6
Lessons
2 days
Duration
English
Language
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OBJECTIVES:
- This course is designed for junior engineers who want to learn about generative artificial intelligence (AI) techniques and algorithms. Participants will gain a fundamental understanding of generative models and how they can be applied to generate new data samples, images, text, and other types of content. The course includes hands-on labs to provide practical experience in building and training generative AI models.
- This two-day course provides a condensed yet comprehensive introduction to generative AI for junior engineers. Through a combination of lectures and hands-on labs, participants will gain a solid understanding of fundamental generative models and practical experience in building and training generative AI models using Python and popular deep learning frameworks.
Course features:
- Practical hands on
- Lab sessions
- Training by experienced faculty
PRE-REQUISITES:
- Basic Programming Knowledge: Participants should have a basic understanding of programming concepts and syntax in at least one programming language such as Python, Java, or similar.
- Familiarity with Python: Basic familiarity with Python programming language is recommended, as the hands-on labs will be conducted using Python and popular machine learning libraries such as TensorFlow or PyTorch.
- Mathematics Knowledge: Basic knowledge of linear algebra, calculus, and probability theory is beneficial for understanding the underlying principles of generative AI algorithms.
Lab 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