Deep Learning with PyTorch

Course Information

  • 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.

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Certification

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

Additional Information

Duration

2 months (Full Time)

Assessment

3 hours online assessment after each module

Class (No of teacher : student): 1:20

Intake

  • 3 Nov 2025 to 29 Sep 2026
  • 4 May 2026 to 26 June 2026
  • 2 Jan 2026 to 2 Mar 2026
  • 2 Mar 2026 to 27 Apr 2026

Enrolment Requirement

  • Age: 21 years old and above
  • Language Proficiency: At least C6 for GCE "O" Level English
  • Academic: At least C6 for GCE "O" Level in any 3 subjects

Graduation Requirement

  • Attendance: 75%
  • Assessment: Passed

Deep Learning with PyTorch

Course Code: C539

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What's This Course About

Embark on an enlightening journey into the realm of deep learning with PyTorch through Tertiary Courses. Our meticulously crafted curriculum begins with the foundational step of installing PyTorch, followed by elucidating math operations crucial for complex computations. As we traverse deeper, participants will gain hands-on experience in designing and implementing neural networks, the backbone of any deep learning algorithm.

The course transcends the basics as it immerses students in advanced modules like image recognition through Convolutional Neural Networks (CNNs) and processing sequential data using Recurrent Neural Networks (RNNs). With a blend of theoretical knowledge and practical sessions, this course promises to equip you with the competencies to harness the full potential of PyTorch in deep learning endeavors.

WSQ Funding

Full Fee $600.00 Before GST
GST $54.00 9% of fee
Baseline Nett $354.00 SG/PR age 21+ · 50% funded
MCES / SME Nett $234.00 SG age 40+ · 70% funded
Funding and Grant Applications

No funding is available for this course.

For WSQ funding, please checkout the details at WSQ - Predictive Analytics with PyTorch: Transform Your Data to Prediction

Course Fee

$600.00 (GST-exclusive)
$654.00 (GST-inclusive)

Course Date

Course Time

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Additional Note

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

Post-Course Support

  • 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.

Cancellation & Reschedule Policy

  • 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

Course Details

What You'll Learn

Topic 1 Overview of Deep Learning and Pytorch

Overview of Deep Learning

Introduction to Pytorch

Install and Run Pytorch

Basic Pytorch Tensor Operations

Computation Graphs

Compute Gradients with Autograd

Topic 2 Neural Network for Regression

Introduction to Neural Network (NN)

Activation Function

Loss Function and Optimizer

Machine Learning Methodology

Build a NN Predictive Regression Model

Load and Save Model

Topic 3 Neural Network for Classification

Softmax

Cross Entropy Loss Function

Build a NN Classification Model

Topic 4 Convolutional Neural Network (CNN)

Overview of CNN

Convolution, Max Pooling and Padding

Build a CNN Model for Image Classificaiton

Overfitting Issue with Small Dataset

Techniques to overcome Overfitting Issue

Topic 5 Transfer Learning

Introduction to Transfer Learning

Pre-trained Models

Feature Extraction & Fine Tuning for Small Dataset

Topic 6 Recurrent Neural Network (RNN)

Overview of RNN

Long Term Dependencies

LSTM and GRU

Apply LSTM to Time Series Forecasting

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

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

Trainers

Richard Wan

Richard Wan is an ACLP-certified lecturer and software consultant with over 40 years of experience in software and hardware development, spanning AI, computer vision, and machine learning. He began his programming career with 8-bit computing in the late 1970s and went on to earn his M.Sc. in Electrical Engineering (Computer Vision) from the University of Wisconsin–Madison. His professional contributions include co-founding multiple high-tech companies, pioneering digital publishing technologies, and leading AI-driven software development in healthcare, defense, and manufacturing.

Richard has taught a wide range of technical courses, including machine learning with Scikit-Learn, deep learning with TensorFlow and PyTorch, and computer vision with OpenCV. In predictive analytics, he emphasizes the use of PyTorch for building deep learning models that can forecast trends, detect anomalies, and classify outcomes. His teaching approach blends decades of hands-on development with structured, beginner-friendly instruction, equipping learners with practical skills to transform data into prediction.

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