Update README.md (#121)

README.md

@@ -16,24 +16,24 @@ Some key features of MLX include:
    MLX has higher-level packages like `mlx.nn` and `mlx.optimizers` with APIs
    that closely follow PyTorch to simplify building more complex models.
 
- - **Composable function transformations**: MLX has composable function
+ - **Composable function transformations**: MLX supports composable function
    transformations for automatic differentiation, automatic vectorization,
    and computation graph optimization.
 
  - **Lazy computation**: Computations in MLX are lazy. Arrays are only
    materialized when needed.
 
- - **Dynamic graph construction**: Computation graphs in MLX are built
+ - **Dynamic graph construction**: Computation graphs in MLX are constructed
    dynamically. Changing the shapes of function arguments does not trigger
    slow compilations, and debugging is simple and intuitive.
 
  - **Multi-device**: Operations can run on any of the supported devices
-   (currently, the CPU and GPU).
+   (currently the CPU and the GPU).
 
  - **Unified memory**: A notable difference from MLX and other frameworks
    is the *unified memory model*. Arrays in MLX live in shared memory.
    Operations on MLX arrays can be performed on any of the supported
-   device types without moving data.
+   device types without transferring data.
 
 MLX is designed by machine learning researchers for machine learning
 researchers. The framework is intended to be user-friendly, but still efficient
@@ -66,7 +66,7 @@ in the documentation.
 
 ## Installation
 
-MLX is available on [PyPi](https://pypi.org/project/mlx/). To install the Python API, run:
+MLX is available on [PyPI](https://pypi.org/project/mlx/). To install the Python API, run:
 
 ```
 pip install mlx