Cookies, Code, and AI - A Sweet Journey into Backpropagation
Tags: ai, softwaredevelopment, learningculture, generativeai
Published: 2024-07-11 13:56:01

---
title: Cookies, Code, and AI - A Sweet Journey into Backpropagation
published: true
description: Discover how backpropagation empowers AI systems to learn and improve by drawing parallels with baking cookies. Understand its role in making neural networks more intelligent, efficient, and capable of tackling complex tasks. Explore key insights from Artem Kirsanov's video and learn practical applications of this crucial algorithm to enhance decision-making, automation, and personalization in business. Embrace the power of backpropagation and stay ahead in the AI-driven world.
tags: AI, SoftwareDevelopment, LearningCulture, GenerativeAI
cover_image: https://dev-to-uploads.s3.amazonaws.com/uploads/articles/hl63rzysxsntpz2i5ml6.png
---

[Originally posted on Dev3loper.ai](https://www.dev3loper.ai/insights/cookies-code-and-ai-a-sweet-journey-into-backpropagation)

Have you ever wondered how computers learn from their mistakes, just like we do when baking cookies? Imagine you've whipped up a batch of too-salty or sweet cookies. You wouldn't give up, right? Instead, you'd tweak your recipe—maybe add more sugar or a pinch less salt until they are perfect. This process of tasting, adjusting, and tasting again is like how computers learn using an incredible **backpropagation** technique.

So, what exactly is backpropagation? Simply put, it's a way for computers to learn from their mistakes. When a computer makes a guess and finds out it's wrong, backpropagation helps it figure out how to improve its guess next time. It's like having a magical cookbook that enables you to adjust your recipe based on how your cookies taste.

To help understand this better, think about the first time you baked cookies. You followed a recipe, but they were too salty when you took a bite. You realize you added too much salt and not enough sugar. The next time, you might reduce the salt by half a teaspoon and increase the sugar by a tablespoon. After another batch, you taste and adjust again. Maybe the cookies are better this time, but they are still imperfect, so more tweaking is needed. Each time you bake and taste, you learn from the previous batch and improve.

Backpropagation in neural networks works similarly. When the network makes a prediction, and it's wrong, it calculates the error—the difference between its prediction and the actual result. This is like tasting your cookies and finding them too salty. The network then adjusts its internal settings (called weights and biases) to reduce this error. Think of these settings as the ingredients in your recipe. A big adjustment is needed if the error is significant, like drastically decreasing salt. If the error is small, a tiny tweak is made, similar to adding just a pinch more sugar.

But why is backpropagation such a big deal? Well, it's the cornerstone of almost every modern machine-learning system. From your voice assistant understanding your commands to self-driving cars recognizing obstacles on the road, backpropagation is critical. It helps machines get more intelligent with each mistake they make, learning faster and performing better over time.

If you think about it, backpropagation is very much like baking cookies. You might start with a basic recipe when you bake, but you'll likely make adjustments depending on how the cookies turn out. Maybe more chocolate chips, less sugar, or an extra minute in the oven. Similarly, backpropagation allows computers to improve their 'recipes' by tweaking their processes based on feedback. Essentially, the computer is continuously learning and adjusting, just as you do with each batch of cookies.

Let's explore this concept using our baking analogy and see how it forms the backbone of many robust AI systems today.

## Summary of the Video by Artem Kirsanov

{% embed https://www.youtube.com/watch?v=SmZmBKc7Lrs %}

In a fascinating video by computational neuroscience student and researcher Artem Kirsanov, we dive deeper into the concept of backpropagation and its pivotal role in machine learning. Artem begins by highlighting that despite the varied applications and architectures within machine learning—from language models like GPT to image generators like Midjourney—all these systems share a standard underlying algorithm: backpropagation.

Artem delves into the historical development of backpropagation, tracing its roots back to the 17th century. However, the modern form of the algorithm was notably formulated by Seppo Linnainmaa in 1970 in his master's thesis. This foundational work was further elaborated in 1986 when David Rumelhart, Geoffrey Hinton, and Ronald Williams demonstrated that backpropagation could efficiently train multi-layered neural networks, which were then able to solve complex problems and recognize intricate patterns.

To explain backpropagation, Artem uses the example of a curve-fitting problem. Imagine you have collected a set of points on a graph and aim to draw a smooth curve that best fits these points. This curve is determined by coefficients, much like the ingredients in a cookie recipe. You desire to find the best combination of these coefficients to minimize the error or loss between the curve and the actual data points.

Artem then breaks down how backpropagation helps in this task. The algorithm calculates the gradient, which indicates the direction and rate at which each coefficient should be adjusted. This process is akin to tasting your cookies and adding more sugar or decreasing the salt to improve the taste. The algorithm iteratively adjusts the coefficients to reduce the loss, leading to a more accurate fit.

A crucial part of the video is explaining the chain rule, a fundamental principle in calculus that allows us to calculate the gradients of complex functions. Just as a recipe combines different ingredients in various steps, complex machine learning models combine various mathematical operations. The chain rule helps us determine how small changes in each step affect the final result, enabling precise adjustments.

Artem encapsulates these ideas within the concept of computational graphs. These graphs map out the sequence of calculations in a neural network, allowing backpropagation to compute the necessary adjustments efficiently. Even in large neural networks with millions of parameters, this method remains effective and scalable, making it instrumental in the rise of robust AI systems today.

By the end of the video, Artem beautifully ties these concepts back to their practical implications. Understanding backpropagation allows us to appreciate the elegance and efficiency of modern AI systems, highlighting why this algorithm is fundamental to their success. From recognizing images and translating text to generating creative content, backpropagation is the unsung hero behind many technological advancements.

