# Cetana
An advanced machine learning library empowering developers to build intelligent applications with ease, written in Rust.
> **Cetana** (चेतन) is a Sanskrit word meaning "consciousness" or "intelligence," reflecting the library's goal of bringing machine intelligence to your applications.
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## Overview
Cetana is a Rust-based machine learning library designed to provide efficient and flexible machine learning operations across multiple compute platforms. It focuses on providing a clean, safe API while maintaining high performance.
## Features
- **Type-safe Tensor Operations**
- **Neural Network Building Blocks**
- **Automatic Differentiation**
- **Model Serialization**
- **Multiple Activation Functions** (ReLU, Sigmoid, Tanh)
- **Basic Optimizers and Loss Functions**
- **CPU Backend** (with planned GPU support)
## Table of Contents
- [Example Usage](#example-usage)
- [Compute Backends](#compute-backends)
- [Roadmap](#roadmap)
- [Phase 1: Core Implementation (CPU)](#phase-1-core-implementation-cpu)
- [Phase 2: GPU Acceleration](#phase-2-gpu-acceleration)
- [Phase 3: Advanced Features](#phase-3-advanced-features)
- [Phase 4: High-Level APIs](#phase-4-high-level-apis)
- [Contributing](#contributing)
- [License](#license)
## Example Usage
## Compute Backends
- [x] CPU (in progress)
- [ ] CUDA
- [ ] Metal Performance Shaders (MPS)
- [ ] Vulkan
## Roadmap
### Phase 1: Core Implementation (CPU)
- [x] Basic tensor operations
- [x] Addition, subtraction
- [x] Matrix multiplication
- [x] Element-wise operations
- [x] Broadcasting support
- [x] Neural Network Modules
- [x] Linear layers
- [x] Activation functions (ReLU, Sigmoid, Tanh)
- [x] Convolutional layers
- [x] Pooling layers
- [x] Automatic Differentiation
- [x] Backpropagation
- [x] Gradient computation
- [x] Auto Grad
- [x] Loss Functions
- [x] MSE (Mean Squared Error)
- [x] Cross Entropy
- [x] Binary Cross Entropy
- [ ] Training Utilities
- [x] Basic training loops
- [ ] Advanced batch processing
- [ ] Mini-batch handling
- [ ] Batch normalization
- [ ] Dropout layers
- [ ] Data loaders
- [ ] Dataset abstraction
- [ ] Data augmentation
- [ ] Custom dataset support
- [x] Model Serialization
- [x] Save/Load models
- [x] Export/Import weights
### Phase 2: GPU Acceleration
- [ ] CUDA Backend
- [x] Basic initialization
- [ ] Memory management
- [ ] Basic operations
- [ ] Advanced operations
- [ ] cuBLAS integration
- [ ] MPS Backend (Apple Silicon)
- [ ] Basic operations
- [ ] Performance optimizations
- [ ] Vulkan Backend
- [x] Device initialization
- [x] Basic compute pipeline
- [x] Memory management
- [ ] Advanced operations
- [ ] Performance optimizations
### Phase 3: Advanced Features
- [ ] Distributed Training
- [ ] Multi-GPU support
- [ ] Data parallelism
- [ ] Model parallelism
- [ ] Automatic Mixed Precision
- [ ] Model Quantization
- [ ] Performance Profiling
- [ ] Operation timing
- [ ] Memory usage tracking
- [ ] Bottleneck analysis
- [ ] Advanced Optimizations
- [ ] Kernel fusion
- [ ] Memory pooling
- [ ] Operation scheduling
- [ ] Graph optimization
### Phase 4: High-Level APIs
- [ ] Model Zoo
- [ ] Pre-trained Models
- [ ] Easy-to-use Training APIs
- [ ] Integration Examples
- [ ] Comprehensive Documentation
## Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
## License
Licensed under the Apache License, Version 2.0. See [LICENSE](LICENSE) for details.