What is a Neural Network?

Explaining at a Low Level

Imagine your brain is like a big team of tiny workers, each with a special job to do. These workers help you understand everything around you—like recognizing your friend’s face or figuring out that the sky is blue. Now, a neural network is like a team of tiny workers inside a computer. These workers help the computer learn and make decisions, just like your brain helps you.

Think of it like this: if you’re learning to recognize different animals, each tiny worker in your brain looks at different parts of the animal—like the ears, tail, and color. When all these workers talk to each other and share what they see, you can tell if it’s a dog, cat, or elephant. A neural network in a computer does the same thing. It looks at different pieces of information and learns to recognize patterns, like whether a picture is of a cat or a dog.

Explaining with Advanced Concepts

A neural network is a series of algorithms that attempt to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. Neural networks can adapt to changing input; so the network generates the best possible result without needing to redesign the output criteria.

Structure:

Neurons: The basic units of a neural network are called neurons, inspired by the human brain’s neurons. These neurons are organized into layers.

Layers: A neural network consists of three types of layers: input layer, hidden layers, and output layer.

• Input Layer: The input layer receives the initial data.
• Hidden Layers: Hidden layers are where the network processes the inputs. A neural network can have one or multiple hidden layers.
• Output Layer: The output layer produces the final result.

Weights and Biases:

Each connection between neurons has a weight. These weights are adjusted during the training process to help the network make accurate predictions. Biases are added to the neurons to help the model fit the data better.

Activation Functions:

Activation functions determine if a neuron should be activated. They introduce non-linear properties to the network, allowing it to model complex data. Common activation functions include ReLU (Rectified Linear Unit), sigmoid, and tanh.

Training Process:

Forward Propagation: Data is passed through the network, layer by layer, from the input layer to the output layer.

Loss Function: After producing an output, the network calculates the error using a loss function, which measures how far the output is from the actual result.

Backpropagation: The network adjusts the weights and biases by propagating the error backward through the network to minimize the loss function. This is done using optimization algorithms like gradient descent.

Deep Learning:

When neural networks have many hidden layers, they are referred to as deep neural networks. This forms the basis of deep learning, which is used for more complex tasks like image and speech recognition.

Practical Application

For instance, in image recognition, the input layer receives pixel values of an image. The hidden layers process these values to detect features like edges, shapes, and textures. Finally, the output layer classifies the image as a cat, dog, or another object based on the features detected.

To sum up, a neural network is a powerful tool that allows computers to learn from data and make intelligent decisions by mimicking the way our brains work. It’s the backbone of many AI applications, from recognizing speech to driving cars.

Conclusion

Neural networks can sound pretty complex, right? But with tckrAI, you don’t have to worry about the nitty-gritty details. We use neural networks to help our users make better trading decisions. Essentially, these networks are like tiny workers in a computer, learning and recognizing patterns in data just like how our brains work. With tckrAI, all the heavy lifting is done behind the scenes. You get the benefits of this powerful technology—like spotting trends and predicting market movements—without needing to dive into the technical stuff. It’s about making advanced AI accessible and useful, letting you focus on what you do best: trading.

Where to Learn More

One source that we’ve found that does an extremely good job explaining these topics as well as so many others is known as 3blue1brown on YouTube. His videos are in-depth and use easy-to-follow diagrams and 3-D models to better explain the topics. If you’re new to neural networks, I’d recommend watching his series on them to better understand more. You can find that series here. I should note, I’m not affiliated with him at all or being paid to promote him – I just like his videos.