Building AI Programs in Excel: Unlocking Profitable Opportunities
Table of Contents
- Introduction to AI in Excel
- Understanding the Principles of Artificial Intelligence
- The Structure of Neural Networks
- The Role of Inputs, Hidden Layers, and Output Layer
- The Learning Process of Neural Networks
- Forward Propagation and Back Propagation
- Activation Functions in AI
- The Importance of Bias in Neural Networks
- Calculating Prediction and Loss
- Evaluating the Learning Progress in AI
- Building an AI Program in Excel
🧠 Introduction to AI in Excel
In the world of trading, artificial intelligence (AI) is increasingly being used to make smarter investment decisions. The ability of AI to learn and adapt in complex environments has proven to be a Game-changer. In this article, we will explore a program called Logan, developed by Better Trading using Excel. Logan utilizes AI to recognize flower colors based on their length and width. Understanding the logic behind Logan's AI program could be the key to unlocking profitable opportunities in the stock market.
Understanding the Principles of Artificial Intelligence
AI is characterized by its ability to learn and find the best solutions to complex problems through self-learning. This learning process simulates the structure of the human brain, analyzing problem features and leveraging past experiences to make judgments. To build an AI program to solve problems, it is crucial to understand the structure of the human brain, specifically the concept of neural networks. Neural networks consist of layers, including the inputs layer, hidden layers, and output layer.
The Structure of Neural Networks
Neural networks can be visualized as a subway map with different stations representing different layers. The inputs layer acts as the data collection point, analogous to our five senses in perceiving information. The hidden layers are responsible for the thinking and analysis process, while the output layer translates thoughts into actions. A neural network can have multiple nodes in each layer, each representing a specific feature or function. The complexity of the problem being solved determines the number of nodes in each layer.
The Learning Process of Neural Networks
The learning process in neural networks is akin to traveling on a subway, moving forward to the destination and then returning to the starting point repeatedly. This process is commonly known as forward propagation and back propagation. During forward propagation, data is inputted into the network, and predictions are made based on the available information. These predictions are then compared to the actual results using a loss function, which calculates the error. Back propagation adjusts the weights in the network to minimize the loss, enabling the AI program to learn and improve over time.
Activation Functions in AI
Activation functions play a vital role in the learning process of neural networks. They determine the output or prediction based on the input received. One commonly used activation function is the Sigmoid function, which is ideal for predicting binary outcomes. However, there are various other activation functions such as Swish, developed by Google, that may yield better results. The choice of activation function depends on the specific problem and can greatly impact the accuracy of predictions.
The Importance of Bias in Neural Networks
Bias is an essential component of activation functions in neural networks. It helps improve the accuracy of predictions by offsetting any bias in the activation function. Bias is a constant value that remains unchanged regardless of the number of nodes in the inputs layer. Its role in the neural network can be compared to the red dot emitted by a laser sight, significantly increasing the hit rate of predictions.
Calculating Prediction and Loss
Predictions made by the neural network are represented by a percentage or a value between 0 and 1. A value closer to 1 indicates greater certainty, while a value closer to 0 suggests a higher degree of uncertainty. These predictions are compared to the actual results to calculate the loss, which measures the inaccuracy of the predictions. A common loss function used in neural networks is the square difference between the prediction and the actual value.
Evaluating the Learning Progress in AI
The progress of AI learning can be evaluated by monitoring the loss during the training process. The goal is to minimize the loss as much as possible. A loss of zero indicates that the AI program has learned from the given data and can make accurate predictions. However, achieving a perfect loss of zero may require an extensive number of learning iterations. In practical scenarios, a minimal loss below a certain threshold is considered acceptable, indicating significant learning progress.
Building an AI Program in Excel
Now that we have a solid understanding of AI principles, it's time to explore how to build an AI program using Excel. Better Trading has developed an AI program named Logan, which incorporates the concepts discussed above. Logan's AI recognizes flower colors based on their length and width. To showcase its learning process, the initial learning data of Logan has been cleared, allowing us to observe how it relearns and makes predictions. By providing Logan with new data, we can assess its accuracy in predicting flower colors.
To interact with Logan in Excel, simply input the length and width data of a flower in the designated cells. Logan will then provide a prediction based on the learned Patterns. The accuracy of Logan's predictions improves as the learning process progresses. By analyzing the loss and predictions, we can determine the effectiveness of Logan's AI program in recognizing flower colors.
Building an AI program in Excel opens up exciting possibilities for traders and investors. Although this article provides a Simplified overview, it lays the foundation for further exploration into AI and its applications in the financial domain. The Excel program used in this demonstration will be made available for download in the description, providing an opportunity to delve deeper into the formulas and mechanics behind Logan's AI program.
In conclusion, AI in Excel offers a glimpse into the future of trading and investing. By leveraging the power of neural networks and the principles of AI, traders can make more informed decisions and potentially increase profitability. Logan serves as an example of how AI can be harnessed to recognize patterns and make accurate predictions. As technology continues to advance, the application of AI in finance will undoubtedly play an increasingly significant role in shaping the industry's landscape.
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