Fundamentals of Deep Learning
Artificial Intelligence (AI) technology is becoming increasingly important in the modern world. AI has the potential to transform various fields and revolutionize several industries, ranging from healthcare, transportation, finance, education, marketing, and entertainment.
Deep learning is a powerful AI approach that uses multi-layered artificial neural networks to deliver state-of-the-art accuracy in tasks such as object detection, speech recognition, and language translation.
San Jos茅 State University and NVIDIA Deep Learning Institute (DLI) invite you to attend a Deep Learning hands-on training workshop. The participants will have the unique opportunity to:
- Learn and apply the fundamental techniques to train a deep learning model
- Gain hands-on experience via NVIDIA online labs (Jupyter notebooks)
- Earn a NVIDIA certificate, upon successfully passing the final assessment
- Receive a completion certificate from International Gateways at San Jos茅 State University
- Spend a day exploring interests and opportunities in this field and connect with like-minded peers
Don鈥檛 miss the chance to dive into Deep Learning and gain a competitive edge!
Program Date: Sunday, November 9, 2025
Time: 8:30am-4:30pm (includes lunch break)
Location: San Jos茅 State University Campus, 1 Washington Square, San Jos茅 CA 95192
Instructor
Min-Huey (Josephine) Wang is an Instructor affiliated with San Jos茅 State University and a certified NVIDIA Instructor authorized to lead NVIDIA workshops. She holds a PhD in Physics and specializes in Data Science and Deep Learning, with a focus on computer vision and enhancing Face ID security. Prior to her instructor role, Josephine was a Data Scientist at Apple Inc., where she led advanced statistical analysis and machine learning projects, and a Staff Scientist at SLAC/Stanford University, conducting cutting-edge research in accelerator physics and data analysis. As an adjunct associate professor at National Tsing Hua University and an experienced workshop instructor, she is passionate about hands-on learning and technical mentorship. Josephine is dedicated to equipping learners with the confidence and knowledge to apply deep learning techniques effectively in real-world applications. Her mission is to bridge the gap between theory and practice by empowering participants to build and deploy their own deep learning models. She believes AI is transforming the world and aims to inspire learners to embrace its opportunities thoughtfully and adaptively. Through her instruction, Josephine fosters an engaging environment that prepares learners to navigate the evolving challenges and possibilities of AI.
Learning Outcomes
- Learn fundamental concepts of AI, machine learning, and deep learning
- Acquire the fundamental techniques and tools required to train a deep learning model (regression line, loss curve, activation functions, classification, convolutional neural networks)
- Gain experience with common deep learning data types and model architectures
- Enhance datasets through data augmentation to improve model accuracy
- Leverage transfer learning between models to achieve efficient results with less data and computation
- Earn a potential NVIDIA certificate upon passing the assessment exam criteria
Workshop Outline
- Explore the fundamental mechanics and tools involved in successfully training deep learning neural networks
- Train your first computer vision model to learn the process of training
- Introduce convolutional neural networks to improve the accuracy of predictions in vision applications
- Apply data augmentation to improve model generalization
- Use a pre-trained image classification model
- Leverage transfer learning to train and detect adjacent images
- Introduce natural language processing and advanced architectures
Prerequisites
An understanding of fundamental, such as functions, loops, dictionaries, and arrays; familiarity with ; and an understanding of how to compute a regression line.
Suggested materials to satisfy prerequisites: , or take a free online Python beginner class per your preference. You will have trouble following the course or taking full advantage of the class without Python knowledge. Statistics background is preferred. Understanding of regression model evaluation metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE) will enhance your learning experience.
Technologies: PyTorch, Pandas
Assessment Type: Skills-based coding assessments evaluate participants鈥� ability to train a deep learning model to high accuracy.
91猎奇 Completion Certificate: Participants will receive a certificate of completion from International Gateways at San Jos茅 State University.
NVIDIA DLI Certificate: Upon satisfying the assessment exam passing criteria, participants will receive a NVIDIA Deep Learning Institute (DLI) certificate to recognize their subject matter competency.
Hardware Requirements: Desktop or laptop computer capable of running the latest version of Chrome or Firefox.
Registration and Payment
Registration deadline: The deadline is October 24th, 2025 or until the course reaches capacity. Applicants will be placed on a waitlist if the course is full.
A final email confirmation with course instructions will be sent to participants on October 31st, 2025. Applicants should check their email on that day about the course arrangements.
The course fee is $550.
Complete this registration form to receive an email notification with a payment link.
Course fees are non-refundable after October 26th, 2025. However, if the course is cancelled, the fees will be fully refunded.
