fix torch_nano document link error and small change (#6257)

docs/readthedocs/source/doc/Nano/Overview/pytorch_train.md

@@ -74,8 +74,6 @@ class MyNano(TorchNano) :
 MyNano().train(...)
 ```
 
-- note: see [this tutorial](./pytorch_nano.html) for details about our `TorchNano`.
-
 Our `TorchNano` also integrates IPEX and distributed training optimizations. For example,
 
 ```python

docs/readthedocs/source/doc/Nano/QuickStart/index.md

@@ -11,7 +11,7 @@
 
     > ![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub][Nano_pytorch_nano]
 
-    In this guide we'll describe how to use BigDL-Nano to accelerate custom training loop easily with very few changes
+    In this guide we will describe how to use BigDL-Nano to accelerate custom training loop easily with very few changes
 
 ---------------------------
 

docs/readthedocs/source/doc/Nano/QuickStart/pytorch_nano.md

@@ -2,7 +2,7 @@
 
 **In this guide we'll demonstrate how to use BigDL-Nano to accelerate custom train loop easily with very few changes.**
 
-### **Step 0: Prepare Environment**
+### Step 0: Prepare Environment
 
 We recommend using [conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/) to prepare the environment. Please refer to the [install guide](../../UserGuide/python.md) for more details.
 
@@ -15,7 +15,7 @@ pip install --pre --upgrade bigdl-nano[pytorch]
 source bigdl-nano-init
 ```
 
-### **Step 1: Load the Data**
+### Step 1: Load the Data
 
 Import Cifar10 dataset from torch_vision and modify the train transform. You could access [CIFAR10](https://www.cs.toronto.edu/~kriz/cifar.html) for a view of the whole dataset.
 
@@ -49,7 +49,7 @@ def create_dataloader(data_path, batch_size):
     return train_loader
 ```
 
-### **Step 2: Define the Model**
+### Step 2: Define the Model
 
 You may define your model in the same way as the standard PyTorch models.
 
@@ -70,7 +70,7 @@ class ResNet18(nn.Module):
         return self.model(x)
 ```
 
-### **Step 3: Define Train Loop**
+### Step 3: Define Train Loop
 
 Suppose the custom train loop is as follows:
 
@@ -149,7 +149,7 @@ class MyNano(TorchNano):
             print(f'avg_loss: {total_loss / num}')
 ```
 
-### **Step 4: Run with Nano TorchNano**
+### Step 4: Run with Nano TorchNano
 
 ```python
 MyNano().train()