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

Understand generative AI's place among supervised, unsupervised, and reinforcement learning, and how sampling from a P(X) model enables generating new data.

Compare generative and discriminative models by contrasting decision boundaries with sampling from p(x); explore unconditional and conditional sampling, autoencoders, and GAN-based architectures.

Discover why generative models matter and how pre-training and semi-supervised learning boost discriminative models. Explore autoencoders that learn the distribution of x, extract meaningful latent features, and initialize discriminative networks.

Explore the universal encoder decoder design pattern that maps inputs to a latent feature and then to outputs, closing the semantic gap in semantic segmentation and translation tasks.

Explore encoder-decoder design patterns and multimodal mappings across input and output modalities, including unimodal text to text and image to image, and multimodal text, image, and voice tasks.

Explore unimodal mappings with text-to-text tasks, focusing on language models that predict the next word from context using encoder-decoder architectures, self-supervised learning, and downstream NLP applications.

Learn statistical language models that build a next-word probability distribution from an n-gram context using counts, a sliding window, and smoothing. Explore backoff, storage and text generation with hallucinations.

Explore character-level neural language models leveraging a recurrent network to predict the next character, trained by backprop to adjust wxh, whh, and wh1, enabling sampling-based text generation.

Explore word-level neural language models that predict next words from context using one-hot inputs and softmax, with RNNs, LSTMs, GRUs, and transformers. Examine embeddings, encoder-decoder architectures, and Shakespeare-style text generation.

Explains n-gram language modeling with bigrams and trigrams on Reuters and Alice in Wonderland, and introduces rnn-lstm models for character- and word-level text generation in python and keras.

Explore seq2seq models and encoder–decoder architectures, contrasting RNNs with transformer networks for parallel encoding in text generation. Discuss bottlenecks and attention to overcome limits in NLP tasks.

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 large scale language models drive generative AI in text, using self-supervised pre-training to enable transfer learning for encoder-decoder tasks like translation, summarization, and chatbot applications.

Explore how pre-trained transformers enable universal, data-hungry models through self-supervised pre-training (masked language modeling and NSP) and attention mechanisms, with Bert as a key success.

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.

Discover GPT, a generative pre-trained transformer using decoder weights for downstream tasks, and how few-shot prompts enable zero- or few-shot learning without fine-tuning.

Explore how few-shot learning and pre-trained language models evolved from GPT-3 to ChatGPT, focusing on supervised fine-tuning, reward models, and RLHF to improve aligned, guided outputs.

Learn to build apps with OpenAI APIs to query language models using an API key. Explore playgrounds, token concepts, and use cases like QA, classification, translation, and image generation.

Explore fine-tuning OpenAI models with your data, preparing CSV to JSON, training with create, and deploying a named fine-tuned model via API for specialized tasks.

Build a contextual chat bot using the completion API, manage max tokens and rate limits, maintain conversation history with summarized prompts for a chatgpt-like experience.

Build a true ChatGPT clone in Google Colab using the chat completion API with GPT-3.5 Turbo, leveraging system messages and history for a responsive chat.

Deploy a chat gpt clone with streamlit to build a web app calling completion and chat completion APIs, with history via session state and one-click deployment, illustrating developer roles.

Build a ChatGPT clone with Django or Streamlit using Colab notebooks. Add streaming, history management, and user feedback with consent, then deploy to cloud via GitHub.

Explore image-to-image mapping in generative ai using encoder-decoder architectures like u-net with bottlenecks, skip connections, and upsampling to produce segmentation masks or new images.

Explore the auto encoder, an encoder-decoder architecture for image generation learned via self-supervised learning, using a bottleneck latent representation for reconstruction and denoising to enable downstream tasks.

Explore how latent codes in autoencoders enable t-sne visualizations of clusters, and sample from latent space to generate digits without inputs, revealing attribute-like features controlled by Z1 and Z2.

Explore conditional variational autoencoders with multi-modal distributions and class labels to steer image generation, enabling data augmentation and attribute control, while noting current quality limits before GANs.

Build autoencoders in Keras with encoder–decoder architecture that compresses 28 by 28 binary images to a 32-dimensional latent code, reconstructs via sigmoid output, and supports denoising, sparsity, and transfer learning.

Explore generative adversarial networks, where the generator uses latent codes to create fake images and the discriminator learns to distinguish real from fake.

Feed random noise into the generator to produce fake images from the training data. Discard the discriminator to keep only the G network, mirroring the autoencoder approach.

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.

Explore the deep convolutional GAN, featuring a convolutional generator and discriminator, upsampling via conv 2d transpose or upsampling, latent code input, and binary cross-entropy training on 32×32×3 CIFAR-10 images.

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.

Generative adversarial networks today deliver high-quality, artifact-free images with realistic detail across domains, driven by variants such as progressive GANs and other GAN families for text-to-image synthesis and domain adaptation.

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.

Explore diffusion models that turn noise into images via forward noising and reverse denoising with a denoising U-net, guided by text prompts in text-to-image generation.

Extend latent diffusion models to conditionally generate images from text using the clip architecture, embedding text and image in latent space to guide denoising with attention-based conditioning.

Explore stable diffusion models that blend latent-space diffusion, denoising units, and an encoder–decoder architecture with clip-based text encoding and attention.

Explore Dream Studio and Midjourney to interact with diffusion models, generate images from text prompts. See how encoder-decoder features, attention mechanisms, and text encoding influence outputs and upscaling.

Explore OpenAI's image APIs for text-to-image generation with prompts, image.create, variations, upscaling, and edits via mask, in playground or Colab.

Explore how to run and interact with stable diffusion models in Hugging Face using the diffusers library, including login, loading pipelines, and generating images from prompts.

Explore text-to-image generation techniques and build a Midjourney-style clone by selecting models and versions, applying variations and edits, and integrating these capabilities into your own app.

Develop image to text using CNN encoders and autoregressive decoding; study image captioning, transformers, and multitask medical report generation.

Explore txt2voice with the VALL-E encoder-decoder, where a text encoder creates embeddings and a decoder generates personalized speech in a chosen voice from acoustic prompts.

Explore the good side of generative AI: GPT clone apps in streamlit generate code from requirements, while tools like GitHub Copilot elevate higher-level work and transform education.

Assess the bad faces of generative AI, from AGI debates in GPT-4 to job displacement and rising regulation, with education use and calculator-like tools shaping policy.

Explore how bias and misuses in generative AI, from polarization to adversarial attacks, threaten jobs, governance, and equitable access.

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.

Highlight encoder-decoder design patterns and transformer-based models to explore text-to-text, image-to-image, text-to-image, image-to-text, and text-to-voice mappings with diffusion approaches.