Deep Learning Specialization
From first principles to state-of-the-art architectures
About this program
A six-month deep dive into the mathematics and engineering of neural networks. You'll implement backpropagation by hand, build CNNs, RNNs, and transformers from scratch in PyTorch, and develop the intuition needed to read and reproduce modern research papers. Heavy lab component.
Student ratings
Highly rated — 203 verified Canadian graduates rated this program 4.6/5. Reviews emphasize the applied capstone, instructor responsiveness, and career outcomes.
- 5★131
- 4★66
- 3★4
- 2★1
- 1★1
Who this program is for
- →Practitioners already shipping ml engineering work who want depth
- →Senior engineers, data scientists, and technical leads
- →Canadian residents seeking a verifiable diploma credential
Topics you'll cover
6 modules across 6 months — 24 lessons in total.
Full syllabus
Module 1 · Month 1 — Math and First Networks▾
- L1Linear algebra refresher
- L2Calculus for backprop
- L3Implementing a network from scratch
- L4Autograd internals
Module 2 · Month 2 — Convolutional Networks▾
- L1Convolution, pooling, padding
- L2ResNet and skip connections
- L3Object detection
- L4Lab: training on CIFAR and ImageNet subsets
Module 3 · Month 3 — Sequence Models▾
- L1RNNs, LSTMs, GRUs
- L2Sequence-to-sequence
- L3Attention mechanism
- L4Lab: machine translation
Module 4 · Month 4 — Transformers▾
- L1Self-attention from scratch
- L2Positional encodings
- L3Encoder, decoder, and encoder-decoder
- L4Training a small GPT
Module 5 · Month 5 — Modern Tricks▾
- L1Mixed precision training
- L2Distributed data parallel
- L3Optimization tricks (AdamW, schedulers)
- L4Regularization and augmentation
Module 6 · Month 6 — Capstone▾
- L1Pick a recent paper
- L2Reproduce results
- L3Extend in a novel direction
- L4Write a workshop-style report
What you'll be able to do
- ●Implement backpropagation and modern optimizers from scratch
- ●Train CNN, RNN, and transformer architectures
- ●Reproduce results from a recent NeurIPS paper
- ●Debug training runs that aren't converging
- ●Read and critique ML research
Career paths after graduation
Frequently asked questions
How much does the Deep Learning Specialization cost?▾
Tuition is $175 CAD, paid once. You can pay in full at checkout or choose an interest-free monthly plan. A 30-day refund window applies from your enrollment date.
How long is the Deep Learning Specialization program?▾
Self-paced. Most students complete it in 6 months at roughly 10 hours per week, but you can go faster or slower — you keep lifetime access.
What are the prerequisites?▾
Linear algebra; Multivariable calculus; Strong Python and NumPy
Is the diploma recognized in Canada?▾
Yes. Graduates receive the Altaris AI Academy Diploma in ML Engineering — a verifiable credential with a unique certificate number you can publish on LinkedIn and that any employer can verify at altarisai.org/verify.
What is the refund policy?▾
Full refund within 30 days of enrollment, no questions asked. After day 30, prorated refunds are available per our Refund Policy.
Who teaches the program?▾
Working Canadian AI practitioners — not academics. Every module is built and reviewed by a lead instructor working in the field today.
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