Learning AI from scratch– category –

Recap and Today's Topic Hello! In the previous session, we discussed hyperparameter tuning, where adjusting key parameters can significantly impact a model’s performance. Today, we’ll focus on two specific methods used to optimize hyperp...

Recap and This Week’s Topic Hello! In the previous lesson, we covered cross-validation, a method for evaluating how well a model generalizes to new data. Cross-validation provides an objective assessment of the model’s performance on uns...

Recap and Today's Topic Hello! In the previous session, we discussed regularization techniques such as L1 and L2 regularization, which play a crucial role in preventing overfitting by ensuring that the model doesn't become too tailored t...

Recap and This Week’s Topic Hello! In the previous lesson, we discussed overfitting prevention, a method for preventing models from becoming too adapted to the training data, which reduces their generalizability to new data. This time, w...

Recap and Today's Topic Hello! In the previous session, we learned about activation functions in neural networks, which play a crucial role in determining the output of each neuron. Today, we will explore loss functions, an essential com...

Recap and Today's Topic Hello! In the previous session, we covered epochs and batch sizes, which are essential elements in model training, influencing the efficiency and convergence speed of learning, particularly when working with large...

Recap and This Week’s Topic Hello! In the previous lesson, we discussed the importance of learning rate, a crucial factor in determining the speed and accuracy of model training. Setting the learning rate appropriately can accelerate con...

Recap and Today's Topic Hello! In the previous session, we discussed Stochastic Gradient Descent (SGD), a crucial method for efficiently optimizing parameters, especially when working with large datasets. SGD is particularly powerful in ...

Recap and This Week’s Topic Hello! In the previous lesson, we explored gradient descent, a fundamental algorithm for optimizing model parameters by adjusting them to minimize the loss function. While gradient descent is a powerful techni...

Recap and This Week’s Topic Hello! In the previous lesson, we explored loss functions, which are used to measure a model's error. A loss function evaluates the accuracy of predictions, and minimizing this loss is the primary goal of the ...

Lesson 47: Activation Functions – Explaining the Functions That Determine Neuron Output Recap and This Week’s Topic Hello! In the previous lesson, we explored the perceptron, the basic unit of neural networks. The perceptron processes we...

Recap and Today's Topic Hello! In the previous session, we learned about the basics of neural networks. Neural networks have a hierarchical structure consisting of input, hidden, and output layers, and they mimic the brain's neural circu...

Recap and This Week’s Topic Hello! In the previous lesson, we discussed CatBoost, a powerful boosting algorithm specialized for categorical variables. CatBoost offers automatic handling of categorical data and helps prevent overfitting, ...

Recap and Today's Topic Hello! Last time, we learned about LightGBM, a highly efficient gradient boosting framework known for its speed and ability to handle large datasets. Today, we will explore CatBoost, another boosting method that e...

Recap and Today's Topic Hello! Last time, we discussed XGBoost, a highly accurate and computationally efficient algorithm widely used in data science. Today, we’ll explore LightGBM (Light Gradient Boosting Machine), another gradient boos...