Neural Networks

Explained: Neural networks

Reference: https://news.mit.edu/2017/explained-neural-networks-deep-learning-0414

The majority of deep learning applications use "convolutional" neural networks, in which the nodes of each layer are clustered, the clusters overlap, and each cluster provides data to several nodes (orange and green) of the following layer.

Welcome to the exciting world of neural networks! These incredible algorithms are inspired by the very structure and function of the human brain, and they can help you unlock the true potential of your data.

Imagine a system that can recognize patterns, identify trends, and make predictions - all on its own. That's the power of neural networks. By using interconnected nodes, organized into layers, these algorithms can process and transmit information like never before.

But neural networks aren't just a tool for the tech-savvy - they have practical applications across a wide range of industries, from healthcare to finance to marketing. With their ability to learn from data, these algorithms can help you make better decisions, optimize your processes, and stay ahead of the competition.

So if you're ready to explore the cutting-edge of machine learning and artificial intelligence, look no further than neural networks. With their power, versatility, and endless potential, they just might be the missing piece in your quest for success.

What is a Neural network?

Neural networks are a class of machine learning algorithms inspired by the structure and functioning of the human brain. They consist of interconnected nodes (also called neurons) that process and transmit information, typically organized into layers. Neural networks can be used for a wide range of tasks, including classification, regression, and pattern recognition.

The basic idea behind neural networks is to train a model to recognize patterns in data by adjusting the strength of connections between nodes. During training, the network receives input data, propagates it through the layers, and generates an output. The output is then compared to the desired output, and the network adjusts its parameters (i.e., the weights on the connections between nodes) to minimize the difference between the two.

Neural networks can be used for supervised learning, unsupervised learning, or a combination of both. In supervised learning, the network is trained on labelled data, where the desired output is known for each input. In unsupervised learning, the network is trained on unlabeled data and learns to identify patterns and structures in the data without any explicit feedback. In some cases, neural networks can also be used for reinforcement learning, where the network learns by receiving feedback in the form of rewards or penalties.

Neural networks have been used to solve a wide range of problems, including image and speech recognition, natural language processing, and predictive analytics. They are also widely used in robotics, control systems, and other applications where complex, non-linear relationships need to be modelled.

Reference: https://www.investopedia.com/terms/n/neuralnetwork.asp

Working of neural network

Let's say we have a neural network that is trained to recognize handwritten digits. The network consists of multiple layers of interconnected nodes, and each node in one layer is connected to every node in the next layer.

The first layer of the network is called the input layer, and it receives the input data, which in this case is an image of a handwritten digit. The input layer consists of multiple nodes, with each node representing a pixel in the image.

The second layer of the network is called the hidden layer, and it performs calculations on the input data to extract features that are relevant for recognizing the digit. Each node in the hidden layer receives inputs from every node in the input layer and computes a weighted sum of those inputs. The result of the computation is then passed through an activation function, which introduces non-linearity into the network.

The output layer of the network receives inputs from the hidden layer and produces a vector of probabilities, with each element of the vector representing the likelihood that the input image represents a particular digit (0-9). The final prediction is based on the element of the vector with the highest probability.

During the training process, the network is presented with a large number of labelled images of digits, and it adjusts the weights on the connections between nodes to minimize the difference between the predicted output and the true label. This process is known as backpropagation, and it involves computing the gradient of the error with respect to the weights and using that gradient to update the weights in a way that reduces the error.

By iteratively adjusting the weights on the connections between nodes, the neural network learns to recognize the patterns and features that are most important for identifying handwritten digits. Once the network is trained, it can be used to make predictions on new, unseen data.

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