Generative AI Tutorial for Beginners

Generative AI Tutorial for Beginners

Did you ever dream of a machine that could tell decent stories, sing beautiful music, or even paint exquisite art at the snap of a finger? Well, dream no more because Generative AI made that possible!

In this comprehensive Generative AI tutorial for beginners, we’re going to take a thought-provoking journey through the world of generative AI, breaking down its complex concepts. If you’re a tech enthusiast, a creative professional, or simply curious about the next AI sensation, this Gen AI tutorial is for you. We’ll learn what gen AI is, explore fascinating generative AI examples, and look at how it’s revolutionizing various sectors, as well as getting our hands dirty with some basic code.

Now, let’s begin tapping into your creative potential with AI gen! Check out our Generative AI Course syllabus to get started.

What Exactly is Generative AI (Gen AI)? The Core Meaning

Essentially, generative AI meaning is a branch of artificial intelligence that can produce novel, unique information not previously in existence. It is distinct from traditional AI, which can take data and make predictions or sort information (like identifying spam messages or recognizing faces), in that it generates.

Think of this:

• Traditional AI (Discriminative AI): “Is this a picture of a dog or a cat?” (It classifies between previously existing categories.)
• Generative AI (Gen AI): “Create an image of a cat flying a spaceship.” (It creates something entirely new based on patterns that it has learned.)

These intelligent systems capitalize on humongous amounts of available information – be it text, images, audio, or even complex scientific information. They can then produce new outputs that mimic the shape, style, and content of their training inputs, frequently in remarkably imaginative and lifelike ways, after learning the underlying patterns, structures, and correlations in this data.

The magic of generative AI is that it is able to understand the way something is made and then apply what it knows to generate something new.

Explore: Artificial Intelligence Tutorial for Beginners.

How Generative AI Works: The Minds Behind the Creation

The inner workings within gen AI might seem magical, but they are founded on sophisticated machine learning models, particularly neural networks. Here’s a simplified overview:

• Learning from Data: Imagine showing millions of cat photos to an artificial intelligence system. It doesn’t commit every image to memory. Rather, it learns the underlying features that constitute a “cat” – the structure of its ears, the nature of its fur, the position of its eyes, and so forth. It creates an internal representation of “cat-ness.”
• Identifying Patterns: These models are excellent at finding underlying patterns and relationships within the data. For instance, in text, they catch grammar, sentence structure, common phrases, and even the idiosyncrasies of human language.
• Creating New Content: Once it has learned these intricate patterns, the generative AI model can use this to create new content. If you give it a “prompt” (a unique starting point or directive), it uses the learned patterns to create an output that is in line with what it has learned but clearly new.

Some robust architectural designs underlie generative AI:

Generative Adversarial Networks (GANs): Imagine two neural networks, a “generator” and a “discriminator,” playing a game of cat and mouse.

• The Generator tries to create realistic-fitting replicas (e.g., pictures).
• The discriminator attempts to determine if the input consists of the generator’s replicas or actual data.
• They learn from one another, the generator getting better at fooling the discriminator, and the discriminator getting better at identifying fakes. The adversarial process drives both to improve, resulting in very realistic generated content.

Variational Autoencoders (VAEs): VAEs focus on learning a compressed, meaningful representation of data (a “latent space”). They can then sample from the latent space and produce new data points that are similar to training data but still new.

Transformer Models: These are the stars of the show behind many of today’s most remarkable generative AI illustrations, particularly in text and increasingly across other modalities. 

• Transformers are particularly good at picking up on context and relationships over long data sequences. 
• That is what powers Large Language Models (LLMs) such as ChatGPT to be able to generate contextual and coherent text.

Diffusion Models: They work by progressively adding noise to an image (or data) until it becomes random static, and then learning how to reverse that process and denoise progressively random static into a recognizable picture. This “diffusion” allows them to generate very high-quality, diverse content. That’s what drives most of the hip AI art generators.

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Mind-Blowing Generative AI Examples in Action

Generative AI is no longer a theoretical tool but is already transforming how we interact with technology and generate content across numerous different fields. Some of the most fascinating generative AI examples are:

Text Generation (The Age of LLMs)

This is where gen AI has probably made its most obvious contributions. Large Language Models (LLMs) can:

• Write Articles and Blog Posts: Give it a topic and some keywords, and it will write a full article on your behalf.
• Write Emails and Reports: Automate mundane writing tasks and free your time.
• Generate Creative Content: From poetry and song lyrics to scripts and imaginative stories.
• Translate Languages: Sophisticated, context-aware translations.
• Summarize Long Documents: Get the overall feel of long reports in seconds.
• Write Code: Programmers are now using more and more AI gen tools to finish code, debug, and even entire functions automatically.

Example Text Generation Prompt:

“Write a short, interesting blog post opening about the benefits of telecommuting.”

Potential AI Gen Output:

“Like dinosaurs, the old office model is going extinct. With the fast-paced world of today, telecommuting is no longer a luxury; it’s a business driver for productivity, ease, and enhanced work-life balance. Imagine ditching the commute, designing your ideal workday, and collaborating with world-class talent – all while relishing the convenience of your desired workspace. This is not a utopian vision; it’s today’s reality that’s revolutionizing the way we work and live.”

