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By Kevin McAleer, 2 Minutes
Deep Learning is a subfield of machine learning that uses algorithms inspired by the structure and function of the brain’s neural networks. Python provides libraries like TensorFlow and Keras which makes it a great language for deep learning. This lesson will introduce the basics of deep learning and how to use TensorFlow and Keras for creating deep learning models.
Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example. It is a key technology behind driverless cars, enabling them to recognize a stop sign, or to distinguish a pedestrian from a lamppost.
TensorFlow is a free and open-source software library for machine learning and artificial intelligence. It can be used across a range of tasks but has a particular focus on training and inference of deep neural networks.
import tensorflow as tf
# Create a constant tensor
hello = tf.constant('Hello, TensorFlow!')
# To access a Tensor value, call numpy().
Keras is a user-friendly neural network library written in Python. It is capable of running on top of TensorFlow. Keras makes it really for ML beginners to build and design a neural network.
from keras.models import Sequential
from keras.layers import Dense
# Define the model
model = Sequential()
# Add model layers
model.add(Dense(units=64, activation='relu', input_dim=100))
# Compile the model
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
In this lesson, you’ve learned about the basics of deep learning and its relation to artificial neural networks. We introduced TensorFlow, a library for large-scale machine learning, and Keras, a high-level neural networks API that runs on top of TensorFlow. These powerful tools open up a wide range of possibilities for training deep learning models.