Software Components in Deep Learning

Neural Networks

The computational model known as a neural network was influenced by the composition and operation of the human brain. Finding patterns, learning from the data, and making decisions or predictions are its intended goals. Artificial neurons or "units," which join to create neural networks, are structured into layers of interconnected nodes. The three layers that make up a network are typically input, one or more hidden, and output.

Introduction

Each artificial neuron accepts input signals, analyses them, and then emits a signal. This formula calls for adding up all of the inputs because each input is given a weight. The weighted sum is then subjected to the application of an activation function that establishes non-linearities and the output of the neuron.

To enhance their effectiveness during learning, neural networks modify the weights of their connections based on training data. The error between the desired output and the projected output is propagated backward through the network in order to make this adjustment, which is commonly done via the backpropagation approach.

Because they can learn from a lot of data and generalize it, neural networks are powerful. Pattern identification, natural language processing, and picture and speech recognition are among the tasks they excel in. Neural networks have excelled in a number of areas, including computer vision, speech recognition, autonomous cars, and many others.

Networks having numerous hidden layers are referred regarded as deep neural networks, a subclass of neural networks. Deep neural networks have the capacity to learn intricate hierarchical data representations, allowing them to take on more difficult jobs and produce more accurate results.

Numerous industries have seen a transformation thanks to neural networks, which are also advancing artificial intelligence technology. They are an effective tool for resolving complicated issues and stimulating creativity in a variety of fields because of their capacity to automatically learn from data and discover significant patterns.

There are three main essential layers of a neural network are as follows:

• Input layer
• Hidden Layer
• Output Layer

Once the data is given to the input layer it will transform into the other layers by calculating the weighted sum over the input layer. Then applies the Non-Linear function to the transformed data to calculate the intermediate state. 

The General scenario of a Deep Learning neural network working outline is represented in the above figure. Usually, the input data is transformed through the neural network to obtain an output and the result would be good or bad which totally depends on the data given and the parameters and their values assigned while calculating the weighted sum of the data.

Shallow Neural Networks

Shallow neural networks, also known as single-layer neural networks, are a sort of deep learning neural network design with just one hidden layer between the input and output layers.

Compared to deep neural networks, which feature several hidden layers, these networks are very simple. They are often used to solve relatively simple issues with a small number of input features, such as picture recognition or classification tasks.

              

Shallow Neural Network

Shallow neural networks operate by taking in input data, transforming it linearly, and then applying a non-linear activation function to the output. The activation function's output is then transmitted to the output layer, which generates the final output.

While shallow neural networks are not as powerful as deep neural networks, they can nevertheless be useful for specific tasks, especially when the data is not too complicated or when training data is limited.

Advantages:

• Shallow neural networks learn faster than deep neural networks with several hidden layers since they contain only one hidden layer. This makes them an excellent choice when there is a limited amount of training data or when training time is critical.

• Shallow neural networks feature a simpler design than deep neural networks, making them easier to grasp and interpret. This is useful for jobs requiring transparency and explanation, such as financial analysis or medical diagnosis.

• Reduced overfitting risk: Overfitting happens when a model becomes too complicated and begins to fit the noise in the training data, resulting in poor performance on new data. Because shallow neural networks have fewer parameters to train, they are less likely to overfit than deep neural networks.

• Shallow neural networks require less memory to hold their parameters than deep neural networks do. As a result, they are an excellent choice for applications with limited resources, such as embedded systems or mobile devices.

Disadvantages:

• Shallow neural networks have less representational power as compared to deep neural networks with several hidden layers. As a result, individuals could struggle with jobs requiring complicated feature extraction or hierarchical learning.

• Shallow neural networks may have lesser accuracy on complicated tasks than deep neural networks due to their limited representational power.

• The trouble with sequential data: Because shallow neural networks cannot simulate complicated temporal connections, they may struggle with sequential data, such as natural language processing or time-series analysis.

• Difficulties with feature engineering: To extract meaningful features from raw input data, shallow neural networks rely significantly on feature engineering. This might be time-consuming and necessitate domain knowledge.

• Shallow neural networks are less adaptable than deep neural networks since they are limited to a single hidden layer. This can make adapting them to other types of challenges or data difficult.

Deep Neural Networks 

A deep neural network is a form of artificial neural network that contains numerous hidden layers between the input and output layers. These hidden layers enable the network to learn increasingly complex properties and representations of the input data, making it more powerful and versatile than shallow neural networks.

Deep neural networks are frequently utilized in deep learning and in Machine Learning for a variety of tasks such as image and speech recognition, natural language processing, and gameplay. They are especially good at tasks that demand the extraction of complicated features or the modeling of long-term dependencies, such as sequence-to-sequence tasks.

Deep Neural Network(Source: Openclipart)

Deep neural network training often involves the use of backpropagation, which is an optimization method that modifies the network's weights to minimize the difference between expected and actual outputs.

Deep neural networks are more powerful than shallow neural networks, but they are also harder to train and need more processing resources. Recent developments in hardware and software, however, have enabled the training and deployment of deep neural networks at scale, allowing for breakthroughs in disciplines such as computer vision, natural language processing, and robotics.