Image and Art Generation (AI Art Generators)

This is likely the most visually striking area of generative AI. Applications such as Midjourney, DALL-E, and Stable Diffusion have amazed the world:

• Create Art from Text: Tell it what you want, and the AI creates beautiful pictures, from real-world photographs to abstract works of art.
• Style Transfer: Transfer the artistic style of one picture to another.
• Image Editing and Enrichment: Create variations of existing images, delete objects, or upscale resolutions.
• Product Design: Create infinite design options for products.

Example Prompt for Image Creation (Conceptual):

“A warm, futuristic living room with big windows out onto a green alien world, in the style of a Ghibli anime.”

Audio and Music Creation

Generative AI is not just for eyes; it’s for ears as well!

• Compose Original Music: Generate melodies, harmonies, and even full orchestral pieces in various genres.
• Synthesize Speech: Create natural-sounding voiceovers and audiobooks.
• Sound Effects Generation: Produce custom sound effects for games, films, or applications.
• Audio Enhancement: Denoise audio or separate vocal tracks from instrumental.

Example: Google’s MusicLM can generate music from text descriptions. Imagine typing “a calming jazz tune with a subtle piano and soft drums,” and an AI creates it.

Video Generation (Generative AI Video)

Though still in fast development, generative AI video is a hugely thrilling area.

• Text-to-Video: Synthesize short videos out of text inputs. Picture writing a scene, and the AI brings it to life.
• Realistic Avatars: Develop realistic digital people for presentations or customer support.
• Video Style Transfer: Transpose the visual style of one video onto another.
• Synthetic Data Generation: Develop realistic video data for training other AI systems, particularly autonomous driving or robotics.

Example: OpenAI’s Sora can create extremely realistic and complex generative AI video scenes based on text prompts, with amazing potential for film, animation, and advertising.

Synthetic Data (Data Generation)

This may sound less flashy, but it’s wildly important, particularly for training other AI systems.

• Creation of Training Data: Create synthetic datasets (text, images, numbers) when the data in the real world is limited, sensitive, or too costly to procure.
• Privacy Preservation: Produce data that replicates real data’s statistics without divulging sensitive information for specific individuals.

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Industries Transformed with Generative AI

The influence of gen AI is extensive, impacting many industries:

• Creative Arts & Entertainment: Artists, musicians, writers, and filmmakers are leveraging generative AI as an incredibly effective co-creation platform, accelerating concept development, prototyping, and even end production.
• Marketing & Advertising: Create customized ad copy, images, and video clips in a snap targeted to specific groups.
• Software Development: AI gen tools help developers with code generation, debugging, and test case development, enhancing productivity. Several top generative AI players are specifically targeting this space.
• Healthcare & Pharmaceuticals: Speed up the discovery of drugs by creating new molecular structures, mimic patient data for clinical trials, and design synthetic medical images for diagnostic AI training.
• Education: Customized learning material, automatic essay critique, and interactive learning environments.
• Customer Service: Extremely advanced AI-powered chatbots and virtual agents offering human-like interactions and assistance.
• Design & Manufacturing: Create many iterations of product designs, improve intricate engineering designs, and build 3D models.
• Fashion: Create fresh new clothing patterns, textures, and even collections.

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Getting Started with Generative AI: A Hands-On Peek (Python)

Although the full potential of generative AI typically includes huge models and lots of computational power, you can already experience it using available tools and some Python.

One of the simplest methods to get started is by employing pre-trained models that are accessible via libraries such as Hugging Face’s transformers. For a range of tasks, this library provides a single interface to multiple cutting-edge models.

Begin by installing the transformers library:

pip install transformers torch

(Note: torch is a deep learning library which transformers tend to utilize in the background. You may also see tensorflow as a backup.)

Example 1: Text Generation (with a basic text generator)

Generate some text! We’ll utilize a compact, pre-trained text generation model.

from transformers import pipeline

# Load a text generation pipeline

# ‘distilgpt2’ is a smaller, faster version of GPT-2, great for quick experiments

generator = pipeline(“text-generation”, model=”distilgpt2″)

# Define your prompt

prompt = “Once upon a time, in a land far, far away, there was a brave knight who”

# Generate text

generated_text = generator(prompt, max_new_tokens=50, num_return_sequences=1)

print(generated_text[0][‘generated_text’])

What to expect: The generated output will carry on your story. It could look something like:

“There there lived a brave knight who rode to the furthest depths of the forest. He was aware that he had a challenge ahead of him, so he took precautions by grabbing his gears, such as a magical sword and shield. The knight rode for days, and when he finally reached the destination, he was greeted by a stunning”

Example 2: Text Summarization

Another interesting generative AI example is summarization. You provide it with a long text, and it produces a shorter, meaningful summary.

from transformers import pipeline

summarizer = pipeline(“summarization”)

text_to_summarize = “””

Generative AI is a revolutionary field within artificial intelligence focused on creating new, original content rather than just analyzing existing data. This technology leverages deep learning models, particularly neural networks like Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Diffusion Models, and the famous Transformer architecture. These models are trained on vast datasets, learning the intricate patterns and structures within the data. Once trained, they can generate new text, images, audio, video, and even synthetic data that mimics the characteristics of the training material. Prominent examples include OpenAI’s ChatGPT for text generation, Midjourney and DALL-E for AI art, and emerging tools like Sora for generative AI video. This powerful technology is transforming industries from creative arts and marketing to healthcare and software development, offering unprecedented capabilities for innovation and automation.

