# python-tensorflowHow can I compare and contrast Python TensorFlow and PyTorch?

Python TensorFlow and PyTorch are two of the most popular deep learning frameworks. They are both open source and used for a variety of tasks, including natural language processing, computer vision, and reinforcement learning.

The main difference between the two frameworks is the way they handle computation. TensorFlow uses a static computational graph, meaning that the graph is defined ahead of time and computations are performed on the graph. PyTorch, on the other hand, uses a dynamic computational graph, meaning that the graph is defined as the computation is performed.

TensorFlow also supports distributed computing, allowing it to be used for large-scale projects. PyTorch is more suited for smaller projects, as it does not have the same support for distributed computing.

In terms of speed, PyTorch is generally faster than TensorFlow. This is because PyTorch does not need to build a static graph, and is optimized for fast computation.

In terms of usability, TensorFlow is generally easier to use than PyTorch. It has a larger community and more tutorials available, making it easier to get started.

Example code of TensorFlow:

```
import tensorflow as tf
# Create a constant op
# This op is added as a node to the default graph
hello = tf.constant("Hello, TensorFlow!")
# start a TF session
sess = tf.Session()
# run the op and get result
print(sess.run(hello))
```

## Output example

`Hello, TensorFlow!`

Example code of PyTorch:

```
import torch
# Construct a 5x3 matrix, uninitialized:
x = torch.empty(5, 3)
print(x)
```

## Output example

```
tensor([[1.1390e-36, 0.0000e+00, 3.3631e-44],
[0.0000e+00, nan, 0.0000e+00],
[1.1578e+27, 1.1362e+30, 7.1547e+22],
[4.5828e+30, 1.2121e+04, 7.1846e+22],
[9.2198e-39, 7.0374e+22, 0.0000e+00]])
```

Overall, both TensorFlow and PyTorch are powerful deep learning frameworks, and which one to use will depend on the specific task and the user's preferences.

## Helpful links

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