Introduction to Deep Learning with PyTorch
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Next Start
September 28, 2026
(Offered monthly)Duration
5 Days
ClassroomProgram Fee
$1,500
Exclusive 16% VATOther Dates
October 26, 2026
November 23, 2026
November 23, 2026
Course Description
This comprehensive course provides a practical introduction to deep learning using PyTorch, one of the most popular and powerful deep learning frameworks. Designed for learners who already have foundational machine learning knowledge, the course focuses on building, training, and deploying neural networks for real-world applications. Participants will develop the skills to tackle complex problems in computer vision, natural language processing, and more using modern deep learning architectures.
The curriculum covers tensor operations, neural network fundamentals, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), transfer learning, and modern architectures. Using PyTorch and its ecosystem, learners will work through hands-on exercises and projects that simulate real-world deep learning workflows. By the end of the course, participants will be able to build and train deep neural networks, leverage pre-trained models, and apply deep learning to solve complex problems in vision, text, and sequential data.
Prerequisite note: This course requires foundational machine learning knowledge and Python programming experience. Participants should be comfortable with linear algebra, calculus, and supervised learning concepts. Basic neural network familiarity is helpful but not required.
Course Outline
This course is structured into 10 core modules, each carefully designed to build progressively on the previous one.
Learning Outcomes
Technical Skills
- Build and train neural networks using PyTorch.
- Implement Convolutional Neural Networks (CNNs) for computer vision.
- Implement Recurrent Neural Networks (RNNs) for sequential data.
- Apply transfer learning with pre-trained models.
- Use GPU acceleration for deep learning.
- Handle data loading and augmentation with PyTorch DataLoader.
- Implement modern architectures (ResNet, Transformers).
- Visualize model training and performance.
- Perform hyperparameter tuning for deep learning.
- Save, load, and deploy PyTorch models.
Career Benefits
- Build in-demand deep learning skills used across multiple industries.
- Develop the ability to solve complex problems in vision, language, and more.
- Gain hands-on experience with PyTorch and the deep learning ecosystem.
- Prepare for advanced topics such as generative AI and LLMs.
- Enhance your portfolio with real-world deep learning projects.
- Improve employability for AI, ML, and deep learning roles.
- Develop skills applicable to research, industry, and academia.
Who Should Attend
- Aspiring AI Engineers — Individuals building deep learning skills.
- Data Scientists — Professionals advancing to deep learning.
- Machine Learning Engineers — Individuals working with neural networks.
- Researchers — Academics applying deep learning to research problems.
- Software Engineers — Developers adding AI capabilities to applications.
- Students & Career Changers — Learners preparing for AI-focused careers.
- Computer Vision Enthusiasts — Anyone interested in vision applications.
- NLP Enthusiasts — Anyone interested in text and language applications.
Prerequisites & Requirements
Prerequisites
- Applied Machine Learning with Python or equivalent knowledge.
- Understanding of machine learning fundamentals (supervised learning, model evaluation).
- Fundamental Python programming knowledge.
- Linear algebra basics (vectors, matrices, matrix multiplication).
- Calculus basics (derivatives, gradients).
- Comfortable working with mathematical concepts.
- Basic familiarity with neural network concepts is helpful but not required.
Requirements
- Laptop with at least 2GB RAM (GB recommended for smoother performance).
- All learning facilities are provided on-site, including high-speed internet, projectors, writing materials, and refreshments.
- Administrative rights on your laptop to install R, RStudio, and necessary packages.
- Sufficient free disk space (at least 2GB) for software installation and project files.
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