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Recap: WaveNet In the previous episode, we explained WaveNet, a neural network-based model that directly generates speech waveforms, offering high-quality speech synthesis. By generating audio at the sample level, WaveNet produces more n...

Recap: Speech Generation Models In the previous episode, we discussed the basics of speech generation technology. Traditional methods such as rule-based, unit selection, and parametric speech synthesis have evolved into modern approaches...

Recap: Evaluation Metrics for Text Generation In the previous episode, we discussed evaluation metrics for text generation, explaining key metrics like Perplexity and the BLEU score. Perplexity measures a model's prediction accuracy, whi...

Recap: BERT and the Masked Language Model In the previous episode, we explored BERT (Bidirectional Encoder Representations from Transformers), a powerful model in natural language processing (NLP), and its training method, the Masked Lan...

Recap: Multi-Head Attention Mechanism In the previous episode, we explained the Multi-Head Attention Mechanism, a core technology within the Transformer model. This mechanism allows the model to understand the context of text from multip...

Recap: Positional Encoding In the previous episode, we discussed Positional Encoding in the Transformer model. Positional Encoding is a technique that handles the sequence information of words, playing a crucial role in helping the Trans...

Recap: The Internal Structure of GPT Models In the previous episode, we explored the internal structure of GPT models. GPT is based on the decoder part of the Transformer and uses techniques such as self-attention and masked self-attenti...

Recap: Details of Text Generation Models In the previous episode, we explored text generation models in depth. We learned how models like Sequence-to-Sequence, RNNs (Recurrent Neural Networks), and Transformers automatically generate nat...

Recap: Evaluation Metrics for Image Generation In the previous episode, we explained evaluation metrics for image generation, specifically focusing on the FID score and IS (Inception Score). These metrics are crucial for assessing how cl...

Recap: Pix2Pix In the previous episode, we covered Pix2Pix, a model for image-to-image translation. Pix2Pix can be applied to various transformation tasks, such as colorizing black-and-white images or generating realistic images from ske...

Recap: Conditional GAN (cGAN) In the previous episode, we discussed Conditional GAN (cGAN). cGANs allow for the addition of conditions to the generated data, enabling the creation of data with specific attributes. This capability is usef...

Recap: CycleGAN In the previous episode, we explored CycleGAN, a GAN model that enables style transformation between different domains (e.g., day to night, photo to painting) without requiring paired data. This technology is useful for s...

Recap: StyleGAN In the previous episode, we explored StyleGAN, a model that allows precise control over specific styles in image generation. StyleGAN’s architecture enables the adjustment of particular features (e.g., eyes, hairstyles) w...

Recap: Generative Adversarial Networks (GAN) In the previous episode, we discussed Generative Adversarial Networks (GAN). GANs consist of two models, the Generator and the Discriminator, that compete to generate new data. The generator c...

Recap: DCGAN (Deep Convolutional GAN) In the previous episode, we explained DCGAN (Deep Convolutional GAN), a GAN that uses Convolutional Neural Networks (CNN) to generate high-quality images, particularly in fields like image generation...