AI & Machine Learning
Deep Learning & Neural Networks
Build neural networks for computer vision and NLP with TensorFlow and PyTorch.
Overview
Understand and build deep learning models. You learn how neural networks work, train them with TensorFlow and PyTorch, and apply them to computer vision and natural language processing projects.
Course objectives
- Understand how neural networks learn
- Build models in TensorFlow and PyTorch
- Solve computer vision problems
- Fine-tune transformer models for NLP
Who it's for
- ML practitioners ready for deep learning
- Developers building AI features
- Engineers interested in vision and language models
Prerequisites
- Solid Python skills
- Machine learning basics
- Comfort with basic linear algebra
Syllabus outline
- Neurons, layers and activations
- Backpropagation and gradient descent
- Regularisation and dropout
Lab and practice approach
You train real models on GPU-backed notebooks every week and finish with a computer vision or NLP capstone project.
Why attend this course?
- Deep learning powers modern vision and language AI
- Covers both major frameworks
- Project-based, with GPU labs
After completing this course
Roles you can aim for
- Deep Learning Engineer
- Computer Vision Engineer
- NLP Engineer
What you will be able to do
- Neural network design
- Transfer learning and fine-tuning
- Vision and NLP pipelines
Assessments and capstone project
No vendor exam is required. You are assessed through assignments, quizzes and knowledge checks, technical assessments and a final capstone project.
- Assessment
- Assignments, quizzes and technical assessments
- Final project
- Capstone project reviewed by your instructor
- Vendor exam
- Not required
Why learn with OpenSprig
- Live, instructor-led online sessions you can ask questions in
- Practice on real systems in every class
- Exam-objective-aligned syllabus with mock tests
- Course community group for help between classes
- Weekend-friendly 2–3 hour classes
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