Data Science & AI
Deep Learning Specialization
Master neural networks from backpropagation to transformers — with the math and the code.
- Duration
- 16 weeks
- Format
- Online Live
- Level
- Advanced
- Credential
- Professional Certificate
Program overview
A deep, implementation-first study of modern neural networks. You will build architectures from scratch in PyTorch, understand why they work, and apply them to vision, language, and multimodal problems. Designed for practitioners who want genuine depth, not API-level familiarity.
What you'll be able to do
- Implement core architectures (CNNs, RNNs, transformers) from first principles
- Train deep models efficiently with modern optimization and regularization
- Fine-tune pretrained vision and language models for custom tasks
- Debug training failures systematically
- Read and reproduce results from current research papers
Tools & technologies
- PyTorch
- Hugging Face
- Weights & Biases
- CUDA basics
- torchvision
- Python
Curriculum
5 modules · 16 weeks · every module ends with reviewed applied work.
Neural Network Foundations
- Backpropagation from scratch
- Optimization: SGD to AdamW
- Regularization and normalization
- Training dynamics and debugging
Computer Vision
- Convolutional architectures
- ResNets and modern CNN design
- Transfer learning for vision
- Detection and segmentation overview
Sequence Models & Transformers
- RNNs and attention
- The transformer architecture in depth
- Pretraining and fine-tuning
- Tokenization and embeddings
Applied Deep Learning
- Fine-tuning LLMs and vision transformers
- Multimodal models overview
- Efficient training: mixed precision, LoRA
- Reading research papers effectively
Research-Grade Capstone
- Reproduce or extend a published result
- Experiment design and ablations
- Technical writing and presentation
Projects & capstone
You graduate with evidence, not just knowledge. Key projects include:
Image classifier built from scratch, then improved with transfer learning
Mini-GPT: implement and train a small transformer language model
Capstone: reproduce and extend a recent deep learning paper
Who this program is for
- ML practitioners seeking architectural depth
- Researchers and graduate students
- Engineers preparing for specialized AI roles
Eligibility
Strong Python skills, comfort with linear algebra and calculus, and prior machine learning experience. An entry assessment ensures the cohort maintains an advanced pace.
Your instructors
Dr. Mei-Lin Chang
Faculty, Deep Learning & AI Research
A former research scientist in computer vision and NLP, Mei-Lin has published in leading AI venues and now translates frontier research into teachable, buildable curriculum. Her paper-reading seminars are a student favorite.
Career opportunities
Roles this program prepares you to pursue:
- Deep Learning Engineer
- AI Research Engineer
- Computer Vision Engineer
- NLP Engineer
Earn a verifiable certificate
Complete all required project work to earn the Pioneer Academy Deep Learning Specialization certificate — digitally issued with a unique verification link for your CV and LinkedIn.
Program FAQs
How mathematical is this program?
We use math where it creates understanding — derivations of backpropagation, attention, and optimization are covered carefully, always paired with working code.
Do I need a GPU?
No. All labs run on provided cloud GPU environments included in tuition.
Take the next step
Start your Data & AI journey
The next Deep Learning Specialization cohort is enrolling now. Applications take under ten minutes, and seats are confirmed in order of application.