# Gradient Descent From Scratch In Python

TLDRIn this tutorial, Vic introduces the concept of gradient descent, a fundamental algorithm for training neural networks. The video demonstrates how to implement linear regression using gradient descent in Python. Starting with data on weather, the process involves importing libraries, reading and preprocessing data, and visualizing the relationship between variables. The core of the tutorial focuses on understanding the linear regression model, calculating loss using mean squared error, and iteratively updating weights and biases to minimize loss. The training loop, learning rate adjustments, and weight initialization are discussed in detail. The video concludes with a comparison of the implemented model's parameters to those from scikit-learn, emphasizing the relevance of the concepts learned to neural networks.

### Takeaways

- {"📚":"Gradient descent is a fundamental algorithm for training neural networks by optimizing parameters based on data."}
- {"🔢":"The process involves initializing parameters, making predictions, calculating loss, and updating parameters to minimize error."}
- {"📈":"Linear regression is used as an example to demonstrate how gradient descent works, with the goal of predicting future values based on past data."}
- {"🎯":"The mean squared error (MSE) function is used to measure the prediction error or loss, which is crucial for gradient descent."}
- {"📉":"The gradient represents the rate of change of the loss with respect to the weights, guiding the direction and magnitude of parameter updates."}
- {"🔧":"A learning rate is used to control the size of the steps taken during the update process to avoid overshooting the minimum loss."}
- {"🌀":"Gradient descent is an iterative process, requiring multiple passes (epochs) through the data to converge towards the optimal solution."}
- {"🔁":"The training loop is a common structure in machine learning, where the data is passed through the model repeatedly until the loss is minimized."}
- {"📊":"Data is often split into training, validation, and test sets to prevent overfitting and to evaluate the model's performance accurately."}
- {"🤖":"The concepts learned, such as forward and backward passes, are directly applicable to more complex neural networks."}
- {"⚖️":"Careful tuning of the learning rate and initialization of weights is essential for the effective learning and convergence of the model."}

### Q & A

### What is the main topic of the tutorial?

-The main topic of the tutorial is gradient descent, specifically its implementation in Python for linear regression as a fundamental building block of neural networks.

### Why is gradient descent important in the context of neural networks?

-Gradient descent is important because it is the method by which neural networks learn from data and train their parameters, allowing for the optimization of the network's weights and biases.

### What library is used to read in the data for the tutorial?

-The tutorial uses the pandas library to read in the data for analysis.

### What is the dataset used in the tutorial?

-The dataset used in the tutorial consists of weather data, including maximum temperature (T-Max), minimum temperature (T-Min), rainfall, and the next day's temperature, with the goal of predicting T-Max for the following day.

### How is the linear relationship visualized in the tutorial?

-The linear relationship is visualized using a scatter plot with a line drawn through the data points to represent the trend, which is then used to discuss the concept of a linear relationship in the context of linear regression.

### What is the equation form of the linear regression model used in the tutorial?

-The equation form used in the tutorial is: \( \hat{y} = W_1 \times X_1 + b \), where \( \hat{y} \) is the predicted value, \( W_1 \) is the weight, \( X_1 \) is the input feature (T-Max in this case), and \( b \) is the bias.

### What is the role of the weight (W) in the linear regression model?

-The weight (W) in the linear regression model is a value that the neural network learns through the training process. It determines the influence of the input feature on the prediction.

### What is the learning rate in the context of gradient descent?

-The learning rate in gradient descent is a parameter that controls the step size during the update of the model's weights and biases. It is crucial for the convergence of the algorithm and to prevent overshooting the optimal solution.

### What is the mean squared error (MSE) used for in the tutorial?

-The mean squared error (MSE) is used as a loss function to measure the error of the prediction made by the linear regression model. It calculates the average of the squares of the differences between the predicted and actual values.

### How is the gradient calculated in the tutorial?

-The gradient is calculated by taking the derivative of the loss function with respect to the weights and biases. It represents the rate of change of the loss and is used to adjust the parameters in the direction that minimizes the loss.

### What is the purpose of the training loop in the gradient descent algorithm?

-The training loop is used to iteratively update the model's parameters by passing the data through the algorithm multiple times (epochs) until the error is minimized or the algorithm has converged to an optimal solution.

### Outlines

### 😀 Introduction to Gradient Descent

This paragraph introduces Vic, the presenter, and the topic of gradient descent, which is a fundamental algorithm for training neural networks. The script outlines the plan to use Python to implement linear regression via gradient descent. The importance of reading in weather data and preparing it for training is emphasized, including handling missing values and examining the initial data set. The goal is to predict the maximum temperature for the next day based on various weather-related inputs.

