Republic Polytechnic (RP)-NVIDIA Rapid AI App Development: From Deep Learning to Large Language Models (LLMs)
Training Provider: Republic Polytechnic
Course Reference: TGS-2025057415
S$330
Original: S$1,100
Save S$770
About This Course
Dive into the cutting-edge world of Generative AI (GenAI) and Large Language Models (LLMs) with this comprehensive 2-day course. Get hands-on with deep learning in computer vision and Natural Language Processing (NLP), training models from the ground up or leveraging powerful pretrained options. Explore GenAI applications, build a text-to-image generator using diffusion models, and master Transformer architectures. Deploy LLMs for advanced tasks like ntegrating text generation, multimodal learning, and agentic orchestration. Earn two NVIDIA Design Learning Institute (DLI) Certificates of Competency to showcase your newfound expertise.
What You'll Learn
1. Learn the fundamental techniques and tools required to train a deep learning model
2. Gain experience with common deep learning data types and model architectures
3. Enhance datasets through data augmentation to improve model accuracy
4. Leverage transfer learning between models to achieve efficient results with less data and computation
5. Build confidence to take on your own project with a modern deep learning framework
6. Find, pull in, and experiment with the HuggingFace model repository and Transformers Application Programming Interface (API)
7. Use encoder models for tasks like semantic analysis, embedding, question-answering, and zero-shot classification.
8. Work with conditioned decoder-style models to take in and generate interesting data formats, styles, and modalities.
9. Kickstart and guide generative AI solutions for safe, effective, and scalable natural data tasks.
10. Explore the use of LangChain and LangGraph for orchestrating data pipelines and environment-enabled agents.
2. Gain experience with common deep learning data types and model architectures
3. Enhance datasets through data augmentation to improve model accuracy
4. Leverage transfer learning between models to achieve efficient results with less data and computation
5. Build confidence to take on your own project with a modern deep learning framework
6. Find, pull in, and experiment with the HuggingFace model repository and Transformers Application Programming Interface (API)
7. Use encoder models for tasks like semantic analysis, embedding, question-answering, and zero-shot classification.
8. Work with conditioned decoder-style models to take in and generate interesting data formats, styles, and modalities.
9. Kickstart and guide generative AI solutions for safe, effective, and scalable natural data tasks.
10. Explore the use of LangChain and LangGraph for orchestrating data pipelines and environment-enabled agents.
Entry Requirements
English Proficiency
Course Details
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