TechSambad Learning: Exploring Neural Networks with Google "Learn About"

I recently experimented with a new learning tool from Google called Learn About. This tool provides an exciting way to start learning about a topic and dive deeper based on curiosity. I highly recommend it to academia and professionals interested in expanding their knowledge. Since this tool has yet to be available in India, I used a VPN to access it. In this article, I will share my learning experience about neural networks.

Introduction to Neural Networks

Neural networks are fascinating computer programs inspired by the human brain. They consist of interconnected nodes, called neurons, organized into layers. These networks excel at learning from data and making predictions, powering many of the technologies we use today, from voice recognition to self-driving cars.

To understand how neural networks work, imagine them as layers of neurons that pass information along. They have an input layer where data enters, one or more hidden layers where learning happens, and an output layer that provides the final prediction. Let's do two critical components of these networks: weights and biases.

Neural Network Parameters: Weights and Biases

Neural networks learn and make predictions by adjusting specific values called parameters. The two main types of parameters are weights and biases:

• Weights: Weights represent the strength of the connection between neurons. Imagine you're to classify an image as a cat or a dog. The weights determine how much influence each image pixel has on the final prediction. A more considerable weight means a more robust connection, suggesting that a particular pixel is crucial to determining whether the image is a cat or a dog. For example, if a specific area of an image (like the presence of whiskers) is highly correlated with identifying a cat, the weight associated with those pixels will be more significant, reinforcing their importance in the classification.
• Biases: Biases act like thresholds. They help neurons activate even when the input values are low, adding flexibility to the decision-making process. If a neuron has a strong bias, it might "fire" or "activate) even with weaker inputs, allowing the network to capture complex patterns in data. For example, biases can help ensure that certain features are considered even if other input signals are weak, enabling the model to make more nuanced decisions.

Initially, these weights and biases are assigned random values. As the network learns, these values are adjusted in a?training process.

How Does a Neural Network Learn?

Training a neural network involves several steps, similar to trying to perfect a light switch flick to illuminate a bulb:

1. Start with Random Flicks: The weights and biases start with random values, like a first attempt to turn on a switch.
2. See if the Light Turns On: The network makes predictions with these initial settings.
3. Get Feedback (Error): The predictions are compared to the answers. The difference, called error, tells the network how wrong it was.
4. Adjust the Flick: Using algorithms like backpropagation, the network makes minor adjustments to the weights and biases to reduce the error.
5. Repeat Steps 2-4: The process is repeated many times, improving the predictions step by step until the network learns how to make accurate decisions.

Backpropagation: Finding the Optimal Weights

Determining the best weights and biases is a critical part of training. The backpropagation algorithm is used to systematically adjust the weights by propagating the error backwards through the network. Essentially, the network determines which weights were responsible for the mistake and tweaks them to get a better outcome next time. For instance, if the network misclassifies an image, backpropagation helps determine which weights contributed to the error and adjusts them accordingly to improve accuracy.

Examples of Neural Network Applications

Neural networks are used in various real-world applications:

1. Image Recognition: In applications like Google Photos or Facebook, neural networks help recognize faces, objects, or scenes within photos. For example, a neural network can identify specific people in your photos by learning the unique features of their faces.
2. Speech Recognition: Virtual assistants like Siri and Alexa use neural networks to convert spoken language into text and understand user commands. This involves training networks on large datasets of spoken words and corresponding text to accurately transcribe and understand speech.
3. Self-Driving Cars: Neural networks help autonomous vehicles make sense of their surroundings by processing inputs from cameras, radar, and other sensors. For instance, a neural network might learn to identify pedestrians and traffic signs, helping the car navigate safely.
4. Healthcare Diagnostics: Neural networks assist in diagnosing diseases by analyzing medical images like X-rays or MRIs. For example, they can detect anomalies in lung scans to help identify early signs of diseases like pneumonia or cancer.
5. Natural Language Processing (NLP): Applications like Google Translate or chatbots use neural networks to understand and generate human language. These networks are trained on large corpora of text to learn patterns in language, enabling them to translate text or hold conversations.

Conclusion

In essence, neural networks adjust their internal parameters—weights and biases—until they make accurate predictions. They learn to change these parameters through repeated training to make the correct decisions. This adaptability makes them powerful tools for various tasks, from image recognition to language processing.