Complete for freeAdvancedBrowser simulator included

Course 20

Deep Learning and Computer Vision

Understand and evaluate neural networks, optimization, CNNs, augmentation, transfer learning, detection, segmentation, tracking, GPU trade-offs, and responsible computer vision. The course combines structured lessons, guided browser concept simulations, and learner-created project files made with freely installable or open tools described in the course instructions. This guided page does not execute code, train models, run databases, create repositories, access hardware, or provide hosted infrastructure.

Free completion promise: The always-free pathway combines neural-network concept simulations with learner-created experiment and model-card files. The C4T page does not provide checkpoints or train models; GPU hardware and paid compute are optional.

Before you begin

Requirements & setup for Deep Learning and Computer Vision

Start with the complete free pathway below. Hardware, subscriptions, paid APIs, and cloud credits are never required.

Start here · free path

Your complete $0 setup

The always-free pathway combines neural-network concept simulations with learner-created experiment and model-card files. The C4T page does not provide checkpoints or train models; GPU hardware and paid compute are optional.

$0 required

Setup checklist

0 of 6 complete

This checklist is a temporary setup aid and resets when the page reloads. Saved course progress begins in the setup lessons below.

  1. Use the browser-based tools and simulations throughout this course. Paid software, APIs, cloud credits, GPUs, and physical equipment are optional.

    About 2 min

  2. Open the concept simulator and read its scenario, assumptions, and limitations.

    About 5 minOpen C4T Deep Learning Visual Concept Simulator (opens in a new tab)

  3. Make a prediction, adjust the available controls, and compare the result with the explanation.

    About 5 min

  4. Record what changed and why; repeat with at least one different input.

    About 5 min

  5. For applied work, follow the course instructions to create and keep your own project files with a freely installable or open tool appropriate to the subject.

    About 5 min

  6. C4T Deep Learning Visual Concept Simulator: Complete one prediction-and-explanation cycle and record the evidence you would carry into a learner-created project file.

    About 5 min

Software

1 item

C4T Deep Learning Visual Concept Simulator

A free browser concept simulator with adjustable values, a prediction prompt, deterministic feedback, and a short explanatory trace. This guided page does not execute code, train models, run databases, create repositories, access hardware, or provide hosted infrastructure.

Course need
C4T Deep Learning Visual Concept Simulator
Free

A free browser concept simulator with adjustable values, a prediction prompt, deterministic feedback, and a short explanatory trace. This guided page does not execute code, train models, run databases, create repositories, access hardware, or provide hosted infrastructure. Use keyboard controls and the text trace or written alternative when a visual or pointer interaction is not suitable.

  1. Open the concept simulator and read its scenario, assumptions, and limitations.
  2. Make a prediction, adjust the available controls, and compare the result with the explanation.
  3. Record what changed and why; repeat with at least one different input.
  4. For applied work, follow the course instructions to create and keep your own project files with a freely installable or open tool appropriate to the subject.
Open C4T Deep Learning Visual Concept Simulator (opens in a new tab)

No-hardware path

Deep Learning Visual Concept Simulator

Explore course concepts through adjustable values, prediction, deterministic feedback, and an explanatory trace. No hardware is required.

Hardware

Nothing required

No physical hardware is needed for this course.

Accounts

Nothing required

No external account is needed for this course.

What you will be able to do

Learning outcomes

  • Trace tensor shapes, layers, activations, losses, gradients, and training loops.
  • Design and evaluate CNN, augmentation, and transfer-learning experiments.
  • Compare classification, detection, and segmentation outputs.
  • Document resource use, errors, limitations, and responsible-use constraints.

Included downloads

Free starter assets

Original · synthetic · licensed

These small files contain no personal data, credentials, or secrets. Dataset fixtures are CC0 and starter code is MIT licensed.

Starter pack guide & licensesSee what each original synthetic dataset, fixture, and starter file contains.ML evaluation fixtureFixed, synthetic classification, retrieval, research, and deployment values.

Build real work

Assignments & capstone

Free tools accepted

Optimization and training diagnosis

Trace a small network design, plan a training-loop repair, and diagnose learner-created or openly licensed example loss and validation curves.

The always-free pathway combines neural-network concept simulations with learner-created experiment and model-card files. The C4T page does not provide checkpoints or train models; GPU hardware and paid compute are optional. Submit a project file, text trace, screenshots with descriptions, or equivalent simulator evidence.

Transfer-learning experiment plan

Design an augmentation, transfer, tracking, and evaluation plan for a small vision dataset.

The always-free pathway combines neural-network concept simulations with learner-created experiment and model-card files. The C4T page does not provide checkpoints or train models; GPU hardware and paid compute are optional. Submit a project file, text trace, screenshots with descriptions, or equivalent simulator evidence.

Final assessment

Responsible computer-vision model study

Build or analyze a custom vision model using a documented dataset, architecture, augmentation, training and validation, experiment tracking, errors, metrics, resources, a model card, and responsible-use limitations. Deliverables: Documented dataset and task, Architecture and augmentation rationale, Training record or learner-created or lawfully obtained trace analysis, Metrics and concrete error analysis, and Resource discussion and model card.

Prerequisites & pacing

Skills assumed

  • Rigorous classical ML evaluation and strong Python fundamentals

Recommended prior courses

These are advisory, not locks. If you already have the skills listed above, enroll directly.

Choose your pace

  • self paced: 12 weeks at 6 hours per week
  • weekly: 12 weeks at 6 hours per week

Part of this learning path

Course created by Zafir Shamsi.