Tutorials
Step-by-step guides to building real machine learning projects in DLWΛY, from a first classifier trained in your browser to fine-tuning language models.
- Beginner
Train a Classifier in Your Browser, Start to Finish
Go from a CSV file to a trained, tested model without installing anything: load data, prepare it, design a network visually and train it on your own GPU.
25 minGetting Started - Beginner
Explore and Profile a Dataset in the Data Hub
Import a CSV, preview it, profile every column, assign ML roles, add constraints and check for target leakage, all without leaving the browser.
20 minData Hub - Beginner
Clean a Dataset with the Transform Window
Remove duplicates, filter rows and fix types in the Data Hub's Transform window, read the generated SQL, then save a new dataset version and compare it with the original.
25 minData Hub - Beginner
Import Datasets from Kaggle and Hugging Face
Connect a Kaggle account, attach a dataset from a ZIP, then add a Hugging Face Parquet split both as an import and as a live remote link.
15 minData Hub - Intermediate
Connect a Database and Control Writes
Save a PostgreSQL connection, browse its tables, import one live and as a snapshot, then set a write policy so a pipeline can only change what you allow.
25 minData Hub - Intermediate
Prepare Training Data with PrepFlow
Build a visual preparation graph for a messy table: drop useless columns, impute, encode and scale, mark the target, split, preview every step, then materialize the result and generate the equivalent Python.
30 minPrepFlow - Beginner
From Template to Trained Model in the Model Builder
Start from a template, read the problems and resources tabs, train in the browser, see what the run kept, then freeze layers and start from your own weights.
30 minModel Builder - Intermediate
Build Your First Pipeline
Chain read, split, train, evaluate, a quality gate and a package into one repeatable pipeline, publish it, run it with different parameters and compare the runs.
40 minPipelines - Intermediate
Schedule an Incremental Data Refresh
Fetch a file on a schedule, merge it into a table by key so repeats never duplicate, validate it, refresh a dashboard, then backfill a past period.
35 minPipelines - Beginner
Build an Interactive Dashboard
Drag fields onto shelves, add a calculated field and a parameter, wire up filters and cross-filtering, and finish with a bookmarked story and a PNG export.
30 minDashboard - Beginner
Deploy and Export a Model
Validate a trained model, test it in the playground with real inputs, try a pretrained Hugging Face model, and export a single-file standalone playground you can host anywhere.
20 minDeploy - Intermediate
Fine-Tune a Model You Trained
Continue training a model you built on new data, watch validation live, see whether the tuned model beats the base, and generate a LoRA script for a Hugging Face model.
25 minFine-Tune - Beginner
Work with the AI Copilot Agent
Connect a provider, ask questions in chat, hand a task to the agent, control what it may do, add project memory and a safety hook, and undo a turn you do not like.
20 minAI Copilot - Intermediate
Turn a Paper into a Running Experiment
Load the original Research tutorial with no AI connection, review its evidence, change a setting, generate code, run a smoke check and a full run, and read the Dashboard figures.
35 minResearch - Intermediate
Run Python and Training on a Remote Kernel
Attach a Kaggle notebook or Jupyter server, run PrepFlow and Model Builder as native Python, sync project files, and detach cleanly.
20 minCompute - Intermediate
Organise an Image and Annotation Folder
Load a folder of images and YOLO or COCO labels as one dataset, confirm the layout, link images to labels, create splits that keep pairs together and export a manifest and loader code.
30 minData Hub - Intermediate
Model Relationships Between Datasets
Draw relationships between datasets, validate them for orphan rows, define a measure, and export the model as SQL DDL, Pandera or Pydantic.
20 minData Hub - Beginner
Build Your First Neural Network
Learn to build, train, and evaluate a simple feedforward neural network from scratch using PyTorch in DLWΛY.
30 minGetting Started - Beginner
Intro to PyTorch in DLWΛY
Get familiar with tensors, autograd and layers in PyTorch, running on a kernel attached to DLWΛY.
20 minGetting Started - Beginner
Data Loading & Preprocessing
Learn to load, transform, and batch datasets using PyTorch DataLoader and torchvision transforms.
25 minGetting Started - Intermediate
Image Classification with ResNet
Fine-tune a pretrained ResNet-50 model on a custom image classification dataset using transfer learning.
45 minComputer Vision - Advanced
Object Detection with YOLO
Implement real-time object detection using YOLOv8 and visualize bounding boxes on images and video.
60 minComputer Vision - Intermediate
Neural Style Transfer
Apply artistic styles from famous paintings to your photos using convolutional neural networks.
40 minComputer Vision - Intermediate
Text Classification with BERT
Fine-tune BERT for sentiment analysis or topic classification using the Hugging Face Transformers library.
50 minNLP - Advanced
Fine-tuning GPT-2
Fine-tune GPT-2 on a custom text dataset to generate domain-specific text completions.
90 minNLP - Beginner
Sentiment Analysis Pipeline
Build a sentiment classifier using pre-trained models and evaluate it on real-world review datasets.
20 minNLP