Generative AI (English Version): Unleashing Next-Gen AI

Explore seq2seq with attention, showing how encoder hidden states and attention scores guide decoding, reducing bottlenecks and enabling parallelism over the shift to transformer-style ideas.

Learn to use the Huggingface transformer pipeline to connect pre-trained models to inputs, handle preprocessing and post-processing, and perform tasks like sentiment analysis, zero-shot classification, NER, QA, and translation.

Explore how transfer learning from a pre-trained transformer encoder enables fine-tuning for text classification, using BERT’s CLS token and self-attention, built on next-sentence prediction and masked-language modeling.

Train gan networks by balancing the discriminator and generator in a minimax game, alternating warm-up and generator steps to produce realistic images from z noise and trick the discriminator.

Warm up the discriminator in Keras with real data (label one) and fake data (label zero); then train a GAN with a frozen discriminator to update generator via label flipping.

Condition image generation by feeding class labels to generator and discriminator with one hot encoding. Use embeddings for categorical input and follow warm-up and fine-tuning training loop for conditional GANs.

AttributeGAN extends conditional GANs with multi-label attributes, enabling controlled image editing via a generator encoder-decoder and an attribute classifier, trained with reconstruction and cross-entropy losses.

Explore multimodal mappings in generative AI through text-to-image generation, encoder-decoder architectures, and diffusion models behind Midjourney and Dreamstudio. Discover how text prompts guide image creation from scratch beyond supervised learning.

AI automation shifts tasks from people to tools, changing interfaces and roles without eliminating designers or writers; adopt and regulate usage while embracing open-source democratization and cautious experimentation.