ディープラーニングの基礎（61～90）– ディープラーニングの基本概念とニューラルネットワークの仕組みを理解します。 –

Recap of the Previous Lesson: Autoencoders In the previous lesson, we covered Autoencoders, a technique used for compressing and reconstructing data. Autoencoders compress input data into a low-dimensional latent representation and then ...

Recap of the Previous Lesson: Generative Adversarial Networks (GAN) In the previous article, we discussed Generative Adversarial Networks (GAN), a type of generative model where two networks, the generator and the discriminator, compete ...

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 ...

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 ...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...