16
Lessons
5 days
Duration
English
Language
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OBJECTIVE:
- This course is designed to provide participants with a comprehensive understanding of generative artificial intelligence (AI) techniques and algorithms.
- Participants will learn how to create AI models capable of generating new data samples, images, text, and other types of content.
- The course covers both theoretical concepts and practical hands-on exercises to empower participants to build and deploy generative AI models effectively.
- This 5-day course outline provides a structured curriculum covering essential topics in generative AI, including theory, hands-on exercises, and project work.
- With a combination of lectures, labs, and project-based learning, participants will gain practical skills and experience to apply generative AI techniques in various domains.
Course features:
- Practical hands on
- Lab sessions
- Training by experienced faculty
PRE-REQUISITES:
- Basic Python Programming: Participants should have a basic understanding of Python programming language, including variables, data types, control structures, functions, and libraries.
- Fundamental Machine Learning Concepts: Familiarity with fundamental machine learning concepts such as supervised learning, unsupervised learning, neural networks, and training/validation/testing processes is recommended.
- Mathematics Knowledge: Basic knowledge of linear algebra, calculus, and probability theory is beneficial for understanding the underlying principles of generative AI algorithms.
LAB SETUP REQUIREMENTS:
- Python Environment: Participants should have Python installed on their computers. They can install Python from the official Python website (https://www.python.org/) or using package managers like Anaconda or Miniconda.
- Integrated Development Environment (IDE): Participants can use any Python IDE of their choice for writing and running code. Recommended IDEs include PyCharm, Jupyter Notebook, and Google Colab.
- Machine Learning Libraries: Participants should have the necessary Python libraries installed for machine learning and deep learning. These include TensorFlow, Keras, PyTorch, and scikit-learn. They can install these libraries using pip or conda.
- GPU Support (Optional): For running computationally intensive deep learning models, participants may benefit from having access to a GPU-enabled environment. This can be achieved through cloud platforms like Google Cloud Platform (GCP), Amazon Web Services (AWS), or using GPU-enabled local machines.
Learning Path
- Day 1: Introduction to Generative AI
- Introduction to generative artificial intelligence and its applications
- Types of generative AI models: generative adversarial networks (GANs), variational autoencoders (VAEs), and autoregressive models
- Understanding the difference between generative and discriminative models
- Probability distributions and their role in generative modeling
- Maximum likelihood estimation (MLE) and maximum a posteriori estimation (MAP)
- Sampling techniques for generating data samples from probability distributions
- Overview of generative adversarial networks (GANs) architecture
- Training procedure: generator and discriminator networks
- Applications of GANs in image generation, style transfer, and data augmentation
- Participants implement a basic GAN model using TensorFlow or PyTorch
- Training the GAN on a simple dataset and generating new synthetic samples
- Experimenting with hyperparameters and architecture modifications
- Day 2: Advanced Generative Models
- Introduction to variational autoencoders (VAEs) and their architecture
- Objective function: reconstruction loss and KL divergence
- Applications of VAEs in image generation, anomaly detection, and dimensionality reduction
- Overview of autoencoder architecture and training procedure
- Denoising autoencoders, sparse autoencoders, and convolutional autoencoders
- Hands-on exercise: implementing and training an autoencoder model
- Introduction to adversarial autoencoders (AAEs) and their architecture
- Combining elements of GANs and VAEs for improved generative modeling
- Applications of AAEs in image generation and unsupervised representation learning
- Participants implement and train a variational autoencoder (VAE) model using TensorFlow or PyTorch
- Generating new images and analyzing latent space representations
- Fine-tuning VAE hyperparameters and architecture for improved performance
- Day 3: Text Generation and Natural Language Processing (NLP)
- Overview of text generation techniques and challenges
- Markov models, recurrent neural networks (RNNs), and transformers for text generation
- Applications of text generation in language modeling, chatbots, and content creation
- Introduction to recurrent neural networks (RNNs) architecture
- Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) cells
- Training RNNs for text generation tasks
- Introduction to transformer architecture and self-attention mechanism
- Overview of transformer-based models such as GPT (Generative Pre-trained Transformer) and BERT (Bidirectional Encoder Representations from Transformers)
- Fine-tuning pre-trained transformer models for text generation tasks
- Participants implement and train RNN and transformer-based models for text generation tasks using TensorFlow or PyTorch
- Generating new text samples and evaluating model performance
- Experimenting with different architectures and training strategies for text generation
- Day 4: Image Generation and Style Transfer
- Overview of image generation techniques and challenges
- PixelCNN, auto-regressive models, and conditional image generation
- Applications of image generation in computer vision, art generation, and image synthesis
- Introduction to conditional generative models such as conditional GANs and conditional VAEs
- Conditioning on class labels, attributes, or text descriptions for controlled image generation
- Hands-on exercise: implementing conditional generative models for image generation
- Overview of style transfer techniques using neural networks
- Gatys et al.’s neural style transfer algorithm
- Image-to-image translation with conditional GANs (cGANs) and pix2pix models
Participants implement and train conditional generative models for image generation tasks using TensorFlow or PyTorch
- Experimenting with different conditioning strategies and loss functions
- Applying style transfer techniques to transform images with different artistic styles
- Day 5: Advanced Topics and Project Work
- Overview of advanced generative models such as flow-based models and energy-based models
- RealNVP (Real Non-Volume Preserving) and Glow (Generative Flow with Invertible 1×1 Convolutions) architectures
- Applications of flow-based models in density estimation and image generation
- Brainstorming project ideas and use cases for generative AI applications
- Defining project goals, requirements, and deliverables
- Forming project teams and assigning roles and responsibilities
- Participants work on individual or group projects implementing generative AI models for specific tasks or applications
- Guidance and support provided by instructors for project implementation and troubleshooting
- Participants present their projects to the class and instructors
- Projects are evaluated based on creativity, technical complexity, and practical applicability
- Feedback provided to participants for further improvement and learning