Advantages:

• Deep neural networks can learn increasingly sophisticated properties and representations of incoming data as they progress through numerous layers. When compared to shallow neural networks, this results in improved accuracy and better performance on numerous tasks.

• Improved feature extraction: Because deep neural networks include numerous layers, they can extract more relevant and abstract features from raw input data without the need for manual feature engineering.

• Deep neural networks are adaptable and can be utilized for a variety of deep learning applications, including image identification, natural language processing, and speech recognition.

• Deep neural networks can generalize effectively to new data, which implies they can perform well even on data that they did not see during training. As a result, they are more robust and helpful in real-world applications.

Disadvantages:

• Deep neural networks require large computer resources for both training and inference, which can be a bottleneck for many applications.

• Deep neural networks require a huge quantity of training data to avoid overfitting and achieve decent performance. This can be difficult for some applications, particularly those with limited data.

• Difficult to comprehend: Because deep neural networks have several layers, they can be difficult to interpret and understand, making it difficult to discover and correct flaws or biases in the model.

• Deep neural networks are frequently regarded as "black box" models, which means that it might be difficult to understand how they make predictions or choices.

Shallow Neural Networks Vs Deep Neural Networks

Category	Shallow Neural Networks	Deep Neural Networks
Depth (Number of hidden layers in each)	Shallow neural networks will have only one hidden layer.	Deep neural networks feature numerous hidden layers between the input and output layers. The number of hidden layers varies according to the purpose and network architecture.
Complexity	Shallow neural networks are simple as they have only one hidden layer and are mostly used in Binary Classification and Regression Problems in which the input features are relatively simple.	Deep neural networks are more complicated than shallow neural networks because they have numerous layers capable of learning increasingly abstract and intricate characteristics of input data.
Feature Extraction	Shallow neural networks can only extract simple features from input data.	Deep neural networks can extract more sophisticated and abstract information across numerous layers.
Generalization	Shallow neural networks are not at generalizing to new data because they cannot learn the more complicated because of the one hidden layer whose ability is limited to learn the complicated and the hierarchical feature of the input data.	Deep neural networks are frequently better than shallow neural networks at generalizing to new data because they can learn more complicated and abstract aspects of the input data.
Training	Shallow neural networks have fewer layers and parameters to learn, and they are frequently easier and faster to train as their data size is small and simpler than deep neural networks.	Deep neural networks are frequently not so easy and fast to train the model.
Performance	Shallow neural networks frequently will not perform complicated tasks such as image and Speech recognition, natural language processing, and gameplay.	Deep neural networks frequently outperform shallow neural networks on complicated tasks such as image and speech recognition, natural language processing, and gameplay.

Key Points to Remember

Here are some essential distinctions between shallow and deep neural networks to keep in mind:

Shallow Neural Networks

• Only one hidden layer separates the input and output layers in shallow neural networks.

• They are frequently referred to as multilayer perceptrons (MLPs) or feedforward neural networks (FNNs).

• When compared to deep neural networks, shallow networks are more straightforward, understandable, and trainable.

• They work well for resolving straightforward to merely somewhat complex issues.

• Shallow networks may have trouble processing large or complicated datasets and have a limited ability to learn complex patterns.

• In terms of training time and computational resources, they are typically less costly computationally than deep networks.

• For applications like picture recognition, text categorization, and straightforward regression issues, shallow networks are frequently utilized.

Deep Neural Networks (DNNs)

• Between the input and output layers of deep neural networks, there are numerous hidden layers.

• By removing features at various levels of abstraction, they are able to learn hierarchical representations of data.

• Deep networks are capable of handling massive, high-dimensional datasets and learning to tackle extremely complicated issues.

• Convolutional neural networks (CNNs) and recurrent neural networks (RNNs), two types of deep learning models, have attained state-of-the-art performance in a number of fields, including speech recognition, natural language processing, and computer vision.

• Due to their higher complexity, deep networks demand more computer resources and training time than shallow networks.

• If the training dataset is small, they may experience overfitting; regularization techniques are frequently employed to address this problem.

• To make use of the representational capability of deep networks, transfer learning—where previously trained deep models are improved on fresh tasks—has become a standard technique.

Conclusion

Shallow neural networks, which have one or a few hidden layers, are easier to understand and use less computing power. They produce good results that are easy to interpret for challenges with simpler patterns and smaller datasets.

Deep neural networks, which include many hidden layers, perform well when learning hierarchical representations for problems with complex patterns. They are computationally more demanding yet have a better modeling capacity. They can address large-scale issues but need larger datasets for efficient training.

Deep networks are frequently employed in computer vision, natural language processing, and other disciplines where it is necessary to understand complicated characteristics and interactions. They can, however, be susceptible to overfitting and call for careful regularization methods.

The decision between shallow and deep networks is influenced by the difficulty of the task, the amount of the dataset, the available computing power, and the need for interpretability. Deep network training and optimization approaches are still being researched in an effort to retain deep networks' efficiency and interpretability.

References:

[1] https://www.mlexpert.io/machine-learning/interview-questions/shallow-and-deep-networks

[2] Wikipedia.com