<figure>
<center>

</center>        
</figure>

# Regression with Automatic Differentiation in TensorFlow

# Task 1: TensorFlow


```python
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline

print('Using TensorFlow version:', tf.__version__)
print('Devices available:', tf.config.list_physical_devices())
```

# Task 2: Constants


```python

```


```python

```


```python

```


```python

```

# Task 3: Variables


```python

```


```python

```


```python

```

# Task 4: Automatic Differentiation

Let's take a simple equation as an example:
\begin{equation}
y = x^3 ; \frac{dy}{dx} = 3x^2
\end{equation}


```python

```

What about higher order gradients?

\begin{equation}
y = x^3 ; \frac{dy}{dx} = 3x^2 ; \frac{d^2 y}{dx^2} = 6x
\end{equation}


```python

```

# Task 5: Watching Tensors


```python

```


```python

```

# Task 6: Persistent Tape

\begin{equation}
y = x^3 ; z = 2y ; \frac{dz}{dx} = \frac{dz}{dy} . \frac{dy}{dx}
\end{equation}


```python

```

# Task 7: Generating Data for Linear Regression

Solve a simple linear equation:

\begin{equation}
y = wx + b
\end{equation}


```python

```


```python

```

# Task 8: Linear Regression


```python

```


```python

```

0a7d434ea1ed62a014aaf1fbbe9e8c279bf4e730
ipynb
Jupyter Notebook
coding-exercise/week2/part2/mini_project(1)_question.ipynb
Aye-Nyein-Thaw/TensorFlow-Beginner
a2a8be7cc14b1c14b4f5b0d4284de5acf7f61fd3
MIT
2020-10-07T07:05:49.000Z
2020-11-04T07:44:22.000Z
coding-exercise/week2/part2/mini_project(1)_question.ipynb
Aye-Nyein-Thaw/TensorFlow-Beginner
a2a8be7cc14b1c14b4f5b0d4284de5acf7f61fd3
MIT
coding-exercise/week2/part2/mini_project(1)_question.ipynb
Aye-Nyein-Thaw/TensorFlow-Beginner
a2a8be7cc14b1c14b4f5b0d4284de5acf7f61fd3
MIT
2020-10-07T08:02:43.000Z
2020-11-14T08:51:21.000Z
Qwen/Qwen-72B
1. YES
2. YES