Learning AI from scratch– category –

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...

Recap: Variational Autoencoder (VAE) In the previous episode, we explored Variational Autoencoders (VAE), a probabilistic generative model. VAEs compress data into a latent space and can generate new data based on this compressed represe...

Recap: Mechanism of Autoencoders In the previous episode, we explained Autoencoders in detail. Autoencoders compress (encode) data and reconstruct (decode) it from the compressed representation. This process is useful for tasks like dime...

Recap and Introduction Hello! In the previous episode, we discussed Autoregressive Models, which are a type of generative model used to predict the next value based on time-series data. They generate the next step based on the current da...

Recap: Chapter 6 Summary In the previous episode, we reviewed our understanding of model interpretability, highlighting the importance of interpreting models using methods such as SHAP values and LIME. These techniques make it easier to ...

Recap: Generative Models In the previous episode, we covered the fundamental concepts of generative models. Generative models create new data based on training data and have diverse applications, such as image generation and text generat...