updated read me

README.md

@@ -1,2 +1,46 @@
 # mojomagic
-A Mojo neural network library from scratchh (mostly)
+
+
+# How to Create a Neural Network Library Like TensorFlow in Mojo
+
+Creating a neural network library similar to TensorFlow in Mojo involves several key steps. Below is a high-level guide:
+
+## 1. Define the Core Architecture
+- **Tensors**: Implement a class to handle multi-dimensional arrays (tensors) with support for operations like addition, multiplication, reshaping, etc.
+- **Graph/Layer Abstraction**: Define layers (e.g., Dense, Convolutional) that can be combined to form neural network architectures.
+- **Computation Graph**: Create a system to manage operations in a graph-based structure, allowing for differentiation and backpropagation.
+
+## 2. Implement Automatic Differentiation
+- **Forward Pass**: Perform operations on tensors while recording operations in a computation graph.
+- **Backward Pass**: Use the computation graph to calculate gradients during backpropagation.
+
+## 3. Develop Optimization Algorithms
+- Implement optimizers like Stochastic Gradient Descent (SGD), Adam, etc., to adjust weights based on gradients during training.
+
+## 4. Build the Model Training Loop
+- **Forward Propagation**: Pass data through the model.
+- **Loss Calculation**: Compute loss using appropriate loss functions (e.g., Mean Squared Error, Cross Entropy).
+- **Backward Propagation**: Use gradients to update weights via the chosen optimizer.
+
+## 5. Leverage Parallelization and Hardware Acceleration
+- Use Mojo’s support for efficient tensor operations across different hardware (CPUs, GPUs, etc.).
+- Implement parallelized computation, particularly for matrix multiplications and convolution operations.
+
+## 6. Implement Model Serialization and Deployment
+- Allow models to be saved, loaded, and deployed.
+- Implement methods for exporting trained models and utilizing them in production environments.
+
+## 7. Design a User-Friendly API
+- Create an intuitive API that abstracts away complexity:
+  - High-level functions for defining models and training.
+  - Support for both functional (sequential models) and subclassing (custom models).
+
+## 8. Ensure Extensibility
+- Build the library in a modular fashion, making it easy to extend with new layers, loss functions, and optimizers as needed.
+
+## 9. Perform Testing and Benchmarking
+- Test your library on common neural network tasks (like MNIST, CIFAR-10) to ensure correctness and performance.
+- Compare your results with existing libraries.
+
+## 10. Provide Documentation and Examples
+- Offer thorough documentation and usage examples to make your library accessible to users, even those unfamiliar with Mojo.

src/main.mojo

@@ -0,0 +1,4 @@
+from tensor import Tensor
+
+fn main():
+    pass