Intro to Deep Learning
by Ryan Holbrook · Kaggle
Our Verdict
Worth takingKaggle's free "Intro to Deep Learning," written by Ryan Holbrook, is a genuinely good 6-lesson, roughly 4-hour micro-course that teaches you to build, train and regularize fully-connected neural networks for structured (tabular) data using Keras on a TensorFlow backend, all inside a zero-setup browser notebook. Our verdict: take it as a fast, hands-on first step, but treat it as a primer rather than a complete deep-learning education. Independent reviewer Akash Tandon, who covered Kaggle's deep-learning material, summed up the core trade-off well: the track "only scratches the surface, even if in a better manner than most tutorials out there." It deliberately skips the math (no calculus or backprop derivations) and never touches CNNs, RNNs, transformers or PyTorch, and it explicitly assumes you have already done Kaggle's Intro to Machine Learning course. For the price of free plus a shareable completion certificate, it is one of the lowest-friction ways to write your first working neural network.
Free, fast, fully hands-on, no environment setup, and taught by a credible author; the honest caveat is that it is a narrow primer on dense networks for tabular data, so its value depends on you already knowing basic ML and planning to continue with deeper courses afterward.
Best for: Beginners who have already completed an intro-ML course (ideally Kaggle's own Intro to Machine Learning) and want a quick, code-first introduction to building and training neural networks in Keras without installing anything. Great for tabular/structured-data practitioners, students wanting a confidence-building first win, and anyone who learns better by running and editing code than by watching lectures.
Skip if: Complete beginners with no ML background (start with Intro to Machine Learning first); people who want the underlying mathematics (calculus, backpropagation, linear algebra) or deep theory; anyone needing computer vision (CNNs), NLP/transformers, sequence models, or PyTorch; and learners who prefer structured video lectures over self-paced notebooks. It is also not a credential that carries weight with employers.
About This Course
Build and optimize deep neural networks with TensorFlow and Keras through hands-on exercises.
What You'll Learn
Curriculum
The linear unit / single neuron, the y = wx + b formulation, handling multiple inputs, and defining a linear model in Keras.
Stacking dense layers in a Keras Sequential model and using activation functions (ReLU and beyond) to build fully-connected networks.
How training works: loss functions (MAE, MSE), the SGD optimizer, minibatches, epochs, learning rate and batch size.
Reading learning curves to spot under/overfitting and adding capacity or early stopping to improve generalization.
Two special layers: dropout to reduce overfitting and batch normalization to stabilize and speed up training.
Applying deep learning to classification with cross-entropy loss, a sigmoid output and accuracy as the metric.
An optional capstone notebook (added by the author) showing how to run a model on a large real dataset using TPUs; not required to earn the certificate.
Prerequisites
- Basic Python and pandas (loading and manipulating DataFrames)
- Familiarity with core machine-learning ideas such as features, targets, train/validation split and overfitting (Kaggle officially positions this course as building on its Intro to Machine Learning course)
- No prior deep-learning knowledge and no local software install required (everything runs in Kaggle's browser notebooks)
Instructor
Ryan Holbrook
Instructor · Kaggle
Pros & Cons
Pros
- Completely free and includes a shareable completion certificate, with no software setup since everything runs in Kaggle's browser notebooks
- Very hands-on and self-paced: every lesson pairs a short explanation with a coding exercise that has hints and solutions, so you write real Keras code from lesson one
- Uses the high-level Keras API on TensorFlow, widely regarded as a beginner-friendly choice; reviewer Akash Tandon praised Keras for being 'well-designed, user-friendly and playing a big role in democratizing access to deep learning'
- Tightly scoped (about 4 hours, 6 core lessons) so it is realistic to finish in a day, and it slots cleanly into Kaggle's learning path between Intro to Machine Learning and Computer Vision
- Authored by Ryan Holbrook, a recognized Kaggle curriculum author, with an active course discussion forum for help
Cons
- Intentionally shallow: as Akash Tandon's independent review notes, the material 'only scratches the surface,' and simply 'getting the code to work alone won't be very useful' without further study
- Almost no mathematics or theory (no calculus, backpropagation derivations, or linear-algebra grounding), so it builds intuition and code fluency more than deep understanding
- Narrow scope: it covers only dense/fully-connected networks on structured data and does not teach CNNs, RNNs, transformers/LLMs, or PyTorch; those live in separate Kaggle courses or other providers
- Not a standalone starting point: it assumes prior ML knowledge, so true beginners must take Intro to Machine Learning first, and the certificate carries little weight as a formal credential
Alternatives To Consider
Frequently Asked Questions
Is Intro to Deep Learning free?
Yes — Intro to Deep Learning is free to access. Free. Like all Kaggle Learn micro-courses, there is no paywall, no free-trial gimmick, and no upsell; a completion certificate is issued at no cost after finishing the 6 required lessons.
Who is Intro to Deep Learning for?
Beginners who have already completed an intro-ML course (ideally Kaggle's own Intro to Machine Learning) and want a quick, code-first introduction to building and training neural networks in Keras without installing anything. Great for tabular/structured-data practitioners, students wanting a confidence-building first win, and anyone who learns better by running and editing code than by watching lectures.
What will you learn in Intro to Deep Learning?
How a single neuron / linear unit works (the y = wx + b building block) and how to express it in Keras; Building deep networks by stacking dense layers in a Keras Sequential model and adding nonlinearity with activation functions like ReLU; Training networks with stochastic gradient descent: loss functions (MAE/MSE), optimizers, minibatches, epochs, learning rate and batch size; Diagnosing overfitting vs. underfitting from learning curves and using early stopping.
What are the prerequisites for Intro to Deep Learning?
Basic Python and pandas (loading and manipulating DataFrames); Familiarity with core machine-learning ideas such as features, targets, train/validation split and overfitting (Kaggle officially positions this course as building on its Intro to Machine Learning course); No prior deep-learning knowledge and no local software install required (everything runs in Kaggle's browser notebooks).
Is Intro to Deep Learning worth it?
Free, fast, fully hands-on, no environment setup, and taught by a credible author; the honest caveat is that it is a narrow primer on dense networks for tabular data, so its value depends on you already knowing basic ML and planning to continue with deeper courses afterward.
How we reviewed this course
This is an independent editorial assessment by Cursarium, based on Kaggle's published course materials and aggregated public learner feedback (last reviewed 2026-06). We have not independently completed the course. Links to providers are standard references, not paid placements.
Sources
- Official course page - Learn Intro to Deep Learning (Kaggle)
- Class Central listing - Intro to Deep Learning from Kaggle (free, ~4h, syllabus + reviews)
- Independent review - 'Kaggle Learn review: there is a deep learning track and it is worth your time' (Akash Tandon)
- Course notebooks mirror confirming the 6 lesson titles (drakearch/kaggle-courses)
- Lesson 1 'A Single Neuron' by Ryan Holbrook (Kaggle notebook)
- Bonus capstone 'Detecting the Higgs Boson With TPUs' by Ryan Holbrook