The key topics discussed in this course are summarized below. The overview material is available for download at the end of this page.

### Instalation
```
pip install tensorflow
```
### Import
```python
# Import the TensorFlow library with the alias tf
import tensorflow as tf
```

# Simple Tensor Creation

```python
# Create a 1D tensor
tensor_1D = tf.constant([1, 2, 3])

# Create a 2D tensor
tensor_2D = tf.constant([[1, 2, 3], [4, 5, 6]])

# Create a 3D tensor
tensor_3D = tf.constant([[[1, 2], [3, 4]], [[5, 6],[7, 8]]])
```

# Tensor Properties

- **Rank**: it tells you the <u>number of dimensions</u> present in the tensor. For instance, a <u>matrix</u> has a rank of <u>2</u>. You can get the rank of the tensor using the `.ndim` attribute:

```python
print(f'Rank of a tensor: {tensor.ndim}')
```

- **Shape**: this describes how many values exist in each dimension. A 2x3 matrix has a shape of `(2, 3)`. The length of the shape parameter matches the tensor's rank <u>(its number of dimensions)</u>. You can get the the shape of the tensor by the `.shape` attribute:
  
```python
print(f'Shape of a tensor: {tensor.shape}')
```

- **Types**: Tensors come in various data types. While there are many, some common ones include `float32`, `int32`, and `string`. You can get the the data type of the tensor by the `.dtype` attribute:

```python
print(f'Data type of a tensor: {tensor.dtype}')
```

# Applications of Tensors

- **Table Data**

# Tensor Creation Methods

```python
# Create a 2x2 constant tensor
tensor_const = tf.constant([[1, 2], [3, 4]])

# Create a variable tensor
tensor_var = tf.Variable([[1, 2], [3, 4]])

# Zero tensor of shape (3, 3)
tensor_zeros = tf.zeros((3, 3))

# Ones tensor of shape (2, 2)
tensor_ones = tf.ones((2, 2))

# Tensor of shape (2, 2) filled with 6
tensor_fill = tf.fill((2, 2), 6)

# Generate a sequence of numbers starting from 0, ending at 9
tensor_range = tf.range(10)

# Create 5 equally spaced values between 0 and 10
tensor_linspace = tf.linspace(0, 10, 5)

# Tensor of shape (2, 2) with random values normally distributed 
tensor_random = tf.random.normal((2, 2), mean=4, stddev=0.5)

# Tensor of shape (2, 2) with random values uniformly distributed 
tensor_random = tf.random.uniform((2, 2), minval=-2, maxval=2)
```

# Convertions


- NumPy to Tensor
```python
# Create a NumPy array based on a Python list
numpy_array = np.array([[1, 2], [3, 4]])

# Convert a NumPy array to a tensor
tensor_from_np = tf.convert_to_tensor(numpy_array)
```

- Pandas to Tensor
```python
# Create a DataFrame based on dictionary
df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})

# Convert a DataFrame to a tensor
tensor_from_df = tf.convert_to_tensor(df.values)
```

- Constant Tensor to a Variable Tensor
```python
# Create a variable from a tensor
tensor = tf.random.normal((2, 3))
variable_1 = tf.Variable(tensor)

# Create a variable based on other generator
variable_2 = tf.Variable(tf.zeros((2, 2)))
```

```python
# Creating a tensor of type float16
tensor_float = tf.constant([1.2, 2.3, 3.4], dtype=tf.float16)

# Convert tensor_float from float32 to int32
tensor_int = tf.cast(tensor_float, dtype=tf.int32)
```

# Arithmetic

- Addition
```python
c1 = tf.add(a, b)  
c2 = a + b

# Changes the object inplace without creating a new one
a.assign_add(b)
```

- Subtraction
```python
c1 = tf.subtract(a, b)  
c2 = a - b 

# Inplace substraction
a.assign_sub(b)
```

- Element-wise Multiplication
```python
c1 = tf.multiply(a, b)  
c2 = a * b
```

- Division
```python
c1 = tf.divide(a, b)  
c2 = a / b 
```

# Linear Algebra

- Matrix Multiplication
```python
product1 = tf.matmul(matrix1, matrix2)
product2 = matrix1 @ matrix2
```

