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Deep Learning with PyTorch

Deep Learning with PyTorch

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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.

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

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

For WSQ funding, please checkout the details at WSQ – Predictive Modeling with Pytorch

Course Code: TGS-2020501498

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

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

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

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

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

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.

Marcel Leng: Marcel Leng is a ACTA certified. Marcel graduated with majors in Applied Mathematics and Physics from the National University of Singapore.

His core specialisation skills are R, Python, Machine Learning, Statistical Analysis, and Data Visualisation in Tableau. His current interests include Machine Learning, Deep Learning, Artificial Intelligence, Internet of Things, Robotics and Programming.

Customer Reviews (10)

will recommend Review by Course Participant/Trainee

1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment

The trainer is patient and good (Posted on 11/26/2023)

will recommend Review by Course Participant/Trainee

1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment

. (Posted on 3/1/2023)

will recommend Review by Course Participant/Trainee

1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment

so far so good (Posted on 3/1/2023)

might recommend Review by Course Participant/Trainee

1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment

The training material is not very complete in terms of the codes given in the slides. (Posted on 5/22/2022)

The course is good. The instructors are knowledgeable. Review by Course Participant/Trainee

1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment

The course is good. The instructors are knowledgeable. Would be better if the course classrooms are in a nicer location. The current classrooms / building where the classes are held are very run-down. (Posted on 2/4/2020)