About SNA Technologies:
SNA Technologies Inc. (SNAT), established in 2006 and headquartered in Michigan, USA, is a global leader in IT solutions and training. We specialize in delivering innovative AI-driven training and consulting services that empower IT professionals worldwide. As a recognized member of The Open Group, we contribute to the development of global standards in enterprise architecture and IT solutions. With a team of highly skilled professionals, we provide cutting-edge AI and data science education tailored to real-world industry needs.
Why Choose SNA?
The AI Developer Advanced course provides an in-depth exploration of advanced AI concepts and techniques, with a focus on deep learning, Generative AI, and practical applications. This course is designed for experienced AI developers and data scientists who want to expand their expertise in building sophisticated AI models and solutions.
Key reasons to choose SNA for your advanced AI training:
– Expert instructors with industry experience and deep technical knowledge.
– Focus on real-world applications, including deep learning, advanced prompt engineering, and ensemble learning.
– Hands-on training with leading AI tools and frameworks such as TensorFlow and PyTorch.
– Access to exclusive case studies and projects for practical learning.
– 60 hours of comprehensive, advanced-level training designed to enhance your AI development skills.
Course Approach
This 60-hour course provides a blend of theoretical knowledge and hands-on practical experience. Participants will dive deep into deep learning frameworks, advanced Generative AI, and the latest trends in AI development. The course emphasizes practical coding, model building, and optimization, ensuring that learners gain the skills necessary to create advanced AI solutions using state-of-the-art technologies like TensorFlow and PyTorch.
Summary
The AI Developer Advanced course equips participants with the skills needed to tackle complex AI problems and build advanced AI models. Participants will gain expertise in deep learning frameworks, advanced Generative AI techniques, and model optimization strategies, preparing them to develop sophisticated AI-driven applications and solutions.
Learning Goals
By the end of the course, participants will be able to:
– Understand and apply advanced deep learning concepts using TensorFlow and PyTorch.
– Develop and optimize Generative AI models for real-world applications.
– Use advanced prompt engineering techniques to enhance AI model performance.
– Build and evaluate ensemble learning models for better prediction accuracy.
– Implement model selection, training, and validation in complex AI systems.
– Optimize AI models for performance, scalability, and accuracy.
Topics Covered (60 Hours)
- Deep Learning with TensorFlow
- Introduction to deep learning with TensorFlow.
- Building and training deep neural networks.
- Advanced techniques for model optimization and evaluation.
- Deep Learning with PyTorch
- In-depth exploration of deep learning with PyTorch.
- Hands-on experience with building and optimizing models in PyTorch.
- Comparison of TensorFlow and PyTorch frameworks.
- Advanced Concepts of Generative AI
- Techniques in Generative AI and its applications.
- Working with generative models such as GANs and VAEs.
- Hands-on exercises in creating synthetic data and AI-driven content.
- Advanced Concepts of Prompt Engineering
- Introduction to advanced prompt engineering for AI models.
- Crafting optimized prompts for improving AI responses.
- Practical examples of prompt engineering in action.
- Advanced Concepts of GPT
- In-depth study of GPT models and advanced techniques.
- Fine-tuning GPT models for specialized tasks.
- Exploring real-world applications of GPT-based models.
- Ensemble Learning
- Overview of ensemble learning techniques.
- Combining multiple models to improve accuracy and performance.
- Implementing ensemble methods such as Random Forest and Boosting.
- Practical Project
- Hands-on project where learners apply advanced AI techniques.
- Building and deploying a deep learning model using TensorFlow or PyTorch.
- Real-world case studies and practical applications.
- Model Selection
- Choosing the right AI model for various tasks.
- Factors influencing model selection and performance.
- Practical examples of model selection for real-world applications.
- Model Training
- Techniques for training AI models on large datasets.
- Hyperparameter tuning and optimization for better results.
- Practical exercises on model training using TensorFlow and PyTorch.
- Model Optimization
- Methods for optimizing AI models to enhance performance.
- Techniques for reducing model complexity while maintaining accuracy.
- Hands-on experience in optimizing models for scalability.
- Model Evaluation
- Evaluating model performance using key metrics.
- Understanding overfitting and underfitting issues.
- Real-world case studies for evaluating AI models in production.
- Model Validation
- Validating AI models for real-world applications.
- Ensuring the robustness and generalization of models.
- Practical exercises on model validation techniques.
What Attendees Get
Course registration includes:
– Comprehensive course materials and workbooks.
– Practical exercises and hands-on projects with TensorFlow and PyTorch.
– Access to a community of AI experts and industry professionals.
– Certificate of Completion upon successful course completion.
Who Will Benefit?
This course is ideal for:
– Experienced AI developers and data scientists looking to deepen their knowledge.
– IT professionals transitioning into advanced AI development roles.
– Engineers and software developers seeking expertise in deep learning and Generative AI.
– Students and professionals with a strong programming background in AI.
Prerequisites
Participants should have a solid understanding of AI and machine learning concepts, including experience with Python programming and basic deep learning techniques. Familiarity with tools like TensorFlow or PyTorch will be beneficial but is not mandatory.
Professional Development Units (PDUs)
Participants may be eligible to apply for PDUs towards continuing education requirements with relevant certification bodies.
Course Features
- Lectures 0
- Quizzes 0
- Duration 60 hours
- Skill level All levels
- Language English
- Students 0
- Assessments Yes
