Understanding Neural Networks

Understanding Neural Networks

What is a Neural Network?

Neural Network (NN), also known as Artificial Neural Network (ANN), is the fundamental component of Deep Learning. It is a computational model inspired by the human brain and consists of interconnected nodes, or artificial neurons, organized into layers. These layers typically include an input layer, one or more hidden layers, and an output layer. Neural networks are designed to process data and extract patterns, making them suitable for a wide range of tasks. These can be used for various tasks such as classification, regression, segmentation, and so on.

Basic Structure of a Neural Network

Basic structure of a Neural Network

Neural networks consist of layers of interconnected nodes, also called neurons or artificial neurons. These layers are organized into three main types:

Input Layer: This layer receives the initial data, such as the features of an image or text.

Hidden Layers: These are one or more layers in between the input and output layers. They process the input data through weighted connections and apply activation functions to produce intermediate results.

Output Layer: This layer produces the final results or predictions, which could be a classification, regression, or any other relevant output.

Each connection between neurons is known as an edge and has a weight associated with it, which is adjusted during the training process to optimize the network's performance.

How does a Neural Network work?

Input Data: The neural network receives input data, which is typically pre-processed and normalized to ensure that the network can work effectively.

Forward Propagation: The input data is multiplied by the weights of the connections and passed through activation functions in each neuron of the hidden layers. This process is called forward propagation, and it results in an output from the output layer.

Activation Function: An activation function introduces non-linearity into the network. Common activation functions include the sigmoid, ReLU (Rectified Linear Unit), and tanh functions. These functions help the network learn complex patterns and relationships in the data.

Loss Function: The output from the neural network is compared to the desired output using a loss function, which measures the error or the difference between the predicted and actual values.

Backpropagation: The network uses the calculated loss to update the weights of the connections in a process called backpropagation. This helps the network learn and adjust its parameters to reduce the error.

Training: The forward and backward propagation steps are repeated over many iterations, adjusting the weights each time, until the network converges to a state where the error is minimized.

Prediction: Once trained, the neural network can be used for making predictions on new, unseen data.

Types of Neural Networks

Some commonly used categories of Neural Networks are:


The Perceptron model, proposed by Minsky-Papert, is one of the simplest and oldest models of Neuron. It is the smallest unit of the neural network that does certain computations to detect features or business intelligence in the input data. It accepts weighted inputs and applies the activation function to obtain the output as the final result. Perceptron is also known as TLU (threshold logic unit).

Use: Perceptrons are used for simple binary classification tasks, such as spam detection, medical diagnosis, quality control, and more.

Feed Forward Neural Nets

The simplest form of neural networks where input data travels in one direction only, passing through artificial neural nodes and exiting through output nodes. Here, the hidden layers may or may not be present. These can be further classified as a single-layered or multi-layered feed-forward neural network.

Number of layers depends on the complexity of the function. It has unidirectional forward propagation but no backward propagation. Weights are static here. An activation function is fed by inputs which are multiplied by weights. To do so, a classifying activation function or step activation function is used. For example: The neuron is activated if it is above the threshold (usually 0) and the neuron produces 1 as an output. The neuron is not activated if it is below the threshold (usually 0) which is considered as -1. They are fairly simple to maintain and are equipped to deal with data that contains a lot of noise.

Use: Suitable for tasks like regression and classification, where data flows in one direction, and there is no feedback loop.

Multi-layer Perceptron

An entry point towards complex neural nets where input data travels through various layers of artificial neurons. Every single node is connected to all neurons in the next layer which makes it a fully connected neural network. Input and output layers are present having multiple hidden Layers i.e., at least three or more layers in total. It has a bi-directional propagation i.e., forward propagation and backward propagation.

Inputs are multiplied with weights and fed to the activation function and in backpropagation, they are modified to reduce the loss. In simple words, weights are machine learned values from Neural Networks. They self-adjust depending on the difference between predicted outputs vs training inputs. Nonlinear activation functions are used followed by SoftMax as an output layer activation function.

Use: Suitable for Speech Recognition, Machine Translation and Complex Classification

Convolution Neural Networks

Convolution Neural Network (CNN) contains a three-dimensional arrangement of neurons instead of the standard two-dimensional array. The first layer is called a convolutional layer. Each neuron in the convolutional layer only processes the information from a small part of the visual field. Input features are taken in batch-wise like a filter. The network understands the images in parts and can compute these operations multiple times to complete the full image processing. Processing involves the conversion of the image from RGB or HSI scale to grey scale. Furthering the changes in the pixel value will help to detect the edges and images can be classified into different categories.

Propagation is unidirectional where CNN contains one or more convolutional layers followed by pooling and bidirectional where the output of the convolution layer goes to a fully connected neural network for classifying the images as shown in the above diagram. Filters are used to extract certain parts of the image. In MLP the inputs are multiplied with weights and fed to the activation function. Convolution uses RELU and MLP uses a nonlinear activation function followed by SoftMax. Convolution neural networks show very effective results in image and video recognition, semantic parsing and paraphrase detection.

Use: Widely used for image recognition, object detection, facial recognition, and image generation tasks.

