Generative AI Tutorial Step by Step

Let's dive into the world of Generative AI, where creativity meets technology. This tutorial will guide you through the step-by-step process of building your own Generative AI model.

First, you'll need to choose a deep learning framework, such as TensorFlow or PyTorch, which will be your foundation for building the model. These frameworks provide the necessary tools and libraries to get started.

As you begin building your model, you'll need to decide on the type of Generative AI you want to create, such as a Generative Adversarial Network (GAN) or Variational Autoencoder (VAE). Each type has its own strengths and weaknesses, so understanding the differences is crucial.

With your framework and model type chosen, you can start designing the architecture of your model. This involves defining the number of layers, neurons, and activation functions that will be used to generate the desired output.

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Generative AI is a type of artificial intelligence that can generate new text, images, or music by learning from a large corpus of data.

It can generate coherent and contextually relevant text by learning patterns and structures from a large corpus of text data.

Generative AI models like Recurrent Neural Networks (RNNs), Transformers, or Language Models are trained on textual data to understand the relationships between words and the context in which they are used.

These models capture the statistical patterns of language and use them to generate text that is contextually relevant and appears as if it could have been written by a human.

Generative AI models can generate new text that follows grammatical rules, maintains coherence, and aligns with the given context or topic.

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Generative AI models come in various forms, each with its own strengths and weaknesses. Here are the top generative AI models:

Generative Adversarial Networks (GANs) consist of a generator and a discriminator network that compete against each other, creating synthetic samples that can fool the discriminator. Variational Autoencoders (VAEs) learn a compressed representation of the input data called the latent space and generate new samples by sampling points in the latent space and decoding them. Autoregressive models model the conditional probability of each element in a sequence given the previous elements, generating new data by sequentially predicting the next element based on the previous ones.

Diffusion models approximate the probability distribution of a given data domain, providing a way to generate samples from its approximated distribution. Flow-based models learn an invertible transformation from a simple probability distribution to a complex data distribution, generating samples that match the complex data distribution. Restricted Boltzmann Machines (RBMs) are probabilistic graphical models that learn the joint probability distribution of the input data, generating new samples by sampling from the learned distribution.

Here are some of the key generative AI models in more detail:

Large Language Models are transformers, a type of generative AI model. They can be categorized into encoder-only, encoder-decoder, and decoder-only architectures.

Encoder-only models can be used to extract sentence features, but they lack generative power. This means they can't create new text on their own.

Most existing Large Language Models prefer decoder-only structures due to their stronger representational power. This is because decoder-only models can generate text from scratch, whereas encoder-decoder models rely on the encoder to provide some information.

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In particular, encoder-decoder models can be considered a sparse version of decoder-only models, with information decaying more from encoder to decoder.

Here are some key characteristics of Large Language Models:

Generative AI models are diverse and can be broadly categorized into several types.

Diffusion models aim to approximate the probability distribution of a given data domain and provide a way to generate samples from its approximated distribution.

Diffusion models have two main processes: the forward process, which progressively applies noise to the original input data, and the reverse process, which uses a neural network to estimate the noise and generate data from noise input.

VAEs (Variational Autoencoders) learn a compressed representation of images called the latent space and generate new images by sampling points in this space and decoding them.

GANs (Generative Adversarial Networks) consist of a generator that produces synthetic images and a discriminator that distinguishes between real and generated images.

Here are some notable examples of generative AI models:

* Diffusion models for image generation, such as High-Resolution Image Synthesis with Latent Diffusion Models (CVPR 2022) and Inpainting using Denoising Diffusion Probabilistic Models (CVPR 2022)VAEs and GANs for image generation, as discussed in Generative AI in Image Generation

These models have various applications, including image-to-image translation, face generation and editing, and style transfer and fusion.

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Generative AI models come in various forms, each with its unique architecture and capabilities. Large Language Models (LLMs) are transformers that can be categorized into encoder-only, encoder-decoder, and decoder-only architectures.

There are many different ways of constructing LMMs, including Language Models are General-Purpose Interfaces, Flamingo, BLIP, BLIP-2, mPLUG-Owl2, Florence-2, and Dense Connector for MLLMs.

Here are some representative architectures:

These architectures demonstrate the diversity of LMMs and their potential applications in various fields.

