Model Optimization | Gradient Descent | Trajectory #ai #ml #basics #python #14 смотреть онлайн

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Optimization is the process of finding the best solution among a set of possible solutions. In AI and ML, optimization is used for tasks such as training neural networks, selecting the best features, and hyperparameter tuning.

This code simulates a process of optimizing a neural network model to match a target output using a technique called Gradient Descent. The model's parameters are adjusted iteratively to minimize the difference between the predicted output and the target output. The code visualizes the trajectory of the model's parameter values during optimization using matplotlib. The final optimized model is printed at the end.

Sure, let's break down the code step by step:

Importing Libraries: We start by importing two important libraries:

numpy (as np): It helps us work with arrays and mathematical operations efficiently.
matplotlib.pyplot (as plt): This library is used for creating plots and visualizations.
Defining Initial Model and Target: We create a hypothetical neural network model (model) with some initial parameter values. Think of it as a machine learning model trying to predict something, like recognizing images. We also define a target output (target), which represents the correct answers or desired output for the model.

Empty List for Model Updates: We create an empty list called models to store the updates to the model during optimization. We'll track how the model's parameters change over iterations.

Optimization using Gradient Descent:

We use a loop that runs 100 times (you can adjust this).
Inside the loop, we calculate the gradient of the loss function with respect to the model. The gradient gives us the direction to adjust the model's parameters to minimize the difference between the predicted output and the target output. This is the core of Gradient Descent.
The learning_rate determines how big our steps are during optimization. It's like controlling how fast the model adjusts itself.
We update the model's parameters by subtracting the product of the gradient and learning rate from the model. This adjusts the model in the direction that reduces the loss.
Storing Model Updates: We append a copy of the current model to the models list. This is like taking snapshots of the model's parameters after each optimization step.

Plotting the Trajectory: We use matplotlib to create a plot that shows how each parameter of the model changes over iterations. This helps visualize how the model is getting closer to the optimal solution.

Displaying the Plot: We use plt.show() to display the plot on the screen.

Final Optimized Model: We print the final version of the optimized model after all iterations are completed.

In essence, this code represents the process of training a neural network-like model to learn from data and improve its predictions by adjusting its internal parameters using the Gradient Descent algorithm. The visualization helps us understand how the model's parameters change during this optimization process.