Lesson proof
Concept, demo, checklist, lab, and assignment evidence.
Deep Learning / AI / Deep Learning Engineer
Deep Learning Engineer
Learn neural networks, computer vision, NLP, transformers, embeddings, training loops, GPUs, evaluation, deployment, monitoring, safety, and responsible AI operations.
Deep learning foundationsModel training evidenceAI deployment readiness
Start domain mock testPlatform-wide module outputs
Every module now feeds portfolio proof and CV readiness.
Portfolio pack
Requirement, artifact, validation, risk note, and interview story.
CV signal
Role-specific skill statement linked to a score or artifact.
Review queue
Submitted evidence can support dashboard, readiness, and career exports.
Open materials
Deep learning foundationsLesson + portfolio pack + CV evidenceNeural networks and backpropagationLesson + portfolio pack + CV evidenceComputer vision with CNNsLesson + portfolio pack + CV evidenceNatural language processingLesson + portfolio pack + CV evidenceTransformers embeddings and attentionLesson + portfolio pack + CV evidenceTraining optimization and GPUsLesson + portfolio pack + CV evidenceModel evaluation and safetyLesson + portfolio pack + CV evidenceDeployment monitoring and MLOpsLesson + portfolio pack + CV evidence
Certification objective coverage
Every provider-aligned module is connected to a lesson, labs, mock questions, and implementation proof.
This is the track-level audit view for blueprint alignment. Exact exam wording should still be checked against the current official provider guide before public exam-code claims are made.
deep-learning-engineer.deep-learning-foundations.01 / 13% weight
Apply Deep learning foundations decisions to Deep Learning Engineer scenarios
Implementation proof
- Define Deep learning foundations in plain language and explain the provider service family it belongs to.
- Show how Deep learning foundations is implemented through a guided configuration, simulator, command, diagram, notebook, or case study.
- Capture evidence with screenshots, command output, logs, metrics, topology state, policy review, query result, or troubleshooting notes.
- Connect the evidence to a portfolio pack, CV-ready skill statement, and mock-test weak-domain recovery action.
Evidence requirements
- Correct scenario decision in mock exam
- Written explanation of the key requirement or constraint
- Hands-on lab evidence or troubleshooting proof
- Portfolio pack with requirement, artifact, validation, risk note, and interview story
- CV-ready skill statement linked to a score, artifact, or project result
deep-learning-engineer.neural-networks-and-backpropagation.02 / 13% weight
Apply Neural networks and backpropagation decisions to Deep Learning Engineer scenarios
Implementation proof
- Define Neural networks and backpropagation in plain language and explain the provider service family it belongs to.
- Show how Neural networks and backpropagation is implemented through a guided configuration, simulator, command, diagram, notebook, or case study.
- Capture evidence with screenshots, command output, logs, metrics, topology state, policy review, query result, or troubleshooting notes.
- Connect the evidence to a portfolio pack, CV-ready skill statement, and mock-test weak-domain recovery action.
Evidence requirements
- Correct scenario decision in mock exam
- Written explanation of the key requirement or constraint
- Hands-on lab evidence or troubleshooting proof
- Portfolio pack with requirement, artifact, validation, risk note, and interview story
- CV-ready skill statement linked to a score, artifact, or project result
deep-learning-engineer.computer-vision-with-cnns.03 / 13% weight
Apply Computer vision with CNNs decisions to Deep Learning Engineer scenarios
Implementation proof
- Define Computer vision with CNNs in plain language and explain the provider service family it belongs to.
- Show how Computer vision with CNNs is implemented through a guided configuration, simulator, command, diagram, notebook, or case study.
- Capture evidence with screenshots, command output, logs, metrics, topology state, policy review, query result, or troubleshooting notes.
- Connect the evidence to a portfolio pack, CV-ready skill statement, and mock-test weak-domain recovery action.
Evidence requirements
- Correct scenario decision in mock exam
- Written explanation of the key requirement or constraint
- Hands-on lab evidence or troubleshooting proof
- Portfolio pack with requirement, artifact, validation, risk note, and interview story
- CV-ready skill statement linked to a score, artifact, or project result
deep-learning-engineer.natural-language-processing.04 / 13% weight
Apply Natural language processing decisions to Deep Learning Engineer scenarios
Implementation proof
- Define Natural language processing in plain language and explain the provider service family it belongs to.
