If you’ve ever used a voice app like Alexa or Siri, you’ve interacted with a neural network. (And of course, you already have your own neural network – in your brain.)
In machine learning, an artificial neural network (ANN) is an information processing system modeled after our brains. ANNs are the bread and butter of the popular method of advanced machine learning known as deep learning. ANNs are more simply called neural networks, and more recently, deep neural networks.
By learning how to harness neural networks, you can apply them to various interesting use-cases, such as natural-language processing (NLP) and computer vision.
Today we’ll talk about neural networks and how you can start working with them in Python.
We’ll cover:
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A neural network represents an artificial intelligence technique that enables computers to analyze data, drawing inspiration from the human brain’s functioning. This approach, which is part of the broader machine learning domain known as deep learning, employs a network of nodes or neurons arranged in layers, mimicking the brain’s structure.
Neural networks are complex structures of machine and deep learning algorithms. A neural network is composed of several information processing units.
The information processing units in ANNs share a name with those in our brains: neurons. However, neurons in an ANN are also called nodes, artificial neurons, or perceptrons.
A perceptron is a machine learning algorithm used for binary classification of data. On its own, a single perceptron constitutes a single-layer neural network, which is the most basic type of neural network.
As a binary classifier, a perceptron can learn linear boundaries between classes, provided it’s given a set of linearly separable training data.
To illustrate, a perceptron uses a linear model to separate a given set of input data into two classes:
This is great, but most real-world data isn’t related through a linear relationship.
To perform more complex tasks, neural networks must be able to learn non-linear representations from real-world data. To do this, we need multiple perceptrons to be interconnected in multi-layer neural networks, or multi-layer perceptrons.
Multi-layer neural networks form the basis for deep learning.
When we talk about neural networks, we’re usually referring to multi-layer neural networks.
There are three types of layers in a multi-layer neural network:
The following figure illustrates a neural network with a single hidden layer:
These hidden layers are where the computation happens. Without hidden layers, a neural network will simply return output data identical to the input data.
Hidden layers are what truly enable deep learning.
While there are various types of neural networks, the most common are:
Convolutional neural networks: These are commonly used to analyze images, and are the masterminds behind image and facial recognition.
Recurrent neural networks (RNNs): These learn from sequential training data. They power speech-recognition apps. One type of RNN is the long short-term memory (LSTM) network, which is the type of neural network behind Google Translate.
Multilayer Perceptrons (MLPs): They are a foundational type of feedforward artificial neural network (ANN). As the simplest form of deep neural networks, MLPs consist of multiple fully connected layers. They offer a solution to the high computational demands of contemporary deep learning models by employing layers that function as nonlinear transformations of weighted sums from their preceding layers.
Neural networks can use different machine learning paradigms, including supervised learning, unsupervised learning, and reinforcement learning.
A neural network can receive unstructured data sets, classify data points, recognize patterns, and develop an internal representation through which it makes predictions about similar data sets.
Like humans, a neural network learns to perfect its craft over time. It goes through several iterations of computations and adjustments until it makes predictions to a reasonable accuracy.
Some of the key computational components in neural networks include:
A neural network’s training process looks like this:
Our goal when training neural networks is to reduce the error or loss, which means that the network’s generated outputs will ideally match the required outputs. There are several types of loss functions, a common one being the cross-entropy loss function, which is typical in classification tasks.
To reduce the loss, we update the weights. At this stage, we don’t use random numbers as our weight assignments. Instead, we use optimization algorithms to determine the changes we need to make.
There are many optimization algorithms used to train neural networks. A popular one is the gradient descent algorithm. Gradient descent is an iterative optimization algorithm.
A commonly used variant of gradient descent is stochastic gradient descent, which is well suited for working with large data sets.
Creating neural networks (NN) is one of the many amazing things you can do with the Python programming language.
On your way to mastering neural networks, you’ll need a few ingredients:
Here are the steps you need to follow to create a neural network in Python:
There are various libraries and modules you can use to start creating neural networks in Python:
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Let’s take a sneak peek at how we implement neural networks in Python.
The following Python code is a simple example of image classification using TensorFlow.
# Import packagesimport tensorflow as tffrom tensorflow.keras.models import Modelfrom tensorflow.keras.utils import plot_model# Build neural network modeldef build_model():# instantiate a Sequential class and linearly stack the layers of your modelseq_model = tf.keras.models.Sequential([tf.keras.layers.Flatten(input_shape=(28, 28)),tf.keras.layers.Dense(128, activation=tf.nn.relu),tf.keras.layers.Dense(10, activation=tf.nn.softmax)])return seq_model# Instantiate the model and plot itmodel = build_model_with_functional()plot_model(model)#Load and prepare datamnist = tf.keras.datasets.fashion_mnist(training_images, training_labels), (test_images, test_labels) = mnist.load_data()training_images = training_images / 255.0test_images = test_images / 255.0# Set optimization algorithmmodel.compile(optimizer=tf.optimizers.Adam(),loss='sparse_categorical_crossentropy',metrics=['accuracy'])# Train and evaluate the modelmodel.fit(training_images, training_labels, epochs=5)model.evaluate(test_images, test_labels)
Image classification models learn to recognize images. They’re one of the common types of neural networks we discussed earlier: a convolutional neural network.
There’s more and more data to work with each day. Why not team up with a neural network in Python to do something big with that data?
To help you master neural networks, we’ve created the learning path: Become a Deep Learning Professional. This path covers deep learning fundamentals and includes severals hands-on tutorials and projects to help you master neural networks in Python.
Whether you are:
… you can harness neural networks to do some amazing things. We can’t wait to see what you do with your own NN.
Happy learning!
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