
Deep Learning with PyTorch
Venue
Entrance Fee
Category
Event Type
Share
Schedule
Date | Time |
---|---|
15/03/2025 | 9:30 AM - 5:30 PM |
16/03/2025 | 9:30 AM - 5:30 PM |
24/03/2025 | 9:30 AM - 5:30 PM |
25/03/2025 | 9:30 AM - 5:30 PM |

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
HRD Corp Claimable Course for Employers Registered with HRD Corp
Course Code: M551
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