- Show how Natural language processing is implemented through a guided configuration, simulator, command, diagram, notebook, or case study.
- Capture evidence with screenshots, command output, logs, metrics, topology state, policy review, query result, or troubleshooting notes.
- Connect the evidence to a portfolio pack, CV-ready skill statement, and mock-test weak-domain recovery action.
Evidence requirements
- Correct scenario decision in mock exam
- Written explanation of the key requirement or constraint
- Hands-on lab evidence or troubleshooting proof
- Portfolio pack with requirement, artifact, validation, risk note, and interview story
- CV-ready skill statement linked to a score, artifact, or project result
deep-learning-engineer.transformers-embeddings-and-attention.05 / 13% weight
Apply Transformers embeddings and attention decisions to Deep Learning Engineer scenarios
Implementation proof
- Define Transformers embeddings and attention in plain language and explain the provider service family it belongs to.
- Show how Transformers embeddings and attention is implemented through a guided configuration, simulator, command, diagram, notebook, or case study.
- Capture evidence with screenshots, command output, logs, metrics, topology state, policy review, query result, or troubleshooting notes.
- Connect the evidence to a portfolio pack, CV-ready skill statement, and mock-test weak-domain recovery action.
Evidence requirements
- Correct scenario decision in mock exam
- Written explanation of the key requirement or constraint
- Hands-on lab evidence or troubleshooting proof
- Portfolio pack with requirement, artifact, validation, risk note, and interview story
- CV-ready skill statement linked to a score, artifact, or project result
deep-learning-engineer.training-optimization-and-gpus.06 / 13% weight
Apply Training optimization and GPUs decisions to Deep Learning Engineer scenarios
Implementation proof
- Define Training optimization and GPUs in plain language and explain the provider service family it belongs to.
- Show how Training optimization and GPUs is implemented through a guided configuration, simulator, command, diagram, notebook, or case study.
- Capture evidence with screenshots, command output, logs, metrics, topology state, policy review, query result, or troubleshooting notes.
- Connect the evidence to a portfolio pack, CV-ready skill statement, and mock-test weak-domain recovery action.
Evidence requirements
- Correct scenario decision in mock exam
- Written explanation of the key requirement or constraint
- Hands-on lab evidence or troubleshooting proof
- Portfolio pack with requirement, artifact, validation, risk note, and interview story
- CV-ready skill statement linked to a score, artifact, or project result
deep-learning-engineer.model-evaluation-and-safety.07 / 13% weight
Apply Model evaluation and safety decisions to Deep Learning Engineer scenarios
Implementation proof
- Define Model evaluation and safety in plain language and explain the provider service family it belongs to.
- Show how Model evaluation and safety is implemented through a guided configuration, simulator, command, diagram, notebook, or case study.
- Capture evidence with screenshots, command output, logs, metrics, topology state, policy review, query result, or troubleshooting notes.
- Connect the evidence to a portfolio pack, CV-ready skill statement, and mock-test weak-domain recovery action.
Evidence requirements
- Correct scenario decision in mock exam
- Written explanation of the key requirement or constraint
- Hands-on lab evidence or troubleshooting proof
- Portfolio pack with requirement, artifact, validation, risk note, and interview story
- CV-ready skill statement linked to a score, artifact, or project result
deep-learning-engineer.deployment-monitoring-and-mlops.08 / 9% weight
Apply Deployment monitoring and MLOps decisions to Deep Learning Engineer scenarios
Implementation proof
- Define Deployment monitoring and MLOps in plain language and explain the provider service family it belongs to.
- Show how Deployment monitoring and MLOps is implemented through a guided configuration, simulator, command, diagram, notebook, or case study.
- Capture evidence with screenshots, command output, logs, metrics, topology state, policy review, query result, or troubleshooting notes.
- Connect the evidence to a portfolio pack, CV-ready skill statement, and mock-test weak-domain recovery action.
