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Deep Learning for Image Classification Using Keras

Deep Learning for Image Classification Using Keras

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Dive into the dynamic world of deep learning and discover the power of Keras for image classification. This course unveils the essential techniques and tools to unlock your potential in AI-driven computer vision projects. From the foundational concepts of neural networks to practical implementations, we cover it all.

Whether you're an aspiring data scientist, a machine learning enthusiast, or a professional looking to upscale, this course is your gateway to mastering image classification using Keras. Through hands-on examples, industry-grade projects, and expert guidance, you'll be equipped to tackle real-world challenges with confidence. Join us on this transformative journey.

Certificate

All participants will receive a Certificate of Completion from Tertiary Courses after achieved at least 75% attendance.

Funding and Grant Applications

Click the links below to apply. Note that you need to register the course first.

SkillsFuture Credit

For individuals, please submit your SkillsFuture Credit

SSG TG and AP Application

For companies, please fill in the required details for grant application. SSG Training Grant Application Form

For WSQ funding, please checkout the details at NICF – Basic Deep Learning with Tensorflow Keras

Please do not pay up front. We will advise you on the eligibility and nett fee after registration

Course Code: TGS-2019502828

Course Booking

The course fee listed below is before subsidy/grant, if applicable. We will apply for the grant and send you the invoice with nett fee.

$498.00 (GST-exclusive)

$542.82 (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: No. of Participants

Sponsorship

Self-Sponsored
Employer-Sponsored

Name(s) on Certificate *

Additional Message or Emails for Course Notification

* Required Fields

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 and get back to you asap.

Course Cancellation/Reschedule Policy

We reserve the right to cancel or re-schedule the course due to unforeseen circumstances. If the course is cancelled, we will refund 100% to participants.
Note the venue of the training is subject to changes due to class size and availability of the classroom.
Note the minimal class size to start a class is 3 Pax.

Course Details

Day 1

Topic 1 Keras Basics

What is Keras
Keras vs TensorFlow
Google Colab
Install and Run Keras on Google Colab

Topic 2 Image Classification Model with Feedforward Neural Network (NN)

What is Feedforward NN
One Hot Encoding
Cross Entropy and SoftMax
MNIST Dataset
NN Image Classification NN Model for HandWritten Digits

Topic 3 Image Classification with Convolutional Neural Network (CNN)

What is CNN?
CNN Architecture
CNN Image Classification for HandWritten Digits
Image Class Generator and Fit Generator
CNN Image Classification for Cats and Dogs Images
Solving Overfitting with Dropout & Data Augmentation
Mini Project on Image Classification

Day 2

Topic 4 Image Classification with Transfer Learning

What is Transfer Learning
Image Classification with Pre-Trained Models
Fine Tune Pre-Trained Models
Mini Project on Transfer Learning

Topic 5 Keras Functional CNN Model

What is Functional API
Split CNN Model for Image Classification
Mini Project on Functional CNN Model

Topic 6 Object Detection with Mask R-CNN

Overview of R-CNN Models
Mask R-CNN Demo

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

SSG Training Grant

SSG TG is $15 per pax. Net fee after SSG TG is $309.82. Absentee Payroll is not eligible.

Steps to Apply Skills Future Claim

The staff will send you an invoice with the fee breakdown.
Login to the MySkillsFuture portal, select the course you’re enrolling on and enter the course date and schedule.
Enter the course fee payable by you (including GST) and enter the amount of credit to claim.
Upload your invoice and click ‘Submit’

Get Additional Course Fee Support Up to $500 under UTAP

The Union Training Assistance Programme (UTAP) is a training benefit provided to NTUC Union Members with an objective of encouraging them to upgrade with skills training. It is provided to minimize the training cost. If you are a NTUC Union Member then you can get 50% funding (capped at $500 per year) under Union Training Assistance Programme (UTAP).

For more information visit NTUC U Portal – Union Training Assistance Program (UTAP)

Steps to Apply UTAP

Note

SSG subsidy is available for Singapore Citizens, Permanent Residents, and Corporates.
All Singaporeans aged 25 and above can use their SkillsFuture Credit to pay. For more details, visit www.skillsfuture.gov.sg/credit
An unfunded course fee can be claimed via SkillsFuture Credit or paid in cash.
UTAP funding for NTUC Union Members is capped at $250 for 39 years and below and at $500 for 40 years and above.
UTAP support amount will be paid to training provider first and claimed after end of class by learner.

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

Richard Wan: Richard Wan is a ACTA certified trainer. Richard Wan has more than 30 years of experience in software development in various computer disciplines, including computer vision, communication and digital publishing.

Technical expertise includes: Windows, Linux developments with C, C++, Delphi (Object Pascal), Visual Studio, OpenCV. Embedded system programming including microcontrollers, Arduino, Pi, BeagleBone etc.

Ken Yuen: Ken Yuen is a ACTA certified trainer. He has more than 10 years of experience working as an instructor, Application Development Engineer, Technical Consultant and Project Manager. He is an MOE-Registered Instructor teaching STEM programs for past 3 years such as Arduino, Micro:bits and robotics to schools and libraries based on the smart nation initiative roadmap.

He completed his Diploma in Electronic Engineering at Singapore Polytechnic and graduated with Bachelor of Electrical and Electronics Engineering from Nanyang Technological University and certified PMP (Project Management Professional).

Quah Chee Yong: Quah Chee Yong is a ACTA trainer. Chee Yong is an experienced professional who has held various Technical, Operations and Commercial positions across several industries A firm believer that AI can create a better world, he has equipped himself with the Knowledge and Skills in the fields of Data Science, Machine Learning, Deep Learning and Cloud Deployment He has a deep passion for training & facilitating and is currently a Singapore WSQ certified Adult Educator. He particularly enjoys the interactive engagements with his fellow trainers and learners

Solomon Soh Zhe Hong: Solomon is ACTA certified and has trained and coached over 100 professionals in the area of data science, python programming and coding. Solomon is a Certified AI Engineer Associate by AI Singapore and holds certifications in Alibaba Cloud Architect and Alteryx respectively. Solomon interests include Reinforcement Learning, Natural Language Processing and Time-Series analysis.