Neural Networks Simplified for Tenth Graders

Author(s): Srijay Gupta

Originally published on Towards AI.

As a Data Scientist, I often find myself simplifying complex tech concepts for non-technical audiences. At the core of popular AI tools like ChatGPT and Midjourney are neural networks — these might seem complex at first, but they’re crucial for understanding how Generative AI operates. In this blog, I’ll use the fun and familiar process of travel planning to break down how neural networks function.

What exactly are Neural Networks? U+1F310

Think of them like advanced computer programs, modeled after the human brain. Just like our brains learn from experiences, neural networks improve their skills through a process called Machine Learning. They practice with numerous examples to get better at tasks. This special kind of machine learning involves ‘nodes’, similar to artificial brain cells, organized in layers. These nodes communicate, passing information and making decisions about its importance using ‘weights’ and ‘biases’. These are the network’s way of determining what’s most crucial in the data it receives.

As neural networks learn, they adjust these weights and biases through a method known as ‘backpropagation’. It’s like the system is teaching itself the best way to interpret and respond to the information it processes. With enough practice, it becomes adept at complex tasks, such as recognizing objects in images, translating languages, or predicting future events. The result is an AI system capable of handling intricate challenges, far beyond the scope of traditional machine learning algorithms.

Neural Networks through the Lens of Travel Planning U+1F6EB

Now, join me as we draw parallels between our imaginative friend Emma, crafting the perfect European vacation, and key concepts of neural networks. My goal is to move beyond dry mathematical explanations and tap into non-technical real-world examples that even a tenth-grader can relate to. I hope this helps demystify neural networks for both seasoned technologists and those less familiar with AI and ML.

U+1F50D Cost Function in Travel Planning: Picture Emma immersed in planning her dream trip to Paris. She’s faced with a delightful yet challenging decision: should she indulge in a sunset cruise on the Seine, or savor a gourmet dinner in Montmartre? This decision-making process closely resembles a neural network’s ‘cost function.’ In her case, the cost function is about balancing enjoyment with her budget. Similarly, in neural networks, the cost function measures the difference between the network’s predictions and the actual outcomes. It’s like Emma evaluating each option against her desire for the best Parisian experience without exceeding her budget. Just as Emma adjusts her Paris plans for the optimal blend of pleasure and cost, neural networks continuously fine-tune their calculations to achieve the most efficient and effective outcomes.

U+2696️ Weights and Biases in Decision Making: When Emma is planning her trip to Greece, she faces a choice between a historical tour in Athens and a relaxing day in Santorini. This decision-making process activates her mind’s ‘neurons,’ each option carrying its own appeal or ‘weight.’ Her personal preferences, or ‘biases,’ also play a role in her choice. This situation is very similar to how a neural network operates. Just like Emma evaluates her options based on their appeal and her preferences, neural networks process inputs by activating certain pathways. These pathways are determined by the ‘weights’ of different inputs and the ‘biases’ in the system. Emma’s method of choosing her activities, aligning with what excites and interests her, closely resembles how neural networks select and prioritize information based on learned patterns and preferences.

U+1F3B2 Stochastic Gradient Descent in Journey Planning: On her trip to Lisbon, Emma stumbles upon a hidden Fado music performance, a truly memorable part of her journey. This happy accident is similar to ‘Stochastic Gradient Descent’ in AI. In this method, a bit of randomness is introduced into how neural networks learn, helping them uncover unique and effective solutions. Just like Emma’s unexpected discovery enriched her trip, Stochastic Gradient Descent allows AI to find novel solutions, moving beyond rigid, pre-defined paths. Emma’s openness to new and unplanned experiences reflects how this approach can lead to unexpected and rewarding outcomes in both travel and AI.

U+1F504 Backpropagation in Adjusting Plans: Emma’s journey takes an unexpected turn when she misses her train to Prague. Instead of fretting, she embraces a scenic drive through the Bavarian countryside, turning a potential problem into a delightful detour. This ability to adapt and learn from surprises parallels backpropagation in neural networks. Just as Emma recalibrates her plans, learns from this twist, and enriches her travel experience, backpropagation in neural nets involves adjusting and improving based on errors or new information, enhancing the network’s ability to make accurate predictions in the future.

U+1F4C8 Gradient Descent in Route Optimization: When Emma is deciding on a hike through the Swiss Alps, she looks at different trails, thinking about how steep or tough they might be. This is a bit like ‘gradient descent’ in AI. Just as Emma chooses the trail that gives her a great experience without being too difficult, gradient descent helps neural networks find the best way to solve problems by making small, careful changes. It’s all about finding a path that’s rewarding but not too hard — whether you’re hiking in the Alps or teaching an AI model to learn

U+1F9E0 Learning Rate and Travel Adjustments: How quickly Emma hops from one European city to the next is a lot like the ‘learning rate’. If she rushes through Rome in a day, she might skip the bohemian neighborhood of Trastevere, akin to a high learning rate causing a model to overshoot its learning targets. On the other hand, lingering too long in a small town like Bruges could get boring, just as a slow learning rate might prevent a model from reaching its desired solution. In neural networks, the learning rate controls how much the model changes after processing new information, striking a balance between making significant changes and avoiding excessive adjustments, much like Emma finding the right amount of time to enjoy each city thoroughly.

⏰ Epochs in Itinerary Refinement: Imagine Emma has an unexpected extra day to add to her Europe itinerary. As Emma weighs whether to spend her additional day wandering through Vienna’s historic streets, exploring more of Paris’s art galleries, or delving deeper into Barcelona’s Gaudí architecture, she’s engaging in a process similar to an ‘Epoch’. An epoch is a full run through the training data, optimizing the model’s learning. Each revision of Emma’s plans is not just a change in her itinerary; it’s a careful enhancement, ensuring she maximizes her travel experience. Likewise, a neural network goes through multiple epochs, each time processing the data to refine and improve its performance.

U+1F3AF Convergence in Finalizing Itinerary: After exploring the artistic treasures of the Louvre in Paris and the historical wonders of the Vatican Museums in Rome, Emma finds that adding more museum visits to her itinerary doesn’t significantly enhance her overall experience. This reflects the concept of ‘convergence’ in neural networks, where after a certain amount of training, further adjustments don’t enhance the model’s performance. Emma’s itinerary is well-balanced, offering a mix of sightseeing, relaxation, and adventure much like a well-trained neural network that accurately predicts or classifies data.

U+1F6E0️ Generalization vs. Overfitting in Travel Planning: Emma plans a canal cruise in Amsterdam, yet keeps her schedule open for unexpected delights like a spontaneous street festival. She recognizes the importance of flexibility in her travel plans, referred to as ‘Generalization’. This ensures a neural network’s performance is robust on both familiar and new, unseen data. Conversely, ‘Overfitting’ — akin to an overly rigid itinerary — leads to a model that performs well only in familiar scenarios but falters in real-world applications. Emma’s strategy of blending scheduled activities with room for unexpected experiences is similar to a well-generalized AI model, navigating a variety of situations.

Conclusion

As we reach the end of our exploration, think of Emma’s adventure as a vivid illustration of neural networks in action. Just as she navigated her European journey, balancing choices and embracing new experiences, neural networks operate in a similar fashion. They weigh inputs and adjust their responses, learning and adapting as they go. May your own journey, whether it’s delving into the realms of AI or planning your real-world vacations, be as engaging and insightful as Emma’s European escapade.

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Published via Towards AI