Recurrent Neural Networks

Designed to save the output of a layer, a Recurrent Neural Network (RNN) is fed back to the input to help in predicting the outcome of the layer. The first layer is typically a feed-forward neural network followed by a recurrent neural network layer where some information it had in the previous time-step is remembered by a memory function. Forward propagation is implemented in this case. It stores information required for its future use. If the prediction is wrong, the learning rate is employed to make small changes. Hence, it gradually increases towards making the right prediction during the backpropagation.

Use: Ideal for tasks like natural language processing, speech recognition, time series prediction, and sequential data analysis.

Long Short-Term Memory Networks

LSTM networks are a type of RNN that uses special units in addition to standard units. LSTM units include a ‘memory cell’ that can maintain information in memory for long periods. A set of gates is used to control when information enters the memory when it’s output, and when it’s forgotten. There are three types of gates viz, Input gate, output gate and forget gate. The input gate decides how much information from the last sample will be kept in memory; the output gate regulates the amount of data passed to the next layer, and the forget gates control the tearing rate of memory stored. This architecture lets them learn longer-term dependencies.

Use: Suitable for tasks that require capturing long-term dependencies in sequential data, such as language translation, speech recognition, and sentiment analysis.

Advantages of Neural Networks

Non-Linearity: Neural networks can model complex, non-linear relationships in data, making them suitable for tasks where traditional linear models fall short.

Pattern Recognition: They excel in pattern recognition tasks, making them effective in applications like image and speech recognition.

Learning from Data: Neural networks are data-driven and can learn from large datasets, adapting to changing patterns and improving their performance with more data.

Generalization: They can generalize from the training data, allowing them to make predictions on new, unseen data with reasonable accuracy.

Parallel Processing: Neural networks can be highly parallelized, taking advantage of modern hardware and accelerating computation.

Feature Learning: Deep neural networks can automatically learn relevant features from raw data, reducing the need for manual feature engineering.

High-Dimensional Data: They handle high-dimensional data effectively, making them suitable for tasks like image, text, and speech analysis.

Adaptability: Neural networks can adapt to a wide range of tasks, from image and speech recognition to natural language processing and game playing, often achieving state-of-the-art results.

Memory: Recurrent neural networks can maintain memory of past information, which is useful in sequential data tasks.

Reinforcement Learning: Neural networks are central to reinforcement learning, enabling agents to learn and adapt their behavior in dynamic environments.

Multimodal Data: They can process and integrate data from different sources, allowing them to handle multimodal data like combining text and images for understanding.

Scalability: Neural networks can be scaled up by adding more layers and neurons, allowing them to tackle increasingly complex problems.

Robustness: They often exhibit robust performance even in the presence of noisy or incomplete data.

Time-Series Analysis: Recurrent neural networks and Long Short-Term Memory networks are effective for tasks involving time-series data, such as stock market predictions and weather forecasting.

Adaptive Parameters: The weights and parameters of neural networks can be adapted during training, leading to improved model performance.

Disadvantages of Neural Networks

Complexity: Neural networks, especially deep networks, can be highly complex, making them challenging to design, train, and debug.

Large Data Requirements: Deep neural networks often require substantial amounts of data to perform well, and they may not generalize effectively with limited data.

Overfitting: Complex neural networks are prone to overfitting, where they fit the training data too closely, leading to poor generalization of new data.

Training Time: Training deep networks can be computationally intensive and time-consuming, particularly for large datasets.

Hyperparameter Tuning: Neural networks involve many hyperparameters, and finding the optimal configuration can be a time-consuming process.

Interpretability: Neural networks are often considered "black box" models, making it difficult to interpret and explain their decisions.

Hardware Requirements: Training deep networks effectively may require specialized hardware, like GPUs or TPUs, which can be costly.

Data Preprocessing: Proper data preprocessing, including normalization and augmentation, is critical for neural networks to perform well, adding an extra layer of complexity to the workflow.

Sensitivity to Noise: Neural networks can be sensitive to noisy data, and the presence of outliers can negatively affect their performance.

Lack of Labelled Data: Many neural network applications require labeled data for supervised learning, which can be expensive and time-consuming to acquire.

Limited Understanding of Hidden Layers: While neural networks are capable of complex tasks, it's often challenging to understand the specific features or patterns that hidden layers have learned.

Robustness to Adversarial Attacks: Neural networks can be vulnerable to adversarial attacks where small, imperceptible changes to input data can lead to incorrect predictions.

Biases in Data: Neural networks may inherit biases present in the training data, which can lead to unfair or discriminatory outcomes.

Overall Impact of NNs and the Conclusion

Neural Nets have revolutionized numerous industries by providing solutions to complex problems that were once thought impossible. In computer vision, neural networks have enabled the development of accurate object recognition and autonomous vehicles. Natural language processing powered by neural networks has transformed language translation, chatbots, and voice assistants. They've enhanced healthcare by automating medical image analysis and drug discovery. In finance, neural networks play a key role in fraud detection and risk assessment. Furthermore, their adaptability and generalization ability have made them indispensable in various scientific fields.

However, this technological leap is not without its challenges, including ethical concerns and the need for responsible AI development. The impact of neural networks is expected to grow, shaping the future of technology and redefining the way we interact with machines and data.