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Thank you! I appreciate you being here.
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I'm an experienced video developer based in Paris. You can follow my work; let's get started.
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My name is Alexandre Lairan. I work in Paris and I love everything about Ruby and machine learning. I'm a developer at Prezi, a company that helps marketers with their day-to-day tasks.
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In this talk, we will not discuss TensorFlow, as it may seem a bit complex. Instead, we'll focus on pure logic and demonstrate concepts without relying on advanced tools.
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If you're ever in Paris, don't hesitate to reach out to me. Now let's begin. What is AI and how does it differ from a personal research system? There are some misconceptions surrounding both.
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AI is a program that can learn from data, while a personal research system is a program that handles existing data. For example, the minimax algorithm, which we will discuss, illustrates these concepts.
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The minimax algorithm is simple and works in a turn-based game like tic-tac-toe. You play as crosses and your opponent plays as circles; you have to determine the best move to win.
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In tic-tac-toe, you analyze the current state of the board, assessing possible moves. Each move results in different potential outcomes, where you want to maximize your chances of winning while minimizing those of your opponent. This requires calculating scores and predicting future game states.
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At each level of decision-making, the number of possibilities decreases, as you cannot place a cross or circle in an occupied space.
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This also applies to personal research where you want to select options that improve your chances of success. If there are changes or evolutions in the rules of the game, your program must reevaluate its strategies.
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When we think about evolution, we can relate it to biological processes involving genetic variation among populations. Not every individual will survive; the survivors will reproduce, with some experiencing mutations that allow for new possibilities.
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In nature, these processes are slow, taking billions of years for significant advancements. However, in computing and machine learning, speed is essential. Therefore, we rely on better algorithms and mathematical models to achieve faster results.
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Let's examine an example involving data points and linear regression. We look for where our model fits best, denoted as 5.5, even without a data point present at that location.
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In the real world, data doesn't always behave linearly, and the model must adapt accordingly. The goal is not just to follow points but to classify them effectively, such as distinguishing between different animals or identifying good prospects.
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In handling classification, we need to consider error functions. For example, we may need to classify data based on specific feature sets. This creates a multidimensional space, which adds complexity to our models.
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Within higher dimensions, it becomes increasingly complicated to find a decision boundary that separates different classes accurately. Sometimes, we may even need to eliminate certain data points if they do not fit within the expected outcomes.
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To illustrate further, we can adapt algorithms that allow us to classify and make decisions even with incomplete or noisy data by utilizing ensemble methods or neural networks. These methods allow us to detect patterns effectively.
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Neural networks function by processing inputs through interconnected nodes that compute various mathematical functions. The structure can seem complex, but the fundamental operations involve weight adjusting based on inputs.
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As we begin to scale up, the architecture of the neural network is crucial. Each layer of neurons processes information from the previous layer, eventually arriving at an output.
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In essence, we are constructing learned representations from raw data. This allows neural networks to classify inputs accurately, such as recognizing numbers or images. The challenge remains in optimizing the network's performance and efficiency.
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Programming these neural networks requires structuring the code correctly, ensuring that all necessary libraries and frameworks are included. This is where languages like Crystal or Ruby come into play, enhancing the implementation's efficiency.
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However, as we work on building and adapting these models, we need to be aware of overfitting, where our model performs well on training data but fails to generalize on unseen data.
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To prevent this, we use regularization techniques, ensuring our model maintains a balance between fitting the training data well while also generalizing appropriately.
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Let's now consider the broader implications of these neural networks in practice. We often use pre-existing datasets for training and validation to assure our models can predict outcomes accurately.
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It's vital to preprocess the data correctly, ensuring that our neural networks have uniform representations before training begins.
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On the topic of resources, you can find various learning materials online, including channels dedicated to machine learning concepts. These resources form a valuable part of continuous learning.
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Finally, when discussing implementations, we must address how to translate mathematical functions into programming languages like Crystal or Ruby.
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The idea remains to maintain clarity and efficiency in our code while reducing unnecessary complexity.
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As we wrap up this portion, I want to emphasize the importance of practice and ongoing efforts in honing your skills in machine learning and programming.
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Feel free to reach out if you have further questions or need clarification on any part of this discussion. Thank you for your time, and I hope you enjoyed the presentation!
