Home
AI Vibe Coding with PyTorch Deep Learning

Sessions 2 days
Duration 15 hrs
Level Intermediate
Assessment NA

Venue

12 Woodlands Square #07-85/86/87 Woods Square Tower 1, Singapore 737715. 5 mins walk from Woodlands (NS9) MRT station.

The venue is disabled-friendly.

Download Course Brochure

Certificate of Achievement from Tertiary Infotech Academy Pte Ltd - Upon meeting at least 75% attendance and passing the assessment(s), participants will receive a Certificate of Achievement from Tertiary Infotech Academy Pte Ltd.

AI Vibe Coding with PyTorch Deep Learning

Course Code: C539

AI Vibe Coding Series

10 Review(s) | Add Your Review

Build real deep learning models without writing every line of PyTorch yourself. In this hands-on 2-day course you will use AI coding assistants—Cursor, GitHub Copilot and Claude—to vibe code deep learning workflows end to end: describe the model you want in plain English, let the AI generate the PyTorch code for tensors, training loops and network architectures, then review, debug and iterate with follow-up prompts. You will learn the prompting patterns that keep AI-generated deep learning code correct, trainable and reproducible.

Over four practical topics you will vibe code the full deep learning journey—from PyTorch tensors and autograd, to neural networks for regression and classification, to convolutional neural networks and transfer learning for image recognition, and finally recurrent networks for sequence and time series data. By the end of the course, you will have a portfolio of working PyTorch models and a repeatable AI vibe coding workflow you can apply to any deep learning problem.

Course Fee

$700.00 (GST-exclusive)

$763.00 (GST-inclusive)

Mode of Training

Physical Classroom Training
Synchronised Teaching using ZOOM
Corporate Training at Company Premise

Course Date * Course Date

Course Time * Course Time

9:30am - 5:30pm

No. of Participants

Sponsorship

Self-Sponsored
Employer-Sponsored

Name(s) on Certificate *

Additional Message or Emails for Course Notification

* Required Fields

Please bring your own laptop for hands-on training. If you don't have laptop, we can provide spare laptop for training use.

We provide free consultation related to the subject matter after the course.
Please email your queries to enquiry@tertiaryinfotech.com and we will forward your queries to the subject matter experts.

You can register your interest without upfront payment. There is no penalty for withdrawal of the course before the class commences.
We reserve the right to cancel or re-schedule the course due to unforeseen circumstances. If the course is cancelled, we will refund 100% for any paid amount.
Note the venue of the training is subject to changes due to availability of the classroom.

Course Details

Topic 1 AI Vibe Coding for PyTorch Fundamentals

What Is AI Vibe Coding
Setting Up Cursor, GitHub Copilot and Claude for PyTorch
Prompting Patterns for Correct Deep Learning Code
Vibe Coding PyTorch Tensor Operations
Computation Graphs and Autograd with AI Assistance
Reviewing and Debugging AI-Generated PyTorch Code

Topic 2 Vibe Coding Neural Networks

Neural Network Architectures, Activation and Loss Functions
Vibe Coding a Regression Model in PyTorch
Vibe Coding a Classification Model with Softmax and Cross Entropy
Generating Training Loops, Optimizers and Metrics from Prompts
Saving, Loading and Iterating on Models

Topic 3 Vibe Coding Convolutional Neural Networks

Overview of CNNs: Convolution, Pooling and Padding
Vibe Coding a CNN Image Classifier
Diagnosing Overfitting with AI Assistance
Data Augmentation and Regularization via Prompts
Transfer Learning with Pre-Trained Models

Topic 4 Vibe Coding Recurrent Networks for Sequence Data

Overview of RNNs, LSTM and GRU
Vibe Coding an LSTM for Time Series Forecasting
Tuning Sequence Models with Follow-Up Prompts
Evaluating and Visualizing Model Performance
Packaging a Complete Deep Learning Project

Course Info

Promotion Code

Your will get 10% discount voucher for 2nd course onwards if you write us a Google review.

Minimum Entry Requirement

Knowledge and Skills

Able to operate using computer functions
Minimum 3 GCE ‘O’ Levels Passes including English or WPL Level 5 (Average of Reading, Listening, Speaking & Writing Scores)

Attitude

Positive Learning Attitude
Enthusiastic Learner

Experience

Minimum of 1 year of working experience.

Target Age Group: 21-65 years old

Minimum Software/Hardware Requirement

Software:

You can download and install the following software:

Hardware: Windows and Mac Laptops

Job Roles

Machine Learning Engineer
Data Scientist
Deep Learning Researcher
AI Developer
Neural Network Designer
Computer Vision Engineer
NLP Engineer (branching into deep learning)
AI Product Manager (technical understanding)
Robotics Engineer (with AI components)
Bioinformatics Scientist (deep learning applications)
Medical Imaging Specialist (AI-focused)
Game Developer (AI-driven features)
Predictive Analytics Specialist
AI/ML Educator or Trainer
Autonomous Systems Developer.

Trainers

CY Quah is an ACLP-certified trainer and data science professional with extensive experience in Python, NLP, and machine learning. He has led AI training programs for SAP, Temasek Polytechnic, and IMDA under the SGUnited Mid-Career Pathways initiative, and has delivered corporate workshops on text analytics, recommender systems, and chatbot development. His expertise includes applying NLP tools such as NLTK, spaCy, and Gensim for sentiment analysis, topic modeling, and text classification.

Solomon Soh is an experienced Data Scientist and AI Trainer with a strong record of teaching and mentoring in Python programming, data analytics, and machine learning. Currently a Data Science Trainer with IBM Singapore, he has coached teams on projects involving natural language processing, computer vision, and chatbots, achieving a 96% learner satisfaction rating for his communication and technical expertise. His career spans roles at Workforce Optimizer, Certis Cisco, Ernst & Young, and IQVIA, where he applied Python-driven analytics to improve operations, optimize staffing, and deliver actionable insights. His academic background includes a double degree in Economics and Psychology from Singapore Management University (Summa Cum Laude, triple major in Analytics), an MBA, and a Master’s in Financial Engineering.

Marcel Leng is an ICT professional and educator with strong expertise in machine learning, cryptography, and applied mathematics. He has conducted extensive research and training in AI, data science, and quantum algorithms, and has guided learners in applying advanced computational tools to real-world security challenges.