5 Things You Need to Know About Neural Networks

Neural networks are revolutionizing the fields of Artificial Intelligence (AI) and Deep Learning. They enable computer programs to recognize patterns and solve complex problems by mimicking the human brain. Here are five key concepts you should know about them:

1. Neural Networks are Composed of Node Layers

Neural networks, more accurately called **Artificial Neural Networks (ANNs)**, are built from interconnected nodes organized in layers. These layers typically include:

• Input Layer: Receives the initial data.
• Hidden Layer(s): Performs computations on the input data. Neural networks can have multiple hidden layers.
• Output Layer: Produces the final result or prediction.

These layers are connected to each other. These networks are designed to resemble the human brain.

2. Each Node is a Linear Regression Model

Think of each node, or artificial neuron, as its own **linear regression model**. Linear regression is used to predict future events. Each node receives input data, applies weights, and considers a bias (or threshold) to generate an output. The **weights of the connections** between the nodes determines how much influence each input has on the output.

3. Data Flows Through a Feed Forward Network

Data passes from one layer to the next in a process known as a **feed forward network**. Consider the example of deciding whether to go surfing. The final decision (go or nogo) is the predicted outcome (yhat). Let's say the decision is influenced by three factors:

• Wave quality (x1): 1 for good, 0 for bad.
• Crowd size (x2): 1 for empty, 0 for crowded.
• Shark presence (x3): 1 for sharkfree, 0 for sharks present.

Each factor is assigned a weight (w) based on its importance. For example:

• Wave quality (w1): 5 (very important).
• Crowd size (w2): 2 (less important).
• Shark presence (w3): 4 (important).

The output (yhat) can be calculated as: yhat = (x1 * w1) + (x2 * w2) + (x3 * w3) threshold.

If yhat is greater than 0, the output is 1 (go surfing!). **Adjusting the weights or threshold** will alter the outcome.

4. Neural Networks Learn from Training Data

Neural networks rely on **training data** to improve accuracy. This often involves **supervised learning** on labeled datasets. As the model trains, its accuracy is evaluated using a **cost function**.

The goal is to **minimize the cost function** by adjusting the weights and biases of the network using a method called **gradient descent**. This allows the model to learn the optimal parameters to fit the training data.

5. Different Types of Neural Networks Exist

Beyond the feed forward network described above, various other types of neural networks exist, each suited for specific tasks:

• Convolutional Neural Networks (CNNs): Excellent for image recognition by identifying patterns in images.
• Recurrent Neural Networks (RNNs): Designed for time series data and forecasting future events like sales. They utilize feedback loops.

Neural networks are powerful tools with many applications. Further research and exploration will reveal their true potential in AI and beyond.