Update readme

README.md

@@ -5,6 +5,13 @@ Recreating PyTorch from scratch (C/C++, CUDA and Python, with GPU support and au
 **PyNorch** is a deep learning framework constructed using C/C++, CUDA and Python. This is a personal project with educational purpose only! `Norch` means **NOT** PyTorch, and we have **NO** claims to rivaling the already established PyTorch. The main objective of **PyNorch** was to give a brief understanding of how a deep learning framework works internally. It implements the Tensor object, GPU support and an automatic differentiation system. 
 
 # 2 - Installation
+Install this package from PyPi
+
+```css
+$ pip install norch
+```
+
+or from cloning this repository
 ```css
 $ sudo apt install nvidia-cuda-toolkit
 $ git clone https://github.com/lucasdelimanogueira/PyNorch.git
@@ -13,4 +20,42 @@ $ make
 $ cd ..
 ```
 
-# 3 - Get started
+# 3 - Get started
+### 3.1 - Tensor operations
+```python
+import norch
+
+x1 = norch.Tensor([[1, 2], 
+                  [3, 4]], requires_grad=True).to("cuda")
+
+x2 = norch.Tensor([[4, 3], 
+                  [2, 1]], requires_grad=True).to("cuda)
+
+x3 = x1 @ x2
+result = x3.sum()
+result.backward
+
+print(x1.grad)
+```
+
+### 3.2 - Create a model
+
+```python
+import norch
+import norch.nn as nn
+import norch.optim as optim
+
+class MyModel(nn.Module):
+    def __init__(self):
+        super(MyModel, self).__init__()
+        self.fc1 = nn.Linear(1, 10)
+        self.sigmoid = nn.Sigmoid()
+        self.fc2 = nn.Linear(10, 1)
+
+    def forward(self, x):
+        out = self.fc1(x)
+        out = self.sigmoid(out)
+        out = self.fc2(out)
+        
+        return out
+```