Please note that this list of topics is based on our standard course offering, evolved from typical industry uses and trends. We’ll work with you to tune this course and level of coverage to target the skills you need most. Topics, agenda and labs are subject to change, and may adjust during live delivery based on audience interests, skill-level and participation.
Day 1
- Introduction to Generative AI
- Unveil the world of generative AI and its applications.
- Brief history of generative AI
- Overview of generative models
- Types of generative AI techniques
- Applications of generative AI
- Lab: Setting up the development environment
- Deep Learning and GANs
- Dive into the fundamentals of GANs and their applications.
- Introduction to deep learning
- Basic components of GANs
- GAN architecture and training process
- Common GAN variants and applications
- Lab: Simple GAN implementation: Generate synthetic images with GAN; using TensorFlow, Keras
- Variational Autoencoders (VAEs)
- Explore VAEs and learn their applications in generative AI.
- Introduction to VAEs
- VAE architecture and training process
- Applications of VAEs
- Comparing VAEs and GANs
- Lab: VAE for image generation: Create images with a VAE model; using TensorFlow and Keras
Day 2
- Natural Language Generation (NLG)
- Uncover the power of NLG and its applications in generative AI.
- Introduction to NLG
- Overview of language models
- Transformer architecture and variants
- Applications of NLG in generative AI
- Lab: Text generation using GPT: Generate text with GPT-based models; using Hugging Face Transformers
- Ethical AI / Ethics and Responsible AI
- Understand the ethical implications of generative AI applications.
- AI ethics and its importance
- Bias in generative models
- Responsible AI and best practices
- Future research and open problems
- Lab: Bias detection and mitigation: Identify and mitigate biases in generative models; using TensorFlow, AI Fairness 360
Day 3
- Multimodal Generative AI
- Discover the potential of combining different data modalities in generative AI.
- Introduction to multimodal AI
- Text-to-image synthesis
- Audio-to-video synthesis
- Applications of multimodal generative AI
- Style Transfer and Neural Art
- Explore the creative side of generative AI with style transfer techniques.
- Introduction to style transfer
- Neural style transfer algorithms
- Applications of style transfer in generative AI
- Limitations and future directions
- Lab: Implement neural style transfer: Create artistic images using neural style transfer; using TensorFlow, Keras, VGG-19
- Applying Generative AI in the Real World
- Gain insights on practical applications of generative AI across various domains.
- Generative AI in marketing and advertising
- Generative AI in entertainment and gaming
- Generative AI in healthcare and life sciences
- Generative AI in finance and economics
- Lab: Develop a simple AI-powered application: Build a practical generative AI application; using Pinecone and LangChain
- Capstone: Building and Deploying Generative AI Models
- Learn best practices for building, fine-tuning, and deploying generative AI models.
- Model selection and fine-tuning
- Deployment strategies
- Monitoring and maintenance
- Ensuring user privacy and security
- Lab: Deploy a generative AI model
Working in an interactive learning environment, led by our engaging AI expert you’ll:
- Develop a strong foundational understanding of generative AI techniques and their applications in software development.
- Gain hands-on experience working with popular generative AI models, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformer models.
- Master the use of leading AI libraries and frameworks, such as TensorFlow, Keras, and Hugging Face Transformers, for implementing generative AI models.
- Acquire the skills to design, train, optimize, and evaluate custom generative AI models tailored to specific software development tasks.
- Learn to fine-tune pre-trained generative AI models for targeted applications and deploy them effectively in various environments, including cloud-based services and on-premises servers.
- Understand and address the ethical, legal, and safety considerations of using generative AI, including mitigating biases and ensuring responsible AI-generated content.
This course is highly technical in nature. In order to gain the most from attending you should possess the following incoming skills:
- Python programming experience (Python syntax and constructs, experience with NumPy and Pandas)
- Basic understanding of artificial intelligence and machine learning concepts (supervised and unsupervised learning, neural networks, optimization techniques)
- Some experience with data manipulation and preprocessing (working with various data formats, such as text, images, and structured data, preprocessing and cleaning data for use in machine learning models.
Ten (10) business days’ notice is required to reschedule a class with no additional fees. Notify TOPTALENT LEARNING as soon as possible at 469-721-6100 or by written notification to info@toptalentlearning.com to avoid rescheduling penalties.
Please contact our team at 469-721-6100; we will gladly guide you through the online purchasing process.
You will receive a receipt and an enrollment confirmation sent to the email you submitted at purchase. Your enrollment email will have instructions on how to access the class. Any additional questions our team is here to support you. Please call us at 469-721-6100.
If a student is 15 minutes late, they risk losing their seat to a standby student. If a student is 30 minutes late or more, they will need to reschedule. A no-show fee will apply. Retakes are enrolled on a stand-by basis. The student must supply previously issued courseware. Additional fees may apply.
You will receive a ‘Certificate of Completion’ once you complete the class. If you purchased an exam voucher for the class, a team member from TOPTALENT LEARNING will reach out to discuss your readiness for the voucher and make arrangements to send it.