Generative AI

Generative AI Engineers are specialized professionals focusing on the design, implementation, and optimization of generative models.

Generative AI Engineers are specialized professionals focusing on the design, implementation, and optimization of generative models. They play a crucial role in developing systems that can generate content, such as text, images, music, or even synthetic data.

Our course covers these roles extensively

• Text Generation: Creating content such as articles, stories, and code.
• Image Generation: Producing realistic images or modifying existing ones.
• Music and Audio: Composing music or generating realistic audio samples.
• Video: Creating or editing video content.
• Design and Art: Assisting in creative design processes by generating artwork and designs.
• Conversational Agents: Developing chatbots and virtual assistants that can generate human-like responses.

Text Generation Tools

1. OpenAI GPT-3/GPT-4:

• A state-of-the-art language model that can generate human-like text, translate languages, summarize text, and more.
• Available through the OpenAI API.

1. Hugging Face Transformers:

• An open-source library that provides pre-trained transformer models for text generation, translation, summarization, and more.
• Includes models like GPT-2, GPT-3, BERT, T5, and others.

1. Google T5 (Text-to-Text Transfer Transformer):

• A versatile transformer model that treats all NLP tasks as a text-to-text problem.
• Available through the Hugging Face library and Google Cloud’s AI services.

1. AI Dungeon:

• An interactive text-based adventure game that uses GPT-3 to generate storylines based on user input.
• Demonstrates the creative potential of generative text models.

Image Generation Tools

1. DALL-E / DALL-E 2:

• Developed by OpenAI, DALL-E generates images from textual descriptions.
• Can create original, high-quality images based on detailed prompts.

1. DeepArt:

• An AI tool that creates artwork inspired by famous artists or any other image style.
• Uses neural networks to apply artistic styles to images.

1. RunwayML:

• A platform that provides various generative models for image and video creation.
• Includes models for image synthesis, style transfer, and more.

1. Artbreeder:

• A collaborative tool that uses generative adversarial networks (GANs) to create and blend images.
• Users can create and modify artwork by combining various images.

Music and Audio Generation Tools

1. OpenAI Jukedeck:

• An AI tool for creating original music compositions.
• Users can generate music tracks by specifying parameters like genre, mood, and length.

1. AIVA (Artificial Intelligence Virtual Artist):

• An AI composer that creates music for various purposes, including film scores, video game soundtracks, and more.
• Uses deep learning to compose music in different styles.

1. Amper Music:

• An AI music composition tool that allows users to create custom music tracks.
• Users can specify style, mood, and instrumentation to generate unique compositions.

Video Generation Tools

1. DeepDream:

• A computer vision program that uses a convolutional neural network to enhance and generate images.
• Often used to create surreal, dream-like visuals in videos.

1. Synthesia:

• A video generation platform that creates AI-driven videos with digital avatars.
• Users can generate videos by providing text scripts, which are then read by digital avatars.

Code Generation Tools

1. OpenAI Codex:

• A descendant of GPT-3 fine-tuned for programming tasks.
• Powers GitHub Copilot, an AI-powered code completion tool that assists developers by generating code snippets and suggestions.

1. TabNine:

• An AI code completion tool that uses deep learning to provide code suggestions and autocompletion.
• Supports multiple programming languages and integrates with popular code editors.

Visual and Design Tools

1. DeepArt.io:

• An online tool that applies artistic styles to images using neural networks.
• Users can transform their photos into artwork resembling famous paintings.

1. RunwayML:

• A creative toolkit for artists, designers, and researchers to use AI models in their projects.
• Provides tools for image generation, style transfer, and more.

General-Purpose Generative Tools

1. Google Colab:

• A cloud-based platform that provides free access to GPUs and TPUs for running machine learning models.
• Users can experiment with various generative AI models and frameworks.

1. TensorFlow:

• An open-source machine learning framework developed by Google.
• Provides tools for building and training generative models like GANs, VAEs, and transformers.

1. PyTorch:

• An open-source deep learning framework developed by Facebook’s AI Research lab.
• Widely used for developing and experimenting with generative models.

Download course here
Course Modules

Part 1: Introduction to Generative AI

Module 1: Overview of Generative AI

• Introduction to Generative AI
• Historical Evolution and Key Milestones
• Applications and Impact of Generative AI

Module 2: Fundamentals of Machine Learning and Deep Learning

• Basics of Machine Learning
• Neural Networks and Deep Learning
• Overview of TensorFlow and PyTorch

Part 2: Core Concepts in Generative Models

Module 3: Introduction to Generative Models

• Types of Generative Models: GANs, VAEs, Normalizing Flows
• Training Techniques and Challenges
• Evaluation Metrics for Generative Models

Module 4: Generative Adversarial Networks (GANs)

• Overview of GAN Architecture
• GAN Training: Discriminator and Generator
• Variants of GANs: DCGAN, WGAN, CycleGAN
• Hands-On with GAN Implementations

Module 5: Variational Autoencoders (VAEs)

• Introduction to VAEs
• VAE Architecture and Training
• Applications of VAEs in Image Generation

Module 6: Normalizing Flows

• Introduction to Normalizing Flows
• Flow-based Models and Their Advantages
• Implementation and Applications

Part 3: Natural Language Processing and Generative AI

Module 7: Fundamentals of NLP

• Introduction to NLP and Language Models
• Text Preprocessing Techniques
• Word Embeddings: Word2Vec, GloVe

Module 8: Transformer Models and Language Models

• Overview of Transformer Architecture
• Key Transformer Models: BERT, GPT, T5
• Fine-Tuning Transformers for NLP Tasks

Module 9: Building with ChatGPT and Similar Models

• Introduction to ChatGPT and Its Capabilities
• Fine-Tuning ChatGPT for Specific Applications
• Building Conversational Agents and Chatbots

Module 10: Introduction to Gemini and Other Modern Models

• Overview of Gemini and Its Innovations
• Comparison with Other Generative Models
• Hands-On with Gemini for Text and Image Generation

Part 4: Advanced Techniques in Generative AI

Module 11: Conditional and Few-Shot Learning

• Conditional Generative Models
• Few-Shot and Zero-Shot Learning Techniques
• Applications in Text, Image, and Video Generation

Module 12: Advanced Topics in Generative AI

• Training GANs and VAEs
• Conditional GANs
• Applications in Image and Text Generation

Part 5: Practical Applications and Use Cases

Module 13: Generative AI in Image Synthesis

• Introduction to Image Synthesis Techniques
• Using GANs for Image Generation
• Hands-On with StyleGAN and BigGAN

Module 14: Generative AI in Text and Music

• Text Generation with GPT Models
• Music Generation with RNNs and Transformers
• Creative Applications of Generative AI

Module 15: Developing Real-World Applications

• Building a Chatbot with GPT
• Generating Art and Content with AI
• Ethical Considerations and Safety in Generative AI

Part 6: Tools and Deployment

Module 16: Tools for Developing Generative AI

• Overview of AI Frameworks: TensorFlow, PyTorch, Hugging Face
• Using Pre-trained Models and Fine-Tuning
• Developing and Training Custom Models

Module 17: Deployment and Scaling Generative Models

• Model Deployment Techniques: Flask, Django, FastAPI
• Scaling Generative Models with Cloud Services
• Monitoring and Maintaining AI Models in Production

Capstone Project

• Designing and Building a Generative AI Application
• End-to-End Development: Data Collection, Model Training, and Deployment
• Demonstrating the Application to Solve Real-World Problems

Assessment and Certification

• Quizzes and Assignments
• Practical Lab Projects
• Final Project Presentation and Evaluation
• Certification of Completion
•