TensorFlow is a powerful library for numerical computation, particularly well suited and fine-tuned for large–scale Machine Learning ( but you could use it

TensorFlow is a powerful library for numerical computation, particularly well suited and fine-tuned for large–scale Machine Learning ( but you could use it for anything else that requires heavy calculations). The Google Brain team developed it, and it powers many of Google’s large-scale services, such as Google cloud speech, Google Photos, and Google Search. It was open-sourced in November 2015, and it is now the most popular Deep Learning library ( in terms of citations in papers, adoption in companies, stars on GitHub, etc.). Countless projects use Tensorflow for all sorts of Machine Learning tasks, such as image classification, natural language processing, recommender systems, and time series forecasting. TensorFlow Tutorial Before moving forward, I will import some libraries, that we need to operate with TensorFlow.

```
# TensorFlow ≥2.0 is required
import tensorflow as tf
from tensorflow import keras
assert tf.__version__ >= "2.0"
# Common imports
import numpy as np
import os
# to make this notebook's output stable across runs
np.random.seed(42)
tf.random.set_seed(42)
# To plot pretty figures
%matplotlib inline
import matplotlib as mpl
import matplotlib.pyplot as plt
mpl.rc('axes', labelsize=14)
mpl.rc('xtick', labelsize=12)
mpl.rc('ytick', labelsize=12)
```

The API of TensorFlow revolves around tensors, which flow from operation to operation, hence the name TensorFlow. A tensor is usually a multidimensional array (exactly like a numpy ndarray), but it can hold a scalar ( a simple value such as 42). These tensors will be important when we create custom cost functions, custom metrics, custom layers, and more, so let’s see how to create and manipulate them.

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