20
Lessons
35h
Duration
English
Language
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OBJECTIVEs:
- This five-day course is designed for software developers who want to explore the applications of generative artificial intelligence (AI) in software development.
- Participants will learn how to leverage generative AI techniques to automate tasks, enhance creativity, and streamline software development workflows.
- The course includes theoretical concepts, practical demonstrations, and hands-on labs to provide participants with a comprehensive understanding of generative AI in software development.
- This five-day course provides software developers with a comprehensive understanding of generative AI techniques and their applications in software development.
- Through a combination of lectures, demonstrations, and hands-on labs, participants will gain practical experience in building and deploying generative AI models for various tasks and scenarios.
- By the end of the course, participants will be equipped with the knowledge and skills to apply generative AI techniques effectively in their software development projects.
Course features:
- Practical hands on
- Lab sessions
- Training by experienced faculty
PRE-REQUISITES:
- Proficient Programming Skills: Participants should have proficiency in at least one programming language such as Python, Java, or similar.
- Familiarity with Machine Learning: Basic familiarity with machine learning concepts such as supervised learning, unsupervised learning, and neural networks is recommended.
- Knowledge of Python: Experience with Python programming language is essential, as the hands-on labs will be conducted using Python and popular machine learning libraries such as TensorFlow or PyTorch.
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 but Recommended): For running computationally intensive deep learning models, participants are encouraged to have 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 in Software Development
- Introduction to generative artificial intelligence and its applications in software development
- Understanding the different types of generative AI models: GANs, VAEs, autoregressive models, etc.
- Exploring real-world examples of generative AI in software development
- Review of fundamental machine learning concepts relevant to generative AI
- Understanding the training process, loss functions, and optimization algorithms
- Introduction to generative modeling techniques and architectures
- Installing Python, required libraries, and IDEs
- Verifying the installation and setting up a basic project structure
- Introduction to Jupyter Notebooks and Google Colab for interactive coding
- Overview of TensorFlow and PyTorch, popular deep learning frameworks for generative AI
- Getting started with TensorFlow and PyTorch: basic operations, tensors, and neural networks
- Setting up a development environment with TensorFlow and PyTorch
- Day 2: Generative Models for Data Generation
- Overview of GANs architecture and training process
- Understanding the generator and discriminator networks
- Applications of GANs in data generation and image synthesis
- Implementing a basic GAN model using TensorFlow or PyTorch
- Training the GAN on a dataset and generating new synthetic samples
- Fine-tuning hyperparameters and evaluating model performance
- Overview of VAEs architecture and objective function
- Understanding the encoder and decoder networks
- Applications of VAEs in data generation and anomaly detection
- Implementing a variational autoencoder (VAE) model using TensorFlow or PyTorch
- Training the VAE on a dataset and generating new samples
- Analyzing latent space representations and exploring application scenarios
- Day 3: Text and Image Generation
- Overview of recurrent neural networks (RNNs) architecture
- Understanding sequence generation and text generation tasks
- Applications of RNNs in natural language processing and text generation
- Implementing a basic RNN model for text generation using TensorFlow or PyTorch
- Training the RNN on text data and generating new text samples
- Experimenting with different architectures and training strategies for text generation
- Overview of convolutional neural networks (CNNs) architecture
- Understanding image generation tasks and challenges
- Applications of CNNs in image generation, style transfer, and image-to-image translation
- Implementing a CNN-based generative model for image generation using TensorFlow or PyTorch
- Training the model on image data and generating new image samples
- Experimenting with different architectures and training strategies for image generation
- Day 4: Advanced Generative Models
- Introduction to conditional generative models such as conditional GANs and conditional VAEs
- Conditioning on class labels, attributes, or text descriptions for controlled generation
- Applications of conditional generative models in style transfer and image synthesis
- Implementing conditional generative models for specialized tasks using TensorFlow or PyTorch
- Conditioning the model on additional information to control generated outputs
- Experimenting with different conditioning strategies and evaluating model performance
- Overview of advanced generative models such as flow-based models and energy-based models
- RealNVP and Glow architectures for density estimation and image generation
- Applications of flow-based models in data generation and synthesis
- Implementing advanced generative models such as flow-based models using TensorFlow or PyTorch
- Training the model on data and generating new samples
- Exploring advanced techniques and architectures for generative modeling
- Day 5: Deployment and Integration
- Overview of deployment options for generative AI models
- Deployment considerations: scalability, latency, and resource constraints
- Strategies for deploying generative models in production environments
- Best practices for integrating generative AI models into software
development workflows - Tools and frameworks for incorporating generative AI capabilities into applications
- Case studies and examples of generative AI integration in real-world software projects
- Understanding ethical considerations related to generative AI, including bias, fairness, and privacy
- Compliance with regulations and legal frameworks governing the use of generative AI technologies
- Strategies for ensuring responsible and ethical use of generative AI in software development
- Participants present their final projects developed during the course
- Feedback and discussion on project implementations, challenges, and lessons learned
- Wrap-up and conclusion of the course