What is deep learning?#
Deep learning is a subset of machine learning. You can think of it as an evolution of machine learning or even deeper machine learning.
Deep learning models are meant to analyze data with a similar logical structure to how humans make decisions and draw conclusions. These models are modeled after the human brain, and they enable data to be passed between nodes that mimic neurons. They layer algorithms to create an artificial neural network (ANN) that can learn and make decisions on its own. This design makes deep learning models more capable than standard machine learning models.
Typically, deep learning systems require large datasets to be successful, but once they have data, they can produce immediate results. Once it’s set up, the need for human intervention is very low. An important advancement in the field of deep learning is called transfer learning, which involves the use of pre-trained models. These pre-trained models help fulfill the need for large training datasets.
Let’s take a look at a few examples of deep learning algorithms.
Deep learning algorithms#
Convolutional neural networks (CNNs)
This is a type of neural network that has multiple layers. These layers analyze and extract features from data. CNNs are mainly used for computer vision, image processing, and object detection.
Recurrent neural networks (RNNs)
RNNs use sequential data or time-series data for ordinal or temporal problems. They use training data to learn. Some common use cases of RNNs include Google Translate, image captioning, and Siri.
Artificial neural networks (ANNs)
These are general-purpose neural networks that can be applied to various complex tasks. Feedforward and backpropagation are the two main techniques involved in ANNs. ANNs use the feedforward mechanism to take data through an input node layer and pass it through inner layers until the output node layer is reached. Backpropagation allows the model to learn from its mistakes. Whenever an error is encountered during training, the information is sent back to the previous node to adjust the weights accordingly.
Autoencoders
Autoencoders use neural networks for representation learning. They replicate data from the input layer to the output layer and are used to solve unsupervised learning problems. They’re used for things such as image processing and pharmaceutical research.
What is a neural network?#
Neural networks closely resemble the working of a human brain when it comes to processing and performing tasks. A neural network is made up of nodes in a layered structure. The nodes are connected through synapses, and the number of layers can differ according to the complexity of the problem. They are responsible for learning from the input data and reducing their errors to effectively reach an accurate output.