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Advanced Keras Training

Course Information

  • Sessions 1 day
  • Duration 7.5 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

Advanced Keras Training

Course Code: C842

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What's This Course About

Delve into the advanced realm of Keras with Tertiary Courses and witness a transformative learning experience that promises to elevate your proficiency in neural networks. This intensive training is curated to address challenges posed by smaller datasets, guiding learners to harness potent techniques that optimize and refine models for stellar outcomes. Experience the power of the Functional API, a pivotal tool in Keras, tailored for constructing intricate, multi-output models with superior flexibility.

In addition to hands-on coding sessions, the program encapsulates the aesthetic realm of neural networks through feature map visualization. This segment offers insights into the internal workings of convolutional layers, aiding in model interpretation and fostering a deeper understanding of activations. By the culmination of this training, participants will not only be adept at crafting sophisticated models using Keras but will also possess the acumen to visualize and interpret their nuances, thereby bridging theory with tangible results.

WSQ Funding

Full Fee $350.00 Before GST
GST $31.50 9% of fee
Baseline Nett $206.50 SG/PR age 21+ · 50% funded
MCES / SME Nett $136.50 SG age 40+ · 70% funded
Funding and Grant Applications

No funding is available for this course

Course Fee

$350.00 (GST-exclusive)
$381.50 (GST-inclusive)

Course Date

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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 Image Recognition with CNN

Introduction to Convolutional Neural Network (CNN)

Convolution & Pooling

Build a CNN Model for Image Recognition

Topic 2 Overfitting for Small Datasets

Overfitting and Underfitting

Methods to Solve Overfitting

Small Dataset Overfitting Issue

Data Augmentation & Dropout

Topic 3 Functional Keras API

Overview of Functional API

Create Sequential Model with Functional API

Feature Map Visualization

Topic 4 Transfer Learning for Small Datasets

Introduction to Transfer Learning

Pre-trained Models in Keras

Transfer Learning on Small Dataset

Course Info

Prerequisite

This is an intermediate course. The following knowledge is assumed:

Software Requirement

Please install the following software prior to the class

Please follow this guide to install Tensorflow on Mac https://www.tensorflow.org/install/install_mac

Please follow this guide to install Tensorflow on Window https://www.tensorflow.org/install/install_windows

Job Roles

Job Roles

  • Data Scientists
  • Data Analysts
  • Engineers

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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