Understanding Popular Algorithms in Deep Learning
Artificial intelligence has undergone a revolution thanks to deep learning. Deep learning allows machines to learn from vast amounts of data and carry out complex tasks that were previously only considered possible by humans (like translation between languages, recognizing objects etc.).
Deep learning is based in algorithms, which are mathematical operations that let computers learn from data and get better over time. In this article, we will examine some different popular deep learning algorithms and how they operate.
Neural Networks
Neural networks are a popular deep learning algorithm that are inspired by the structure and function of the human brain. They have several use cases, from image recognition to natural language processing and self-driving vehicles.
A neural network is a collection of connected neurons, also known as nodes, that are organized in layers. Each of the nodes gets an input, processes it, and produces an output. Training a neural network involves adjusting the biases and weights of the connections between the nodes so that the output of the network is close to the desired output, this is achieved by feeding the network with a set of training data and adjusting the weights and biases.
How neural networks work
What a neural network does is learn the patterns in the input data and then map it to the desired output. Each node in the network carries out a mathematical operation on the input it received, and then the output of the node is passed on to the other nodes in the next layer. Weights are used to determine the strength of the connection between nodes and the weights are tuned during the training so that the network can learn to give the desired output for a particular input.
To train a neural network you give the network a set of data and it compares the output with the desired output, the difference between the two will be known as an error which will be used in adjusting the weights till the outputs align.
Types of Neural Networks
Convolutional neural networks (CNNs)
Convolutional neural networks are a type of neural network that is perfect for video and image processing tasks. A convolutional neural network uses a series of various convolutional layers to get features from the input data, and the convolutional layers also use a set of filters known as kernels to carry out a series of convolutions on the input data. Then, the output of each of the layers is passed through a nonlinear activation function such as a ReLU function before it is then passed on the next layer.
The CNN architecture consists of various layers which involve convolutional layers, pooling layers, and fully connected layers, the convolutional layers extract the features from the input data while the pooling layers reduce the spatial dimensionality of the output of the convolutional layers. The fully connected layer then classifies the input data based on the features that were extracted by the convolutional layers.
The convolutional neural network works by learning a set of filters that can be used to extract features from the input data. During the training phase, the network adjusts the weights of the filters to minimize the differences between the predicted output and the actual output. The process of adjusting the weights is called back-propagation.
Use Cases:
- Object detection
- Image segmentation
- Video analysis
- Images restoration
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Recurrent neural networks (RNNs)
A recurrent network is a type of neural network that is good for sequential data processing tasks. A recurrent neural network is designed to handle data like time-series data or natural language processing texts.
RNNs use a hidden state which allows them to update at each step and the hidden state is passed through the network to allow the processing of a sequence of varying length. The architecture of the RNN consists hidden layers that have a connection to both the input and output layered. The hidden layer is meant to maintain a memory of the input sequence and this memory is updated each time step as the network receives new input.
A recurrent neural network uses a series of weights and biases to update the hidden state at every time step. The hidden state is represented as a vector and is updated base on the current input and the previous hidden state. In a language modeling task, the input vector might represent a single word in a sentence. The output vector can then be used to make predictions or classifications based on the input sequence.
Use Cases:
- Language modeling
- Time-series analysis
- Speech recognition
- Sentiment analysis
Generative Adversarial Network
Generative Adversarial Networks are a type of neural network that are used to generate new data samples that are similar to a given dataset. A Generative Adversarial Network has two sub-networks: a generator network and a discriminator network. The generator network is responsible for generating new sample data while the discriminate network is for evaluating whether a given data sample is either real or fake. During training, the generator network learns to generate more realistic data samples that are more difficult for the discriminate network to identify as fake.
GANs work by training the generator and the discriminator networks at the same time using a process known as adversarial training. In each training iteration, the generator generates a batch of new data samples which will then be evaluated by the discriminator network. The discriminator network is trained on both fake and real data samples to aid its ability to differentiate between the two.
Use Cases:
- Image synthesis
- Video synthesis
- Data augmentation
- Style transfer
Conclusion
As a result of allowing machines to learn from data and carry out complex tasks that were previously considered to be only humanly possible, deep learning algorithms have revolutionized the field of artificial intelligence.
Among the methods used in deep learning are neural networks, convolutional neural networks, recurrent neural networks, and generative adversarial networks. It is possible that new and more complex algorithms will be created as the field develops, producing even more remarkable systems.
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