `torch.utils.data.DataLoader`
  * `torch.utils.data.Sampler`, `torch.utils.data.BatchSampler`
  * Multiprocessing, pin\_memory, prefetching

***

### Phase 5: Advanced Training (2-3 weeks)

**Goal**: Implement production-ready training pipelines

#### Week 10: Distributed Training

* **Day 1-3**: Data Parallel
  * `torch.nn.DataParallel`
  * `torch.nn.parallel.DistributedDataParallel`
  * Multi-GPU training
* **Day 4-7**: FSDP & Advanced Parallelism
  * `torch.distributed.fsdp.FullyShardedDataParallel`
  * Tensor parallelism, pipeline parallelism
  * Distributed optimization

#### Week 11: Mixed Precision & Optimization

* **Day 1-3**: Automatic Mixed Precision
  * `torch.cuda.amp.autocast`
  * `torch.cuda.amp.GradScaler`
  * FP16/BF16 training
* **Day 4-7**: Gradient Accumulation & Clipping
  * `torch.nn.utils.clip_grad_norm_`
  * `torch.nn.utils.clip_grad_value_`
  * Memory-efficient training

#### Week 12: Checkpointing & Serialization

* **Day 1-4**: Model Saving/Loading
  * `torch.save`, `torch.load`
  * State dict management
  * `torch.nn.utils.checkpoint` (gradient checkpointing)
* **Day 5-7**: Distributed Checkpointing
  * `torch.distributed.checkpoint`
  * Sharded checkpoints
  * Resuming training

***

### Phase 6: Performance & Deployment (2-3 weeks)

**Goal**: Optimize models for production

#### Week 13: JIT Compilation

* **Day 1-4**: TorchScript
  * `torch.jit.script`, `torch.jit.trace`
  * `torch.jit.ScriptModule`
  * `torch.jit.freeze`, `torch.jit.optimize_for_inference`
* **Day 5-7**: JIT Optimization
  * Fusion optimization
  * Type refinement
  * Graph optimization

#### Week 14: PyTorch 2.0 Compiler

* **Day 1-4**: torch.compile
  * `torch.compile()` modes
  * Backends: inductor, cudagraphs, onnxrt
  * Debugging compilation
* **Day 5-7**: Advanced Compilation
  * Dynamic shapes
  * AOT Autograd
  * Custom backends

#### Week 15: Model Export & Quantization

* **Day 1-3**: ONNX Export
  * `torch.onnx.export`
  * `torch.onnx.dynamo_export`
  * ONNX runtime deployment
* **Day 4-7**: Quantization
  * `torch.quantization.quantize_dynamic`
  * `torch.quantization.quantize_qat`
  * Post-training quantization, QAT

***

### Phase 7: Advanced Topics (2-4 weeks)

**Goal**: Master advanced PyTorch features

#### Week 16: Functional Transforms

* **Day 1-4**: torch.func
  * `torch.func.vmap` (vectorization)
  * `torch.func.grad`, `torch.func.grad_and_value`
  * `torch.func.jacrev`, `torch.func.jacfwd`
* **Day 5-7**: Functionalization
  * `torch.func.functionalize`
  * `torch.func.functional_call`
  * Pure functional transformations

#### Week 17: Sparse Tensors

* **Day 1-4**: Sparse Formats
  * `torch.sparse_coo_tensor`
  * `torch.sparse_csr_tensor`, `torch.sparse_csc_tensor`
  * `torch.sparse_bsr_tensor`, `torch.sparse_bsc_tensor`
* **Day 5-7**: Sparse Operations
  * `torch.sparse.mm`, `torch.sparse.addmm`
  * `torch.sparse.sum`, `torch.sparse.softmax`
  * Sparse gradients

#### Week 18: Signal Processing & FFT

* **Day 1-4**: FFT Operations
  * `torch.fft.fft`, `torch.fft.ifft`
  * `torch.fft.rfft`, `torch.fft.irfft`
  * 2D and N-D FFT
* **Day 5-7**: Spectral Analysis
  * `torch.stft`, `torch.istft`
  * Window functions
  * Audio/signal processing

