Data and AI

Deep Learning with PyTorch

Build and train genuine neural networks in PyTorch, the dominant framework in modern deep learning practice and research

24h4 lessonsTier 2 hireabilityTier 2 certificate

What you'll be able to do

You can build, train, and debug neural networks in PyTorch, understand the genuine mechanics of backpropagation and gradient descent well enough to diagnose training problems, and recognise when a deep learning approach is actually warranted over a simpler classical model.

Job titles this qualifies you for

Machine Learning EngineerAI EngineerLLM Engineer
Market intelligence
Demand
High
Remote
70% remote roles
Nigeria salary
Not a primary local hiring category
Remote (USD)
$101,500–$155,000/year for the typical remote range (25th-75th percentile); averages reported between $128,769 and $195,475/year depending on source; specialised skills (AI agent architecture, LLM fine-tuning) can increase pay up to 25%

Lessons

Stage 1 — Foundation
1
Tensors and the PyTorch Computation Model
40 min
2
Training Loops, Loss Functions, and Optimisers
45 min
Stage 2 — Applied
1
Common Neural Network Architectures
40 min
2
Debugging Training and Knowing When Deep Learning Is Warranted
30 min
Tier 2 Proof Submission

Deep Learning with PyTorch Portfolio

Demonstrate genuine tensor/autograd understanding, training loop mechanics, architecture judgement, and debugging/tool-choice maturity.

1.An autograd and GPU-acceleration explanation
2.A training loop step-by-step description with a zero_grad() failure explanation
3.A CNN parameter-efficiency and Transformer-displacement explanation
4.A loss curve diagnosis and a classical-model-preferred scenario
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