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Deep Learning Fundamentals

Build, train, evaluate, diagnose, and document neural networks while understanding optimization, representation, reproducibility, and deployment constraints.

View curriculum
ProfessionalDifficulty
50 hoursEstimated time
8 modules32 lessons
5 guided labsKnowledge check
00
Plan your learning

Path Brief

Skills you will practice

  • Data Ai · Deep Learning Fundamentals
  • Data Ai · Neural Network Foundations
  • Data Ai · Tensors And Automatic Differentiation
  • Data Ai · Training And Optimization
  • Data Ai · Convolutional Models
  • Data Ai · Sequence And Attention Models

Prerequisites

  • Machine Learning Fundamentals

Tools

  • A current web browser
  • A code editor or terminal appropriate to the path
  • A safe local or virtual lab environment
01
Before you begin

What you need

  • Machine Learning Fundamentals
02
Step-by-step curriculum

Learning modules

Complete lessons in order or open the module that matches your current goal. Your lesson progress is saved on this device.

01Neural Network FoundationsBuild practical skill in neural network foundations and connect it to the outcomes of Deep Learning Fundamentals.4 lessons · 1 lab
Module checkpointNeural Network Foundations checkpoint
1Which statement is a core idea you need to understand for Neural Network Foundations in Deep Learning Fundamentals?
2Which setup step belongs directly to Neural Network Foundations in Deep Learning Fundamentals?
3Which exercise best practices the actual skill taught in Neural Network Foundations in Deep Learning Fundamentals?
02Tensors and Automatic DifferentiationBuild practical skill in tensors and automatic differentiation and connect it to the outcomes of Deep Learning Fundamentals.4 lessons · 1 lab
Module checkpointTensors and Automatic Differentiation checkpoint
1Which diagnostic action is relevant when Tensors and Automatic Differentiation in Deep Learning Fundamentals does not behave as expected?
2Which statement is a core idea you need to understand for Tensors and Automatic Differentiation in Deep Learning Fundamentals?
3Which setup step belongs directly to Tensors and Automatic Differentiation in Deep Learning Fundamentals?
03Training and OptimizationBuild practical skill in training and optimization and connect it to the outcomes of Deep Learning Fundamentals.4 lessons · 1 lab
Module checkpointTraining and Optimization checkpoint
1Which setup step belongs directly to Training and Optimization in Deep Learning Fundamentals?
2Which exercise best practices the actual skill taught in Training and Optimization in Deep Learning Fundamentals?
3Which diagnostic action is relevant when Training and Optimization in Deep Learning Fundamentals does not behave as expected?
04Convolutional ModelsBuild practical skill in convolutional models and connect it to the outcomes of Deep Learning Fundamentals.4 lessons · 1 lab
Module checkpointConvolutional Models checkpoint
1Which setup step belongs directly to Convolutional Models in Deep Learning Fundamentals?
2Which exercise best practices the actual skill taught in Convolutional Models in Deep Learning Fundamentals?
3Which diagnostic action is relevant when Convolutional Models in Deep Learning Fundamentals does not behave as expected?
05Sequence and Attention ModelsBuild practical skill in sequence and attention models and connect it to the outcomes of Deep Learning Fundamentals.4 lessons · 1 lab
Module checkpointSequence and Attention Models checkpoint
1Which exercise best practices the actual skill taught in Sequence and Attention Models in Deep Learning Fundamentals?
2Which diagnostic action is relevant when Sequence and Attention Models in Deep Learning Fundamentals does not behave as expected?
3Which statement is a core idea you need to understand for Sequence and Attention Models in Deep Learning Fundamentals?
06Regularization and EvaluationBuild practical skill in regularization and evaluation and connect it to the outcomes of Deep Learning Fundamentals.4 lessons
Module checkpointRegularization and Evaluation checkpoint
1Which setup step belongs directly to Regularization and Evaluation in Deep Learning Fundamentals?
2Which exercise best practices the actual skill taught in Regularization and Evaluation in Deep Learning Fundamentals?
3Which diagnostic action is relevant when Regularization and Evaluation in Deep Learning Fundamentals does not behave as expected?
07Experiment Tracking and ReproducibilityBuild practical skill in experiment tracking and reproducibility and connect it to the outcomes of Deep Learning Fundamentals.4 lessons
Module checkpointExperiment Tracking and Reproducibility checkpoint
1Which statement is a core idea you need to understand for Experiment Tracking and Reproducibility in Deep Learning Fundamentals?
2Which setup step belongs directly to Experiment Tracking and Reproducibility in Deep Learning Fundamentals?
3Which exercise best practices the actual skill taught in Experiment Tracking and Reproducibility in Deep Learning Fundamentals?
08Deployment and Performance ConstraintsBuild practical skill in deployment and performance constraints and connect it to the outcomes of Deep Learning Fundamentals.4 lessons
Module checkpointDeployment and Performance Constraints checkpoint
1Which exercise best practices the actual skill taught in Deployment and Performance Constraints in Deep Learning Fundamentals?
2Which diagnostic action is relevant when Deployment and Performance Constraints in Deep Learning Fundamentals does not behave as expected?
3Which statement is a core idea you need to understand for Deployment and Performance Constraints in Deep Learning Fundamentals?
03
Portfolio integration

Capstone Project

Train and Document a Deep Learning System

Combine the path skills in a portfolio-ready project with clear functional requirements, tests, verification evidence, and operating notes.

GuidedIndependentPortfolio

Acceptance criteria

  • The core workflow works from start to finish.
  • Invalid input or failure states are handled clearly.
  • The result is tested and documented.
  • A reviewer can reproduce the setup and verification.
03
Knowledge check

Test your understanding

Answer all questions. Results and explanations are calculated instantly in your browser.

1Path review for Deep Learning Fundamentals: Which statement is a core idea you need to understand for Neural Network Foundations in Deep Learning Fundamentals?
2Path review for Deep Learning Fundamentals: Which setup step belongs directly to Tensors and Automatic Differentiation in Deep Learning Fundamentals?
3Path review for Deep Learning Fundamentals: Which exercise best practices the actual skill taught in Training and Optimization in Deep Learning Fundamentals?
4Path review for Deep Learning Fundamentals: Which diagnostic action is relevant when Convolutional Models in Deep Learning Fundamentals does not behave as expected?
5Path review for Deep Learning Fundamentals: Which statement is a core idea you need to understand for Sequence and Attention Models in Deep Learning Fundamentals?
04
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Skill assessment

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