![]() Lesson 5 - Back propagation Accelerated SGD Neural net from scratch Work with the fastai.tabular module to set up and train a model.Ĭollaborative filtering (recommendation systems).Īn “embedding” is simply a computational shortcut for a particular type of matrix multiplication (a multiplication by a one-hot encoded matrix e.g. Then fine-tune this model for the final classification taskĬover tabular data (such as spreadsheets and database tables). Remove the encoder in this fine tuned language model, and replace it with a classifier.Fine-tune this language model using your target corpus.Create (or use pretrained) language model (predict the next word of a sentence).Here's a popular science article on the model Predict whether a movie review is positive or negative using ULMFiT. Lesson 4 - NLP Tabular data Collaborative filtering Embeddings Predict face keypoints (interesting areas) Image segmentation - process of labeling every pixel in an image with a category that shows what kind of object is portrayed by that pixel. Use the data block API to get the data into shape (more info here). Lesson 3 - Data blocks Multi-label classification Segmentation ![]() ![]() Using the model to find and fix mislabeled or incorrectly-collected images.Ĭreate a model and our own gradient descent loop. Lesson 2 - Data cleaning and production SGD from scratch Set the most important hyper-parameter when training neural networks: the learning rate, using Leslie Smith’s fantastic learning rate finder method.įeatures that fastai provides for allowing you to easily add labels to your images. The videos can be found in a YouTube playlist. The course uses pytorch and the fastai wrapper. Image localization (segmentation and activation maps).Notes on how to setup the course in Azure here: Notes on how to setup the couse in GCP here: Seven lessons, each around 2 hours long, and you should plan to spend about 10 hours on assignments for each lesson. A blog post on what you need for deep learning:
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