### Recap:

![Backpropagation Recap](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/1tdw7yglnuy9rlzhaaf6.png)

- **Common Algorithm**: Backpropagation underpins diverse machine learning systems despite different applications.
- **Historical Roots**: Modern backpropagation formulation by Seppo Linnainmaa in 1970, further refined by Rumelhart, Hinton, and Williams in 1986.
- **Curve Fitting Example**: This section explains backpropagation using the analogy of fitting a curve to data points by adjusting coefficients.
- **Gradient Calculation**: The algorithm calculates gradients to determine how to adjust parameters for error minimization.
- **Chain Rule**: Fundamental in computing gradients of complex functions, enabling precise adjustments in neural networks.
- **Computational Graphs**: Visualize the sequence of calculations, allowing efficient adjustments even in large networks.
- **Practical Implications**: This paper highlights the critical role of backpropagation in the success of modern AI, from image recognition to text translation.

Artem's video serves as an enlightening deep dive into the workings of backpropagation, making complex mathematical concepts accessible and engaging. It underscores the importance of this algorithm in making AI more competent and shaping the future of technology.

## Personal Insights and Practical Applications

![Personal Insights and Practical Applications](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/6ad4cocvlna2i8044wxh.png)

Understanding backpropagation isn't just about grasping a nifty algorithm—it's about recognizing its transformative impact on artificial intelligence and how we can leverage it in various applications. As an engineer and developer, I see several key benefits and opportunities for businesses harnessing this technology.

Firstly, backpropagation enhances neural networks' learning capabilities. Imagine trying to teach a child to recognize different animals. Initially, their guesses might be far off. But with each correct and incorrect guess, they get better, learning to distinguish a cat from a dog more accurately. Similarly, backpropagation allows AI systems to refine their understanding and improve their performance over time. This continual learning process makes technologies like facial recognition, voice assistants, and language translators effective.

Efficiency and scalability are other significant advantages. The beauty of backpropagation lies in its ability to handle large datasets and complex models efficiently. For instance, in healthcare, AI models trained via backpropagation can process vast amounts of patient data to predict disease outbreaks or recommend personalized treatment plans. These models become more accurate and reliable as they learn from more data, making them invaluable tools for critical decision-making.

From a business perspective, the applications of backpropagation in AI are vast and varied, offering numerous advantages. Improved decision-making capabilities are a standout benefit. Companies can leverage machine learning models to analyze vast datasets, uncover trends, and make data-driven decisions. For example, AI models in finance can predict market shifts, assess risks, and optimize investment strategies, enhancing profitability and reducing uncertainties.

Another practical application is automating complex tasks. Consider the e-commerce industry, where AI can handle customer service inquiries, manage inventory, and personalize shopping experiences. With backpropagation, these AI systems learn from each interaction, becoming more efficient and effective. This improves customer satisfaction and frees up human resources for more strategic tasks.

Personalization and enhancing customer experience is another area where backpropagation shines. Businesses can use AI to tailor recommendations based on user preferences and behavior. Think of streaming services suggesting movies or e-commerce sites recommending products. Thanks to backpropagation, these personalized experiences are powered by machine learning models that continuously learn and adapt.

Leveraging backpropagation can significantly enhance applications' capabilities in software development. We can create more thoughtful, intuitive software by incorporating machine learning models that learn and adapt. For example, developing a recommendation engine for an app or crafting an intelligent chatbot that provides accurate and relevant responses becomes feasible with backpropagation. Enhancing user experience and driving engagement through intelligent features can be a game-changer in the competitive tech landscape.

Understanding and applying backpropagation ultimately allows us to unlock AI's full potential. It equips us to build solutions that are not only smarter but also more efficient and adaptable. As businesses evolve in the digital age, integrating AI powered by backpropagation can provide a significant competitive edge, paving the way for innovation and growth.

## Conclusion

![Conclusion](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/wju10zbvxxwc0ji4300l.png)

Understanding backpropagation is like having the secret ingredient that makes AI systems truly intelligent. Just as baking perfect cookies involves continuous testing and adjustment, backpropagation enables neural networks to learn from their errors and improve steadily over time. Through this process, AI becomes more reliable, efficient, and capable of solving increasingly complex problems.

Artem Kirsanov's exploration of backpropagation provides deep insights into the algorithm's fundamental workings, making complex concepts accessible and engaging. His explanation underscores the pivotal role of backpropagation in various AI advancements, from image recognition and natural language processing to more personalized and efficient business applications.

For those developing and implementing AI solutions, grasping how backpropagation works offers a significant advantage. It allows us to build systems that can learn and adapt, enhancing performance and delivering better results. This is crucial in today's competitive landscape, where technology and efficiency drive success.

Backpropagation is more than just an algorithm; it's the engine that powers modern AI. AI systems can achieve remarkable feats by learning from mistakes and continuously improving. Whether it's providing more accurate medical diagnoses, enhancing customer experiences, or optimizing business operations, the applications of backpropagation are as extensive as they are transformative.

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I encourage you to watch Artem Kirsanov's video to learn more about this fascinating topic and better understand the mechanics behind backpropagation. As AI evolves, staying informed and understanding these foundational algorithms will be critical to leveraging their full potential.

Let's embrace the power of backpropagation and continue innovating, creating more innovative, efficient solutions that enhance our lives and businesses. Feel free to share your thoughts, experiences, or questions about backpropagation and its applications. Let's keep the conversation going and explore the endless possibilities of AI together!