“””

summary = summarizer(text_to_summarize, max_length=50, min_length=20, do_sample=False)

print(summary[0][‘summary_text’])

What to expect: A brief summary such as:

“Using deep learning models, generative AI creates new content from original content. It utilizes GANs, VAEs, Diffusion Models, and Transformer architecture. Examples are ChatGPT, Midjourney, DALL-E, and Sora. This technology is revolutionizing industries.”

Example 3: Simple Image Generation (Conceptual – More setup needed for complete local execution)

Local image generation is more computationally intensive and may need larger model downloads, but you can play around with simpler concepts of image generation with diffusers (a Hugging Face sub-library) and pre-trained models. For actual AI art generator functionality, you usually work with cloud-based APIs or execute models on GPUs.

Here’s the conceptual overview of how it works:

# This is a conceptual example for illustration.

# Running a full image generation model locally requires significant resources.

# For beginners, using online services like Hugging Face Spaces or Google Colab

# or APIs from generative AI companies like OpenAI (DALL-E) or Stability AI (Stable Diffusion)

# is recommended for actual image generation.

# from diffusers import DiffusionPipeline

# import torch

# # Load a pre-trained stable diffusion model

# # This line downloads a large model, so it takes time and disk space.

# # pipeline = DiffusionPipeline.from_pretrained(“runwayml/stable-diffusion-v1-5”, torch_dtype=torch.float16)

# # pipeline.to(“cuda”) # If you have a powerful NVIDIA GPU

# # prompt = “a photo of an astronaut riding a horse on mars”

# # image = pipeline(prompt).images[0]

# # image.save(“astronaut_horse_mars.png”)

# print(“To generate images, consider using online platforms or dedicated GPU resources for best results.”)

print(“Image generation with generative AI models typically involves large downloads and significant computing power. For a beginner, trying out web-based tools or cloud platforms is usually easier than local setup.”)

print(“However, the core idea is providing a text prompt and having the AI model generate an image based on that description.”)

Key Takeaways for Code:

• Pipelines: Hugging Face transformers has convenient-to-use “pipelines” for standard tasks.
• Pre-trained Models: You do not train these huge models from scratch. You download and utilize versions that have pre-learned from huge datasets.
• Resources: Although text generation can be done on a CPU, image and generative AI video typically gain hugely from the power of GPUs.

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The Future of Generative AI: Beyond the Horizon

The generative AI space is growing at a pace unprecedented in history. What we witness now is merely the tip of the iceberg. We are likely to see:

• More Multimodal and Sophisticated Generations: AI will generate content effortlessly across modalities – think of one prompt generating a story, sketches, music, and a brief generative AI video clip, all connected and harmonious.
• Greater Accessibility: AI tools will be even simpler to utilize, allowing more individuals, irrespective of technical experience, to leverage the potential of AI gen.
• Hyper-Personalization: Experiences, products, and content will be created in real-time to exactly fit personal preferences and requirements.
• Scientific Breakthroughs: Driving research in medicine, material science, and energy by quickly prototyping and simulating intricate systems.
• Ethical Implications: With increasingly powerful generative AI, consideration of bias, misinformation, intellectual property rights, and responsible AI gen development will be all the more important. Several generative AI businesses are consciously working on these issues.

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Major Generative AI Players and Businesses

Generative AI is rich with innovation from both new upstart companies and mainstream tech giants. Some prominent players are:

• OpenAI: Developers of ChatGPT, DALL-E, and Sora (generative AI video).
• Google (Alphabet): Building models such as Gemini, Imagen, and other tools on Google Cloud.
• Microsoft: Spelling big bucks on OpenAI and incorporating gen AI across its offerings (e.g., Copilot in Microsoft 365).
• Meta: Creating open-source LLMs like LLaMA and pushing research in some of the areas of generative AI.
• Stability AI: Famous for its open-source Stable Diffusion model, a favorite AI art generator.
• Anthropic: Creating strong and secure AI models such as Claude.
• Midjourney: A well-liked AI art generator because of its amazing picture output.
• RunwayML: With a focus on generative AI video and creative software.

These generative AI firms are leading the way in setting new frontiers. 

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Conclusion: Your Generative AI Journey Starts Today!

With your first step, you’ve ventured into the fascinating world of generative AI. From learning about the fundamental generative AI definition to discovering amazing generative AI examples in text, pictures, and even generative AI video, you now possess a solid understanding of this revolutionary technology.

Although this blog gives a good introduction, the realm of generative AI is enormous and ever-changing. To learn the potential to the fullest and create meaningful applications, a guided learning journey is irreplaceable. Our Generative AI Course in Chennai will guide you from an inquisitive starter to a self-assured practitioner.