Introduction to Neural Networks: Building Blocks of Deep Learning
Autonomous vehicles, ChatGPT, Alexa or Siri, and other recent advancements in technology have one thing in common, Neural Networks. Artificial Intelligence and Machine Learning algorithms make up for most breakthroughs in technology and Neural networks are at the crux of these applications. Neural networks mimic the human brain network. The human brain is deemed the most complex thing in the universe, and neural networks take this complexity a notch higher by being integrated into most applications in every market segment in today’s digital world.
The most basic computational unit of any neural network, including the brain, is the neuron. A neural network has three layers — the input layer, the output layer, and a hidden layer between them. If the network comprises multiple hidden layers to process data, such models are termed deep neural networks. Most deep neural networks are feed-forward, they only flow in one direction from input to output. Backpropagation allows us to calculate and attribute the error associated with each neuron, allowing us to adjust and fit the algorithm appropriately.
Neural networks form the backbone of deep learning methods. Neurons in these networks take input, process it and pass it on to other hidden layers of the network till the processed output reaches the output layer. The multiple layers of abstraction give the deep networks a compelling advantage to solve complex pattern recognition problems.
Activation function
An activation function, also called a transfer function determines the kind of output in a neural network. It defines if a neuron in the hidden layer should be activated or not. Each neuron has an associated weight and threshold. If the output of any neuron is above the specified threshold value, that neuron is activated, sending data to the next layer of the network.
Components of a typical neural network involve neurons, connections known as synapses, weights, biases, propagation function, and a learning rule. Neurons will receive input from predecessor neurons that have a threshold, an activation function, and an output function. Connections consist of weights and biases that indicate how neurons transfer output. Propagation computes the input and the output. It also sums the predecessor neurons’ function with weight. The learning refers to the adjustment in the weights and bias parameters.
Linear and non-linear functions are two broad categories of activation functions for neural networks.
Neural networks learn through supervised learning. Supervised machine learning involves an input variable and a corresponding desired output variable. The goal is to model the underlying structure of the data to understand more about the data. The advantage of deep learning is that the program builds the feature set by itself without supervision.
Types of neural network
Neural networks can recognize images at a pixel level which is almost impossible for humans. The input data culminates varietal raw data to produce output representing different labels. A neural network with only one hidden layer, often used for a simple task is called a shallow network. Applications for composite tasks require multiple layers, and these representations manifest through different neural network types as discussed below.
Artificial Neural Network (ANN)
Artificial neural networks are standard neural networks designed to learn by adjusting the weights in response to input data. These networks are trained using a technique called back-propagation, which involves adjusting the weights of the connections between the nodes. As a result, they are capable of performing complex tasks such as pattern recognition and predictions.
Multilayer Perceptron (MLP)
A type of feed-forward neural network with three or more hidden layers that use nonlinear activation functions. They are fully connected networks where every neuron is connected to every other neuron which might lead to over-fitting of data. MLP is commonly used for Data compression and Prediction applications.
Convolutional neural network (CNN)
Processing in CNN is through a grid-like structure composed of a series of convolutional layers. The output of the convolutional layers is flattened into a one-dimensional vector, which is then passed to the fully connected layers. CNNs are used for a variety of computer vision applications, as they show effective results in identifying people in pictures and videos. They are popular deep learning models for medical imaging, semantic parsing, and self-driving cars.
Recurrent neural network (RNN)
RNNs are deep neural networks that store information from previous computations and pass it forward in a sequential manner. RNN exhibits dynamic behavior over time. RNNs are primarily used for tasks involving time series prediction, such as stock market movements or weather patterns. RNN is also popular for tasks like speech recognition, image tagging, and machine translation.
Long short-term memory (LSTM)
LSTMs are considered an improvement over RNN, as they can understand and learn from context. LSTM stores information for long periods. Unlike traditional neural networks, LSTMs can remember long-term dependencies and also be trained on relatively small datasets. It uses memory cells and gates to selectively read, write, and erase information. The widespread application of LSTM includes and is not limited to anomaly detection, video classification, predicting stock prices, and generating music.
Generative Adversarial Networks (GAN)
GAN is used to generate new data from the training data. It consists of a generator that creates new data and a discriminator that differentiates between the real and generated data. GAN is especially useful for gaming and animation applications, which require the generation of synthetic data.
Autoencoders
Autoencoders are used to learn efficient representations of data. A stacked autoencoder is a type of autoencoder where the input is first encoded by one neural network, and the output is passed as input to another encoder network. This can be beneficial for tasks such as image recognition, where a deep understanding of the data is for accurate predictions.
Variational autoencoders are different from stacked autoencoders, as they learn from a probability distribution while stacked encoders learn a compressed version of input data. Variational autoencoder is used for applications such as image generation and text generation.
No matter what function we want to compute, and what output we need, there is a neural network that can do the job. In contrast to task-based algorithms, deep learning systems learn from data representations. It can also learn from unstructured or unlabeled data.
Processing in Deep Neural Network
In deep neural networks, the error or loss function obtained during processing needs to be minimized. This is done by tuning the weights and biases to get optimal values leading to accurate results. The image below shows an example of how deep neural networks process different animal input images. The deep learning algorithm learns from the errors, tunes the free parameters i.e., weight and bias, and classifies the input image as a dog accurately.
Several Python libraries are available to implement deep neural networks. Some of the popular libraries include NumPy for data cleaning/manipulation, Pandas for Data alignment/Indexing, Scikit-Learn for Data classification/Model Selection, SciPy for Data Visualization/Manipulation, Keras for Data Pooling/building models, and so on. Once the deep learning algorithm is fine-tuned for accuracy, complex tasks like speech recognition or image recognition take seconds versus hours, when compared to the manual processes by domain experts.
Deep learning and machine learning
Deep learning is a subset of machine learning. Due to the effectiveness of deep networks in providing accurate results for complex tasks, their application requires high-end machines to identify significant features. This requires less manual intervention, but more time for the algorithms to train. Machine learning applications, on the other hand, need human experts to lay the foundation, and identify the features to train. ML algorithms take lesser time to train but more time to test depending on the data set.
Neural networks are capable of deep learning. Deep neural networks can learn to recognize patterns that are too difficult for humans to discern. Neural networks require a large amount of training data and can be slow to train. This data is error-prone and is also tough to comprehend and analyze.
Deep learning algorithms can be effectively utilized by employing large, well-organized, high-quality data sets for the network to accurately identify patterns. The allocation of appropriate computation resources, neural network architecture, and training context will lead to consistency and higher accuracy in the result predictions.
Conclusion
Neural networks are robust deep learning models capable of synthesizing large amounts of data in seconds. Neural network algorithms have set a performance benchmark in every industry vertical for their precision, and reduced manual efforts applicable for most composite real-world tasks. Deep neural networks are powerful tools in data science and analytics to classify and cluster data at accelerated rates. The more deep learning algorithms learn, the better they perform.
