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Deep Learning with PyTorch

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

Deep Learning with PyTorch

Course Code: C539

10 Review(s) | Add Your Review

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.

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)

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 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

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.

Review

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 2/28/2023)

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 2/28/2023)

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)