At the heart of Deep Learning (a subset of Machine Learning) lies Artificial Neural Networks (ANNs). A concept inspired by the human brain’s biological neural networks. Developed to think and make analytical decisions like humans, ANNs allow computers to recognize patterns and solve common problems in various fields of AI.
ANNs are structured to mimic the working of the human brain and its network of neurons. The neuron equivalents in ANNs are called nodes, and the connections between different nodes are called edges. Like the synapses of a biological brain, each node can process and transmit signals. But before we get there, we must know more about these networks, their working, and what practical use we can derive from them.
Diving deep into the network
ANNs are made up of node layers that comprise an input layer, several hidden layers, and an output layer. The input layer receives information or inputs provided by the programmer, the hidden layer processes it, and the output layer produces results. These layers are connected and have associated weights and biases.
If an individual node’s output is above the specified threshold value, that node is activated and sends the data to the next layer of the network. The output of a neuron is determined by combining the weighted sum of its inputs with a bias term. The weighted sum, also known as the activation, is then passed through a non-linear activation function to produce the output.
The initial input data, such as images or documents, are called external data. The desired output data is what we want the system to do with that information, for instance, recognizing an object in an image.
Training the model is key.
Neural networks are trained using data before they can learn how to classify and cluster data quickly and accurately. Once a neural network is trained, it becomes a powerful tool that can be used for a plethora of AI applications. The more data sets you use to train your neural network models, the bigger the neural network grows and becomes more accurate and faster. So if you want to improve the performance of your neural networks, make sure to feed them lots of data sets.
Type of Neural Networks
There are many different types of Neural Networks that are used in the field of Artificial Intelligence. However, these are the four most prominent neural networks because of their widespread applications and foundational roles.
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1. Feed-Forward Neural Networks: Also known as Multi-Layer Perceptrons (MLPs), this neural network forms the foundation of computer vision, natural language processing, and other neural networks. The name Feed-Forward is derived from the flow inputs inside the network, which are only processed in the forward direction. While they are also known as MLPs, they do not use Perceptrons. Instead, this network is composed of Sigmoid Neurons to tackle real-world problems that are mostly non-linear.
2. Recurrent Neural Networks (RNNs): The building block of the RNN is the Recurrent Convolutional Layer (RCL). This network uses sequential data or time series data and has been highly successful for AI applications like Text and Speech Recognition. What sets this network apart is the feedback loops. RNNs are also used to predict future outcomes, such as stock market trends and sales forecasting.
- Language Translation
- Natural Language Processing (NLP)
- Speech recognition (Used in applications like Siri and Google Translate)
- Image captioning
- Convolutional Neural Networks (CNNs): Prevalent in the image and video processing fields, these networks are causing all the rage in the world of Deep Learning. Quite similar to Feed-Forward Networks, CNNs use principles of linear algebra like matrix multiplication to identify patterns within an image. The building blocks of CNNs are kernels that filter that extract relevant information from the inputs using convolutional operations. The superior performance, high speed, and pinpoint accuracy sets them apart from other Networks.
- Face Detection and Verification
- People Detection and Tracking
- Object Detection
- OCR and Document Parsing
- Pose Detection
- ASL Translation
- Intelligent Video Analytics (IVA)
- Emotion Analysis/Tracking
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