### 📈 Understanding Linear Regression

The paragraph explains the concept of linear regression and its necessity for a linear relationship between the predictors and the predicted value. It describes the process of visualizing this relationship through a scatter plot and drawing a line of best fit. The script also covers the equation for linear regression, introducing the concepts of weights and bias. It further discusses how linear regression can be expanded to use multiple predictors and the parameters involved in such a model. The paragraph concludes with the use of scikit-learn to train a linear regression model and plot the resulting line of best fit.

### 🧮 Calculating Loss and Gradient

This section delves into the importance of calculating the error or loss of a prediction, which is crucial for the gradient descent algorithm. It introduces the mean squared error (MSE) as the loss function and explains how it is used to measure the prediction error. The script then discusses how to graph different weight values against loss to understand the effect of varying weights on the model's performance. It also explains the concept of the gradient, which is the rate of change of the loss with respect to the weights, and how it is calculated.

### 🔄 Gradient Descent Optimization

The paragraph focuses on the optimization process of gradient descent, aiming to find the weight values that minimize the loss. It explains the concept of the gradient and how it changes with different weight values. The script illustrates this with a graph and explains the goal of gradient descent is to find the weight value where the gradient is zero, which corresponds to the lowest possible loss. It also introduces the concept of the partial derivatives with respect to both the weights and the bias, which are used to update these parameters.

### 🔢 Updating Parameters and Learning Rate

This section discusses how to update the weights and biases of the model to minimize the error. It explains the process of calculating the partial derivatives and using them to adjust the parameters. The script highlights the importance of the learning rate in controlling the size of the steps taken during the optimization process. It shows how taking too large a step can increase the loss, while a learning rate that is too small can lead to very slow convergence. The paragraph also includes a visualization of how the gradient changes as the weights change and the need to adjust the learning rate accordingly.

### 🔁 Training Loop and Batch Gradient Descent

The paragraph outlines the process of setting up a training loop for gradient descent, which involves repeatedly passing the data through the algorithm until the loss is minimized. It explains the concept of batch gradient descent, where the gradient is averaged across the entire dataset before updating the parameters. The script details the steps needed to build the algorithm, including initializing weights and biases, making predictions, calculating loss and gradient, and updating parameters in the backward pass. It also emphasizes the importance of using a validation set to monitor the algorithm's performance and a test set for final evaluation.

### 🎯 Final Model Parameters and Convergence

The final paragraph discusses the finalization of the model's parameters after training and the convergence of the algorithm. It explains that careful attention must be paid to the learning rate and the initialization of weights and biases, as these factors can significantly affect the outcome and convergence rate of the model. The script also touches on the possibility of adding a regularization term to prevent the weights from becoming too large. The paragraph concludes with a summary of the key concepts learned in the tutorial and a preview of applying these concepts to neural networks in future tutorials.

### Mindmap

### Keywords

### Gradient Descent

### Neural Networks

### Linear Regression

### Pandas

### Scikit-learn

### Mean Squared Error (MSE)

### Weights and Biases

### Forward Pass

### Backward Pass

### Learning Rate

### Convergence

### Highlights

Gradient descent is an essential building block of neural networks, allowing them to learn from data and train their parameters.

The tutorial uses Python to implement linear regression with gradient descent, a method that will be expanded upon for more complex networks in future videos.

Data on weather is used to train a linear regression algorithm to predict tomorrow's maximum temperature (TMax) using other columns.

Linear regression requires a linear relationship between the predictors and what is being predicted.

A scatter plot visualizes the relationship between TMax and TMax tomorrow, suggesting a linear trend.

The linear regression model is represented by the equation: Predicted Y = W1 * X1 + B, where W is the weight and B is the bias.

Scikit-learn's linear regression class is used to train the algorithm and make predictions.

The mean squared error (MSE) function is introduced to calculate the loss or error of the prediction.

Gradient descent aims to minimize the loss by adjusting weights and biases, using the gradient of the loss function.

The gradient is the rate of change of the loss with respect to the weights, indicating how quickly the loss changes as weights change.

A learning rate is used to control the size of the steps taken during gradient descent to avoid overshooting the minimum loss.

Batch gradient descent is employed, which calculates the gradient by averaging the error across the entire dataset.

The algorithm is initialized with random weights and biases, and a training loop is set up to iteratively improve these parameters.

The partial derivatives with respect to weights and biases are calculated to understand how to adjust the parameters to reduce error.

The training process involves a forward pass to make predictions, a calculation of loss and gradient, followed by a backward pass to update parameters.

The algorithm's convergence is monitored by observing when the loss stops decreasing significantly, indicating that the minimum loss point has been reached.

The learning rate and initialization of weights and biases are critical factors that can affect the speed of convergence and the final outcome of the model.

The concepts introduced, such as forward and backward passes, are directly applicable to more complex neural networks.