Learning AI from scratch– category –
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Lesson 87: The Basics of Generative Adversarial Networks (GANs)
Recap of the Previous Lesson: Self-Supervised Learning In the last lesson, we covered Self-Supervised Learning, a method that allows models to efficiently learn from unlabeled data by creating tasks such as predicting hidden portions of ... -
Lesson 86: Self-Supervised Learning
Recap of the Previous Lesson: Overview of the GPT Model In the previous lesson, we discussed the GPT (Generative Pre-trained Transformer) model, which is specialized in natural language generation. GPT uses an autoregressive approach to ... -
Lesson 85: Overview of the GPT Model
Recap of the Previous Lesson: The BERT Model In the previous lesson, we discussed BERT (Bidirectional Encoder Representations from Transformers), a Transformer-based model that captures bidirectional context, allowing it to deeply unders... -
Lesson 84: Overview of the BERT Model
Recap of the Previous Lesson: The Transformer Model In the previous lesson, we explored the Transformer model, which has become the dominant architecture in natural language processing (NLP). Unlike traditional models such as RNNs or LST... -
Lesson 83: The Transformer Model – Understanding the Foundation of Modern NLP
Recap of the Previous Lesson: The Attention Mechanism In the previous lesson, we discussed the Attention Mechanism, a technique that allows models to focus on the most important parts of input data. By focusing on key elements, the Atten... -
Lesson 82: The Attention Mechanism
Recap of the Previous Lesson: Sequence-to-Sequence Models In the previous lesson, we discussed Sequence-to-Sequence (Seq2Seq) models, which take an input sequence (such as a sentence or audio data) and generate an output sequence. Seq2Se... -
Lesson 81: Sequence-to-Sequence Models – A Magic Box for Generating Text from Text
Hello, everyone! Let’s continue our journey into the world of AI. In the previous lesson, we explored the GRU (Gated Recurrent Unit), an efficient and powerful model that simplifies the complexity of LSTM while retaining its capabilities... -
Lesson 80: Gated Recurrent Units (GRU) — A Simpler Yet Efficient Alternative to LSTM
Recap and Today's Topic Hello, everyone! In our last lesson, we dove deep into the world of Long Short-Term Memory (LSTM), an impressive model designed to handle time-series data by retaining important information over long sequences. To... -
Lesson 79: Long Short-Term Memory (LSTM) — An Improved Version of RNN
Recap and Today's Topic Hello! In the previous session, we learned about Recurrent Neural Networks (RNNs), which are well-suited for handling time-series and sequence data. RNNs can retain past information to make predictions or classifi... -
Lesson 78: Introduction to Recurrent Neural Networks (RNN) – Understanding Models for Time Series Data
Recap and This Week’s Topic In the previous lesson, we discussed pooling layers, which help reduce the dimensionality of data while preserving important information. This time, we’ll cover Recurrent Neural Networks (RNNs), which are part... -
Lesson 77: Pooling Layers
What are Pooling Layers? Hello! In this lesson, we will learn about an important element in neural networks called the "pooling layer." The pooling layer's primary role is to compress the features extracted by convolutional layers and re... -
Lesson 76: Convolutional Layers
What are Convolutional Layers? Hello! Today's topic is "Convolutional Layers." Convolutional layers play a crucial role in extracting features from image and audio data and are a central element of Convolutional Neural Networks (CNNs). I... -
Lesson 75: Fundamentals of Convolutional Neural Networks (CNNs) – Explaining Models Specialized for Image Data
Recap of the Previous Lesson and Today's Theme In the last lesson, we learned about transfer learning. We understood that transfer learning allows us to apply existing pre-trained models to new tasks, achieving high accuracy with a small... -
Lesson 74: Transfer Learning – Applying Pre-trained Models to New Tasks
Recap and This Week’s Topic In the previous lesson, we covered data augmentation, a technique often used in machine learning to enhance model performance by expanding limited datasets. This week, we will delve into another powerful metho... -
Lesson 73: Data Augmentation – Explaining Techniques for Increasing Data
Recap of the Previous Lesson and Today's Theme In the previous lesson, we learned about early stopping, a technique to prevent overfitting in models. Early stopping is a method that prevents overfitting and utilizes computational resourc...
