Learning AI from scratch– category –

Recap and Today's Topic Hello! In the previous session, we discussed initialization in neural network models, which helps improve learning efficiency and facilitates appropriate parameter convergence. Today, we’ll focus on a critical tec...

Recap of the Previous Lesson and Today's Theme In the previous lesson, we learned about optimizers in machine learning models. I hope you gained an understanding of techniques like Adam, RMSprop, and Adagrad, which efficiently optimize m...

What is an Optimizer? Hello! In this lesson, we’ll discuss optimizers, a key element in neural network training. Optimizers are essential for ensuring that models minimize errors efficiently and make accurate predictions. Specifically, o...

What is Dropout? Hello! Today, we’ll learn about Dropout, a powerful regularization technique used to prevent overfitting in neural networks. Deep learning models, as they learn from data, often face a challenge known as overfitting, whe...

What is Batch Normalization? Hello! In this episode, we'll explore "Batch Normalization," an essential technique for stabilizing deep learning training and improving model accuracy. Batch normalization is widely used to prevent vanishing...

What is the Exploding Gradient Problem? Hello! In this lesson, we’ll be discussing the exploding gradient problem, a key issue in the training of neural networks. Similar to the vanishing gradient problem we covered previously, the explo...

What is the Vanishing Gradient Problem? Hello! Today's topic covers a common challenge in deep learning known as the Vanishing Gradient Problem. This issue arises particularly in deep neural networks and can severely hinder the learning ...

What is an Activation Function? Hello! In the previous lesson, we covered backpropagation, a key process in training neural networks. This time, we’ll focus on activation functions, which play a crucial role in neural networks. Activatio...

Recap and Today's Topic Hello! In the previous session, we explored forward propagation, which explains how input data flows through the layers of a neural network to generate predictions. Today, we’ll dive into the reverse process: back...

What is Forward Propagation? Hello! In the previous lesson, we learned about Multilayer Perceptrons (MLP) and gained an understanding of the basic structure of neural networks. In this lesson, we will dive into a crucial process called f...

Recap and Today's Topic Hello! In the previous session, we discussed the fundamental concepts of Deep Learning, which uses neural networks to automatically extract features from data by stacking multiple layers. Today, we will delve into...

Welcome to the World of Deep Learning!! Hello! Today, we’ll talk about one of the most exciting technologies in the field of AI: Deep Learning. Deep Learning is a technique that uses vast amounts of data to solve complex problems. It’s b...

Entering the World of Machine Learning: The Importance and Variety of Algorithms In Chapter 2, we delved into the major algorithms used in machine learning. The goal of this chapter was to understand how these core algorithms work and to...

Recap and Today's Topic Hello! In the previous session, we learned about evaluation metrics for classification tasks, such as accuracy, recall, and the F1 score. These metrics are useful when predicting categories. Today, we’ll focus on ...

Recap and This Week’s Topic Hello! In the previous lesson, we explained grid search and random search, two popular methods for exploring hyperparameter combinations. You learned how to use these methods to find the optimal hyperparameter...