Deep Learning Basics — Complete Guide
Deep Learning Basics — Complete Guide: free step-by-step lesson with examples, common mistakes, and interview tips — part of ML.NET Tutorial on Toolliyo Academy.
On this page
ML.NET Tutorial · Lesson 17 of 100
Deep Learning Basics
Foundations → Models → NLP & advanced → MLOps
Foundations · 1 — Context & data · ~6 min · Module 2: Machine Learning Basics
What is this?
Deep learning uses neural nets; in ML.NET you often consume ONNX/TF models rather than training huge nets from scratch.
Why should you care?
AIPredict image/brand models may come from Python but score in .NET via ONNX.
See it live — copy this example
Use a .NET console or Web API project with Microsoft.ML. Run dotnet run after pasting.
// Conceptual: train elsewhere → export ONNX → score in ML.NET
Console.WriteLine("ONNX model → ml.Transforms.ApplyOnnxModel → PredictionEngine");What happened?
- Know when classical ML.NET trainers are enough vs when you need deep models.
- Keep scoring in-process when possible.
Practice next
- List one AIPredict case for classical ML.
- List one for ONNX deep model.
- Sketch ApplyOnnxModel.
- Read ONNX file size budget.
- Note CPU vs GPU needs.
Remember
Classical vs deep. ONNX bridge. Score in .NET.
AIPredict deep vs classical
Fraud stays FastTree; logo detect uses ONNX.
Outcome: Clear tool choice per problem.
Interview prep for this lesson
Practice these questions aloud after reading—each links to a full structured answer.
Sign in to ask a question or upvote helpful answers.
No questions yet — be the first to ask!