#### Week 19: Profiling & Debugging

* **Day 1-4**: Profiler
  * `torch.profiler.profile`
  * `torch.profiler.record_function`
  * Performance analysis
* **Day 5-7**: Debugging Tools
  * `torch.autograd.detect_anomaly`
  * `torch.autograd.gradcheck`, `torch.autograd.gradgradcheck`
  * Memory profiling

***

## Learning Resources by Category

### Essential APIs (Must Know)

1. **Tensor Operations**: 95% daily use
2. **torch.nn.Module**: Core building block
3. **torch.optim**: Training essentials
4. **torch.autograd**: Automatic differentiation
5. **torch.utils.data**: Data loading

### Important APIs (Should Know)

1. **torch.nn.functional**: Functional operations
2. **torch.cuda**: GPU management
3. **torch.distributed**: Multi-GPU/multi-node
4. **torch.jit**: Model optimization
5. **torch.compile**: PyTorch 2.0 compiler

### Advanced APIs (Nice to Know)

1. **torch.func**: Functional transforms
2. **torch.export**: Model export
3. **torch.quantization**: Model quantization
4. **torch.sparse**: Sparse tensors
5. **torch.profiler**: Performance profiling

### Specialized APIs (Domain Specific)

1. **torch.fft**: Signal processing
2. **torch.linalg**: Linear algebra
3. **torch.special**: Special functions
4. **torch.nested**: Nested tensors
5. **torch.distributed.fsdp**: Large model training

***

## Practice Projects by Phase

### Phase 1 Projects

1. Implement tensor operations from scratch
2. Build a simple neural network without nn.Module
3. Create custom data transformations

### Phase 2 Projects

1. Build CNN for image classification
2. Implement RNN for sequence prediction
3. Create a Transformer from scratch

### Phase 3 Projects

1. Implement custom loss function with autograd
2. Build custom optimizer
3. Create backward hooks for analysis

### Phase 4 Projects

1. Build efficient data pipeline for large datasets
2. Implement custom sampler
3. Create data augmentation pipeline

### Phase 5 Projects

1. Multi-GPU training pipeline
2. Mixed precision training
3. Distributed data parallel training

### Phase 6 Projects

1. Export model to ONNX
2. Quantize model for mobile
3. Optimize with torch.compile

### Phase 7 Projects

1. Use vmap for batch processing
2. Implement sparse neural network
3. Build audio processing pipeline with FFT

***

## Daily Study Routine

### Beginner (Weeks 1-4)

* **30 min**: Read documentation
* **60 min**: Code along with examples
* **30 min**: Practice exercises
* **Total**: 2 hours/day

### Intermediate (Weeks 5-12)

* **20 min**: Documentation review
* **90 min**: Build projects
* **30 min**: Debug and optimize
* **Total**: 2.5 hours/day

### Advanced (Weeks 13-19)

* **15 min**: Read research papers
* **120 min**: Advanced projects
* **15 min**: Community engagement
* **Total**: 2.5 hours/day

***

## Assessment Checklist

### Phase 1: ✓

* [ ] Can create tensors in 5+ different ways
* [ ] Understand broadcasting rules
* [ ] Can manipulate tensor shapes efficiently
* [ ] Master indexing and slicing
* [ ] Perform matrix operations

### Phase 2: ✓

* [ ] Build custom nn.Module from scratch
* [ ] Implement CNN, RNN, Transformer
* [ ] Understand loss functions
* [ ] Configure optimizers and schedulers
* [ ] Debug training loops

### Phase 3: ✓

* [ ] Understand computational graphs
* [ ] Implement custom autograd functions
* [ ] Use gradient contexts appropriately
* [ ] Debug gradient flow

### Phase 4: ✓

* [ ] Build efficient data pipelines
* [ ] Implement custom datasets
* [ ] Optimize data loading
* [ ] Handle large datasets

### Phase 5: ✓

* [ ] Set up multi-GPU training
* [ ] Implement mixed precision
* [ ] Use FSDP for large models
* [ ] Manage checkpoints

### Phase 6: ✓

* [ ] Export models to production formats
* [ ] Optimize with JIT/compile
* [ ] Quantize models
* [ ] Profile and optimize performance

### Phase 7: ✓

* [ ] Use functional transforms
* [ ] Work with sparse tensors
* [ ] Implement signal processing
* [ ] Master debugging tools