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README.cn.md
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@ -7,7 +7,7 @@
Jittor 是一个基于即时编译和元算子的高性能深度学习框架，整个框架在即时编译的同时，还集成了强大的Op编译器和调优器，为您的模型生成定制化的高性能代码。
Jittor前端语言为Python。前端使用了模块化的设计，类似于PyTorch，Keras，后端则使用高性能语言编写，如CUDA，C++。
Jittor前端语言为Python。前端使用了模块化的设计，这是目前最主流的深度学习框架接口设计。后端则使用高性能语言编写，如CUDA，C++。
下面的代码演示了如何一步一步使用Python代码，从头对一个双层神经网络建模。
@ -41,7 +41,7 @@ for i,(x,y) in enumerate(get_data(n)):
pred_y = model(x)
loss = ((pred_y - y)**2)
loss_mean = loss.mean()
optim.step (loss_mean)
optim.step(loss_mean)
print(f"step {i}, loss = {loss_mean.data.sum()}")
```

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README.md
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@ -6,7 +6,7 @@
Jittor is a high-performance deep learning framework based on JIT compiling and meta-operators. The whole framework and meta-operators are compiled just-in-time. A powerful op compiler and tuner are integrated into Jittor. It allowed us to generate high-performance code with specialized for your model.
The front-end language is Python. Module Design is used in the front-end, like PyTorch and Keras. The back-end is implemented py high performance language, such as CUDA,C++.
The front-end language is Python. Module Design is used in the front-end, which is the most popular design for deeplearning framework interface. The back-end is implemented by high performance language, such as CUDA,C++.
The following example shows how to model a two-layer neural network step by step and train from scratch In a few lines of Python code.
@ -41,7 +41,7 @@ for i,(x,y) in enumerate(get_data(n)):
pred_y = model(x)
loss = ((pred_y - y)**2)
loss_mean = loss.mean()
optim.step (loss_mean)
optim.step(loss_mean)
print(f"step {i}, loss = {loss_mean.data.sum()}")
```

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README.src.md
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@ -9,9 +9,9 @@ Jittor is a high-performance deep learning framework based on JIT compiling and
Jittor 是一个基于即时编译和元算子的高性能深度学习框架，整个框架在即时编译的同时，还集成了强大的Op编译器和调优器，为您的模型生成定制化的高性能代码。
The front-end language is Python. Module Design is used in the front-end, like PyTorch and Keras. The back-end is implemented py high performance language, such as CUDA,C++.
The front-end language is Python. Module Design is used in the front-end, which is the most popular design for deeplearning framework interface. The back-end is implemented by high performance language, such as CUDA,C++.
Jittor前端语言为Python。前端使用了模块化的设计，类似于PyTorch，Keras，后端则使用高性能语言编写，如CUDA，C++。
Jittor前端语言为Python。前端使用了模块化的设计，这是目前最主流的深度学习框架接口设计。后端则使用高性能语言编写，如CUDA，C++。
The following example shows how to model a two-layer neural network step by step and train from scratch In a few lines of Python code.
@ -46,7 +46,7 @@ for i,(x,y) in enumerate(get_data(n)):
pred_y = model(x)
loss = ((pred_y - y)**2)
loss_mean = loss.mean()
optim.step (loss_mean)
optim.step(loss_mean)
print(f"step {i}, loss = {loss_mean.data.sum()}")
```