Deep Learning vs Neural Networks
There are many different concepts and techniques that make up the fields of artificial intelligence (AI) and machine learning (ML). Two such concepts are deep learning and neural networks.
Let’s properly define each one before diving deeper:
- Deep Learning: A subset of machine learning, deep learning eliminates some of the data preprocessing that is usually involved with ML. Deep learning algorithms can process unstructured data, and put simply, it is a way to automate predictive analytics.
- Neural Networks: Neural networks are also a subset of machine learning and are fundamental to deep learning algorithms. Inspired by the human brain, they are composed of various layers that rely on training data to improve their accuracy over time.
What is Deep Learning?
Deep learning attempts to mimic the human brain by enabling systems to cluster data and make incredibly accurate predictions. It is a subset of machine learning that trains a computer to perform human-like tasks, such as speech recognition or image identification. Through deep learning, systems can improve their abilities to classify, recognize, detect, and describe using data.
Deep learning plays a big role in many of today’s technologies, such as Alexa and Siri. It involves data training a computer through deep algorithms to learn autonomously by recognizing patterns using layers of processing.
Unlike classical machine learning, which usually leverages structured and labeled data to make predictions, deep learning can use unstructured data. This means a lot of the data pre-processing typically involved with machine learning is eliminated. Deep learning algorithms ingest and process this data, which can include things like text and images, and they automate feature extraction. All of this means deep learning relies less on humans than other methods.
Deep learning algorithms also use the processes of gradient descent and backpropagation to become more accurate. This also enables them to make predictions based on new data they have never encountered.
Deep learning models can carry out different types of learning methods. For example, they can undergo unsupervised learning, which doesn’t require labeled datasets. This learning technique enables the models to detect patterns in data and cluster them by certain characteristics, all without the help of human supervision.
What are Neural Networks?
Neural networks make up the process of machine learning, and they are what enable computer programs to recognize patterns and solve problems in the fields of AI, machine learning, and deep learning.
Often referred to as artificial neural networks (ANNs), neural networks are fundamental to deep learning. Inspired by the human brain, their structure mimics biological neurons.
Neural networks have node layers that contain an input layer, one or more hidden layers, and an output layer. Each artificial neuron, or node, connects to another. Neural networks rely on training data to learn and improve their predictions over time, which is what allows them to be used for a variety of applications.
It’s also important to note that there are a few different types of neural networks:
- Artificial neural networks (ANNs): One of the most common types of deep learning networks, ANNs are biologically inspired computational networks consisting of three or more layers. They are used to solve a wide range of problems involving speech recognition, text translation, and much more.
- Convolutional Neural Networks (CNNs): Another type of deep learning networks are CNNs, which are especially useful for computer vision and image recognition tasks. Superior to other neural networks, CNNs are incredibly efficient with image, audio signal, or speech inputs. They rely on three main types of layers: the convolutional layer, the pooling layer, and the fully-connected (FC) layer.
- Recurrent Neural Networks (RNNs): One more main type of deep learning networks, RNNs use sequential data or time series data to solve problems related to language translation and natural language processing (NLP).
Key Differences Between Deep Learning and Neural Networks
Despite deep learning incorporating neural networks within its architecture, there’s a stark difference between the two.
Besides being defined differently, there is also a major difference in their structures.
Some of the main components of a neural network include:
- Neurons: A mathematical function designed to simulate the functioning of a biological neuron. It computes the weighted average of the data input and relays information through a nonlinear function.
- Connection and Weights: Connections connect a neuron in one layer to another neuron in either the same layer or a separate layer. A weight value is connected to each connection, and it represents the strength of the connection between the units.
- Propagation Function: Neural networks consist of two propagation functions. The first is forward propagation, which delivers the “predicted value.” The second is backward propagation, which delivers the “error value.”
- Learning Rate: The learning rate of a neural network determines how quickly or slowly the weight values of the model will be updated.
Some of the main components of a deep learning model include:
- Motherboard: Deep learning models are powered by the motherboard chipset.
- Processors: Deep learning models require GPUs based on the number of cores and cost of the processor.
- RAM: Deep learning algorithms demand high CPU usage and stage area, and they require massive amounts of RAM.
- PSU: Because of the high memory demands, it is important for deep learning models to employ a large PSU that can handle the complex functions.
Some more key differences between neural networks and deep learning include the time required to train the network. Neural networks require less time than deep learning models to train the network. Deep learning models are also more accurate than neural networks, and they show higher performance.
The concepts of deep learning and neural networks are fundamental to today’s artificial intelligence technologies. They help automate intellectual tasks that were once performed by humans. And in today’s digital world, AI is being used by companies of all sizes and for all types of tasks, which are being carried out far more efficiently than humans could achieve alone.