Evidence requirements
- Correct scenario decision in mock exam
- Written explanation of the key requirement or constraint
- Hands-on lab evidence or troubleshooting proof
- Portfolio pack with requirement, artifact, validation, risk note, and interview story
- CV-ready skill statement linked to a score, artifact, or project result
Test readiness
Mock test by domain
Practice every domain in this track with exam-style questions, answer keys, and explanations.
Open mock testMost in-demand certification materials
High-value certificates connected to this track.
TensorFlow / Google
TensorFlow Developer Certificate readiness
Needs reviewLast verified: Not verifiedNext review: Provider source review required
AI engineers building neural networks, computer vision, NLP, and model training workflows.
TensorFlow Developer Certificate readiness is mapped to platform lessons and labs, but still needs a dated official-source review.
- Tensor and training-loop concept map
- CNN, NLP, and model evaluation notebook checklist
- Overfitting, regularization, and metrics drill pack
Google Cloud
Google Professional Machine Learning Engineer
Needs reviewLast verified: Not verifiedNext review: Provider source review required
ML and deep learning engineers deploying, evaluating, monitoring, and governing AI systems.
Google Professional Machine Learning Engineer is mapped to platform lessons and labs, but still needs a dated official-source review.
- Production ML and deep learning deployment checklist
- Model monitoring, drift, and rollback scenario notes
- Responsible AI evaluation and safety review workbook
Certification provider connections
Connect this learning path to the official exam provider.
Certification provider
TensorFlow Developer Certificate readiness
Confirm the official provider, exam code, delivery rules, ID policy, and reschedule window before booking.
Booking partner: Provider exam partner
- Create or confirm the Provider learner account.
- Review the official exam guide, ID policy, delivery options, and reschedule rules.
- Add target exam date, booking status, renewal date, and certificate proof to the learner record.
Google Cloud Certification
Google Professional Machine Learning Engineer
Review the certification path, confirm the exam language and delivery option, then attach the target date to the learner study plan.
Booking partner: Google Cloud exam registration partner
- Create or confirm the Google Cloud certification profile.
- Review the official exam guide, ID policy, delivery options, and reschedule rules.
- Add target exam date, booking status, renewal date, and certificate proof to the learner record.
01 Match
Map each Daskerel track to the official provider, exam code, registration page, and verification route.
02 Prepare
Use provider objectives with Daskerel lessons, mock exams, labs, and evidence packs before booking.
03 Book
Send learners to the official scheduling partner while keeping target dates and next actions in the dashboard.
04 Verify
Capture certificate URL, badge, expiry, renewal plan, and portfolio proof after the learner passes.
Study plan
Start with tensors, activation functions, loss functions, gradient descent, backpropagation, overfitting, regularization, and train-validation-test splits.
Practise small image, text, and tabular neural network labs before scaling into transformers or GPU-heavy workflows.
Treat evaluation, bias checks, monitoring, cost, safety, and rollback as first-class engineering work, not afterthoughts.
Hands-on labs
Train a small neural network in a notebook and compare training and validation metrics.
Fine-tune or run inference with a pretrained vision or text model and document limitations.
Create an evaluation card covering accuracy, precision/recall, confusion matrix, bias risks, and monitoring signals.
Design a deployment plan with model versioning, rollback, cost, and safety controls.
Track learning assets
Templates and revision tools for this path.
Exam blueprint checklistDeep Learning Engineer
Weekly study plannerDeep Learning Engineer
Command and service cheat sheetDeep Learning Engineer
Architecture pattern cardsDeep Learning Engineer
Flashcard revision setDeep Learning Engineer
Mock exam review sheetDeep Learning Engineer
Lab evidence templateDeep Learning Engineer
Interview story builderDeep Learning Engineer
Portfolio project rubricDeep Learning Engineer
Final readiness checklistDeep Learning Engineer
Practice questions
Why is validation loss important during deep learning training?
Validation loss shows how the model performs on data it did not train on, helping detect overfitting and poor generalization.
Why should deep learning deployments include monitoring?
Inputs, data distributions, latency, cost, and model quality can drift after release, so monitoring is needed to detect degradation and trigger review or rollback.