- Matrix Inversion
```python
inverse_mat = tf.linalg.inv(matrix)
```

- Transpose
```python
transposed = tf.transpose(matrix)
```

- Dot Product
```python
# Dot product along axes
dot_product_axes1 = tf.tensordot(matrix1, matrix2, axes=1)
dot_product_axes0 = tf.tensordot(matrix1, matrix2, axes=0)
```

```python
# Create a tensor with shape (3, 2)
tensor = tf.constant([[1, 2], [3, 4], [5, 6]])

# Reshape the tensor to shape (2, 3)
reshaped_tensor = tf.reshape(tensor, (2, 3))
```

```python
# Create a tensor
tensor = tf.constant([[1, 2, 3], [4, 5, 6], [7, 8, 9]])

# Slice tensor to extract sub-tensor from index (0, 1) of size (1, 2)
sliced_tensor = tf.slice(tensor, begin=(0, 1), size=(1, 2))

# Slice tensor to extract sub-tensor from index (1, 0) of size (2, 2)
sliced_tensor = tf.slice(tensor, (1, 0), (2, 2))
```

```python
# Create a tensor
tensor = tf.Variable([[1, 2, 3], [4, 5, 6], [7, 8, 9]])

# Change the entire first row 
tensor[0, :].assign([0, 0, 0])

# Modify the second and the third columns 
tensor[:, 1:3].assign(tf.fill((3,2), 1))
```

```python
# Create two tensors
tensor1 = tf.constant([[1, 2, 3], [4, 5, 6]])
tensor2 = tf.constant([[7, 8, 9]])

# Concatenate tensors vertically (along rows)
concatenated_tensor = tf.concat([tensor1, tensor2], axis=0)

# Concatenate tensors horizontally (along columns)
concatenated_tensor = tf.concat([tensor3, tensor4], axis=1)
```

```python
# Calculate sum of all elements
total_sum = tf.reduce_sum(tensor)

# Calculate mean of all elements
mean_val = tf.reduce_mean(tensor)

# Determine the maximum value
max_val = tf.reduce_max(tensor)

# Find the minimum value
min_val = tf.reduce_min(tensor)
```

```python
# Define input variables
x = tf.Variable(tf.fill((2, 3), 3.0))
z = tf.Variable(5.0)

# Start recording the operations
with tf.GradientTape() as tape:
    # Define the calculations
    y = tf.reduce_sum(x * x + 2 * z)
    
# Extract the gradient for the specific inputs (x and z)
grad = tape.gradient(y, [x, z])

print(f"The gradient of y with respect to x is:\n{grad[0].numpy()}")
print(f"The gradient of y with respect to z is: {grad[1].numpy()}")
```

# @tf.function

```python
@tf.function
def compute_gradient_conditional(x):
    with tf.GradientTape() as tape:
        if tf.reduce_sum(x) > 0:
            y = x * x
        else:
            y = x * x * x
    return tape.gradient(y, x)

x = tf.constant([-2.0, 2.0])
grad = compute_gradient_conditional(x)
print(f"The gradient at x = {x.numpy()} is {grad.numpy()}")
```

What role does a loss function play in a neural network?

Dive deep into the world of TensorFlow with our course, designed to give you a robust understanding of its core components. Begin with an exploration of tensors and the basics of TensorFlow framework. By the course's end, you'll have honed the skills to build tensor-driven systems, including crafting a basic neural network. Equip yourself with the knowledge to harness TensorFlow's full potential and set the foundation for advanced deep learning pursuits.

You will gain a foundational understanding of TensorFlow's primary components - Tensors. You'll delve into the nature and applications of tensors, familiarize yourself with tensor properties, and acquire knowledge in essential mathematical operations.

You'll learn how TensorFlow operates and the ways to improve its performance. By the end of this module, you'll be well-equipped to implement basic neural networks or other tensor calculations, using only the TensorFlow library without any extras.

Introduction to TensorFlow

Summary

Tensorflow Set Up

Instalation

Import

Tensor Types

Simple Tensor Creation

Tensor Properties

Tensor Axes

Applications of Tensors

Batches

Tensor Creation Methods

Convertions

Data Types

Arithmetic

Broadcasting

Linear Algebra

Reshape

Slicing

Modifying with Slicing

Concatenating

Reduction Operations

Gradient Tape

@tf.function