The Gaussian Mixture Model is a powerful tool in the world of generative AI. It's a generative probabilistic framework that combines a few Gaussian distributions with undetermined variables to produce information sets.

GMMs are a parametric framework for the likelihood distribution of the parameters in a biometric system. This means they're particularly useful in applications where accuracy is crucial.

One of the key applications of GMMs is in speaker identification technology. They analyze vocal-tract-associated spectral characteristics to help identify speakers.

This technology has real-world implications, and I've seen it used in various applications, including voice assistants and biometric authentication systems.

GMMs are a type of generative model that's well-suited for tasks involving probability and uncertainty.

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Pretraining and fine-tuning are crucial steps in creating generative AI models.

Pretraining involves teaching a model to understand language structure using trillions of text tokens. This process requires massive computing resources, often more than thousands of GPUs, which can be unaffordable for individuals.

Pretraining is typically done by model publishers, who then publish the pre-trained model for others to use.

Fine-tuning, on the other hand, is a more resource-efficient process that involves teaching the model to follow specific instructions and generate answers aligned with human preference.

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To fine-tune a model, users can download the pre-trained model and use a small personal dataset, such as a movie dialog, to teach the model new skills.

Here are some resources to help you understand the pretraining and fine-tuning process:

Model customization is a powerful tool in generative AI, allowing you to tailor the default behavior of foundation models to produce specific results without complex prompts. This process, called model tuning, helps reduce costs and latency by simplifying prompts.

Model tuning enables you to evaluate the performance of your tuned model using Vertex AI's model evaluation tools. This ensures your model is production-ready and can be deployed to an endpoint for monitoring, just like in standard MLOps workflows.

Generative AI has various applications, including producing new products, speeding up tedious operations, and generating customized data and content.

DALL-E is a multimodal AI application that connects visual elements to the meaning of words with extraordinary accuracy, powered by OpenAI's GPT implementation.

It's an exceptional example of how AI can generate imagery in diverse styles based on human prompts, with its second version, DALL-E 2, allowing users to do just that.

ChatGPT, on the other hand, is a chatbot that utilizes OpenAI's GPT-3.5 implementation, simulating real conversations by integrating previous conversations and providing interactive feedback.

This AI-powered chatbot has gained widespread popularity since its inception, and Microsoft has even integrated a variant of GPT into Bing's search engine.

Bard, developed by Google, is another language model that uses transformer AI techniques to process language, proteins, and various content types.

Google launched Bard hastily after Microsoft's integration of GPT into Bing search, but it had a flawed debut that caused a substantial drop in Google's stock price.

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Model customization is a powerful tool that allows you to tailor the behavior of Google's foundation models to your specific needs. This customization process is called model tuning.

Model tuning helps you simplify your prompts, reducing the cost and latency of your requests. By customizing the default behavior of the models, you can achieve consistent results without using complex prompts.

Vertex AI offers model evaluation tools to help you assess the performance of your tuned model. This ensures that your model is production-ready before deploying it to an endpoint.

You can deploy your tuned model to an endpoint and monitor its performance like in standard MLOps workflows.

Image applications are where generative AI truly shines. Generative AI models can generate highly realistic images that resemble photographs or artistic styles, making them perfect for creative design, photography, and visual effects.

For instance, generative AI can create visually stunning landscapes, portraits, and abstract art. This technology has also been used to transform images from one domain to another while preserving the content or style, such as converting day-time images to night-time or turning sketches into realistic images.

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Generative AI models can also create realistic human faces, allowing for the generation of new identities or editing existing faces by changing attributes like age, gender, or expressions. This technology finds applications in gaming, virtual avatars, and character customization.

Here are some notable examples of image generation applications:

These applications are just the tip of the iceberg, and the possibilities are endless with generative AI.

Generative AI models use a variety of techniques to learn patterns and structures in data, including probabilistic modeling, latent space representation, and adversarial training.

Probabilistic modeling is a key component of generative AI, where models aim to capture the distribution of the training data and generate new samples by sampling from this learned distribution.

Generative models often utilize techniques like autoencoders or variational autoencoders to learn a latent space representation, which is a lower-dimensional representation of the training data that captures the underlying factors or features.

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Some common working principles of generative models include:

These working principles allow generative AI models to learn and generate new data that resembles a given training dataset, with applications in a wide range of fields.

