Practical Deep Learning for Coders: Lesson 1

Jeremy Howard introduces deep learning, showing how to train an image recognition model using PyTorch and fastai in a Jupyter notebook.

Go to course.fast.ai for code, notebooks, quizzes, etc. This course is designed for people with some coding experience who want to learn how to apply deep learning and machine learning to practical problems. There are 9 lessons, and each lesson is around 90 minutes long. We cover topics such as how to: - Build and train deep learning, random forest, and regression models - Deploy models - Apply deep learning to computer vision, natural language processing, tabular analysis, and collaborative filtering problems - Use PyTorch, the world’s fastest growing deep learning software, together with popular libraries such as fastai, Hugging Face Transformers, and gradio You don’t need any special hardware or software — we’ll show you how to use free resources for both building and deploying models. You don’t need any university math either — we’ll teach you the calculus and linear algebra you need during the course. 00:00 - Introduction 00:25 - What has changed since 2015 01:20 - Is it a bird 02:09 - Images are made of numbers 03:29 - Downloading images 04:25 - Creating a DataBlock and Learner 05:18 - Training the model and making a prediction 07:20 - What can deep learning do now 10:33 - Pathways Language Model (PaLM) 15:40 - How the course will be taught. Top down learning 19:25 - Jeremy Howard’s qualifications 22:38 - Comparison between modern deep learning and 2012 machine learning practices 24:31 - Visualizing layers of a trained neural network 27:40 - Image classification applied to audio 28:08 - Image classification applied to time series and fraud 30:16 - Pytorch vs Tensorflow 31:43 - Example of how Fastai builds off Pytorch (AdamW optimizer) 35:18 - Using cloud servers to run your notebooks (Kaggle) 38:45 - Bird or not bird? & explaining some Kaggle features 40:15 - How to import libraries like Fastai in Python 40:42 - Best practice - viewing your data between steps 42:00 - Datablocks API overarching explanation 44:40 - Datablocks API parameters explanation 48:40 - Where to find fastai documentation 49:54 - Fastai’s learner (combines model & data) 50:40 - Fastai’s available pretrained models 52:02 - What’s a pretrained model? 53:48 - Testing your model with predict method 55:08 - Other applications of computer vision. Segmentation 56:48 - Segmentation code explanation 58:32 - Tabular analysis with fastai 59:42 - show_batch method explanation 1:01:25 - Collaborative filtering (recommendation system) example 1:05:08 - How to turn your notebooks into a presentation tool (RISE) 1:05:45 - What else can you make with notebooks? 1:08:06 - What can deep learning do presently? 1:10:33 - The first neural network - Mark I Perceptron (1957) 1:12:38 - Machine learning models at a high level 1:18:27 - Homework Thanks to bencoman, mike.moloch, amr.malik, and gagan on forums.fast.ai for creating the transcript. Thanks to Raymond-Wu on forums.fast.ai for help with chapter titles.