

# Task 1: Introduction

Welcome to Basic Image Classification with TensorFlow.

This graph describes the problem that we are trying to solve visually. We want to create and train a model that takes an image of a hand written digit as input and predicts the class of that digit, that is, it predicts the digit or it predicts the class of the input image.



### Import TensorFlow


```python
import tensorflow as tf

tf.logging.set_verbosity(tf.logging.ERROR)
print('Using TensorFlow version', tf.__version__)
```

# Task 2: The Dataset
### Import MNIST


```python

```

### Shapes of Imported Arrays


```python

```

### Plot an Image Example


```python

```

### Display Labels


```python

```


```python

```

# Task 3: One Hot Encoding
After this encoding, every label will be converted to a list with 10 elements and the element at index to the corresponding class will be set to 1, rest will be set to 0:

| original label | one-hot encoded label |
|------|------|
| 5 | [0, 0, 0, 0, 0, 1, 0, 0, 0, 0] |
| 7 | [0, 0, 0, 0, 0, 0, 0, 1, 0, 0] |
| 1 | [0, 1, 0, 0, 0, 0, 0, 0, 0, 0] |

### Encoding Labels


```python

```

### Validated Shapes


```python

```

### Display Encoded Labels


```python

```

# Task 4: Neural Networks

### Linear Equations



The above graph simply represents the equation:

\begin{equation}
y = w1 * x1 + w2 * x2 + w3 * x3 + b
\end{equation}

Where the `w1, w2, w3` are called the weights and `b` is an intercept term called bias. The equation can also be *vectorised* like this:

\begin{equation}
y = W . X + b
\end{equation}

Where `X = [x1, x2, x3]` and `W = [w1, w2, w3].T`. The .T means *transpose*. This is because we want the dot product to give us the result we want i.e. `w1 * x1 + w2 * x2 + w3 * x3`. This gives us the vectorised version of our linear equation.

A simple, linear approach to solving hand-written image classification problem - could it work?



### Neural Networks



This model is much more likely to solve the problem as it can learn more complex function mapping for the inputs and outputs in our dataset.

# Task 5: Preprocessing the Examples

### Unrolling N-dimensional Arrays to Vectors


```python

```

### Display Pixel Values


```python

```

### Data Normalization


```python

```

### Display Normalized Pixel Values


```python

```

# Task 6: Creating a Model
### Creating the Model


```python

```

### Activation Functions

The first step in the node is the linear sum of the inputs:
\begin{equation}
Z = W . X + b
\end{equation}

The second step in the node is the activation function output:

\begin{equation}
A = f(Z)
\end{equation}

Graphical representation of a node where the two operations are performed:



### Compiling the Model


```python

```

# Task 7: Training the Model

### Training the Model


```python

```

### Evaluating the Model


```python

```

# Task 8: Predictions

### Predictions on Test Set


```python

```

### Plotting the Results


```python

```


```python

```

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Basic-Image-Classification-with-TensorFlow/pp/01_Student_Notebook.ipynb
AnuragAnalog/Guided-Projects-Coursera
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2021-04-11T17:04:58.000Z
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AnuragAnalog/Guided-Projects-Coursera
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Apache-2.0
Basic-Image-Classification-with-TensorFlow/pp/01_Student_Notebook.ipynb
AnuragAnalog/Guided-Projects-Coursera
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