Generative models learn the underlying patterns, structures, and relationships within the training data by utilizing probabilistic modeling to capture the distribution of the training data.

Probabilistic modeling is a key concept in generative models, where they aim to model the probability distribution of the data and generate new samples by sampling from this learned distribution. The choice of probability distribution depends on the type of data being generated, such as a Gaussian distribution for continuous data or a categorical distribution for discrete data.

Many generative models learn a latent space representation, which is a lower-dimensional representation of the training data. This latent space captures the underlying factors or features that explain the variations in the data.

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Generative Adversarial Networks (GANs) employ a unique working principle called adversarial training. GANs consist of two competing neural networks: the generator and the discriminator. The generator generates synthetic samples, while the discriminator tries to distinguish between real and generated samples.

Autoregressive models, such as recurrent neural networks (RNNs), model the conditional probability of each element in a sequence given the previous elements. These models generate new data by sequentially predicting the next element based on the preceding elements.

Some generative models, like variational autoencoders (VAEs), focus on reconstructing the original input data from a lower-dimensional latent space. The models aim to minimize the reconstruction error between the input and the reconstructed output.

Here are the common working principles of generative models:

Prompting is a crucial step in the generative AI workflow, and it starts with crafting input text that guides the model to generate the desired responses or outputs. This process is called prompt design.

To write better prompts, you can refer to the Prompt Engineering Guide from DAIR.AI, which provides valuable resources and strategies to help you create effective prompts.

A prompt can contain text, images, videos, audio, documents, and other modalities, or even multiple modalities (multimodal). This versatility is a key aspect of prompting.

There are several useful resources available to help you with prompt design, including the Awesome ChatGPT Prompts collection and the Awesome Deliberative Prompting guide. These resources offer a wealth of information and examples to help you get started.

AutoPrompt is another tool that can help you create prompts for a diverse set of NLP tasks using an automated method based on gradient-guided search.

Here are some useful resources to help you write better prompts:

Audio generation is a fascinating area of research, and diffusion models have made significant strides in this field.

Diffusion models are pretrained on large amounts of web data, such as the LAION-5B dataset, which requires massive computing resources. Users can download the released weights and fine-tune the model on their personal datasets.

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Some representative papers on diffusion models for audio generation include Grad-TTS, Text-to-Audio Generation using Instruction-Tuned LLM and Latent Diffusion Model, Zero-Shot Voice Conditioning for Denoising Diffusion TTS Models, EdiTTS, and ProDiff.

These models are not only efficient but also capable of producing high-quality audio. For example, ProDiff is a progressive fast diffusion model that achieves high-quality text-to-speech synthesis.

Users can practice with Huggingface Diffusers API to get hands-on experience with diffusion models for audio generation.

Here are some key papers to explore in the field of efficient fine-tuning of diffusion models for audio generation:

These papers showcase the potential of diffusion models for audio generation and provide insights into efficient fine-tuning techniques.

Generative AI has some amazing benefits that make it a valuable tool in various fields. It enables the creation of new and unique content, such as images, music, or text, which can be innovative and original.

Generative AI automates the process of content creation, saving time and resources. This automation can generate large volumes of content quickly and efficiently, assisting in tasks like data augmentation, content generation, and design exploration.

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One of the most exciting aspects of generative AI is its ability to provide personalization and customization. Generative models can be trained on specific data or preferences, allowing for tailored content and customized user experiences.

Generative AI can also serve as a starting point for further creative exploration. It can provide inspiration to artists, designers, and writers by generating diverse variations and exploring creative possibilities.

Here are some key benefits of generative AI at a glance:

Choosing a career course in generative AI can be a daunting task, but it's essential to get it right. You should consider your current experience and specific career aspirations when selecting a course.

Beginners should look for courses that introduce the basics of AI and machine learning, progressing to generative models. This will give you a solid foundation to build on.

Intermediate learners might benefit from courses focusing on specific generative techniques like GANs and VAEs, along with their applications. This will help you develop more advanced skills.

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Reviewing course content, instructor expertise, and learner feedback can help ensure the course aligns with your career goals. It's also a good idea to check the course curriculum and see if it covers the topics you're interested in.

Here are 10 popular generative AI courses to consider:

You can also consider earning a certificate in a specific area of generative AI, such as natural language processing (NLP), image generation, or deep learning models. This can be a great way to validate your expertise and stand out in the job market.