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Get startedStart watching lesson 1 now!The Economist“This month fast.ai, an education non-profit based in San Francisco, kicked off the third year of its course in deep learning. Since its inception it has attracted more than 100,000 students, scattered ar

Kaggle is the world’s largest data science community. One of Kaggle’s features is “Notebooks”, which is “a cloud computational environment that enables reproducible and collaborative analysis”. In particular, Kaggle provides access to GPUs for free. Every

If you need help, there’s a wonderful online community ready to help you at forums.fast.ai. Before asking a question on the forums, search carefully to see if your question has been answered before. (The forum system won’t let you post until you’ve spent a

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In this lesson, we work with Tiny Imagenet to create a super-resolution U-Net model, discussing dataset creation, preprocessing, and data augmentation. The goal of super-resolution is to scale up a low-resolution image to a higher resolution. We train the

Jeremy begins this lesson with a discussion of improvements to the DDPM/DDIM implementation. He explores the removal of the concept of an integral number of steps, making the process more continuous. He then delves into predicting the amount of noise in an

In this lesson, Jeremy, Johno, and Tanishq discuss their experiments with the Fashion-MNIST dataset and the CIFAR-10 dataset, a popular dataset for image classification and generative modeling. They introduce Weights and Biases (W&B), an experiment trackin

In this lesson, we dive into mixed precision training and experiment with various techniques. We introduce the MixedPrecision callback for PyTorch and explore the Accelerate library from HuggingFace for speeding up training loops. We also learn a sneaky tr

In this lesson, Jeremy introduces Dropout, a technique for improving model performance, and with special guests Tanishq and Johno he discusses Denoising Diffusion Probabilistic Models (DDPM), the underlying foundational approach for diffusion models. The l

In this lesson, we dive into various stochastic gradient descent (SGD) accelerated approaches, such as momentum, RMSProp, and Adam. We start by experimenting with these techniques in Microsoft Excel, creating a simple linear regression problem and applying

In this lesson, we discuss the importance of weight initialization in neural networks and explore various techniques to improve training. We start by introducing changes to the miniai library and demonstrate the use of HooksCallback and ActivationStats for

In Lesson 16, we dive into building a flexible training framework called the learner. We start with a basic callbacks Learner, which is an intermediate step towards the flexible learner. We introduce callbacks, which are functions or classes called at spec

We start with a dive into convolutional autoencoders and explore the concept of convolutions. Convolutions help neural networks understand the structure of a problem, making it easier to solve. We learn how to apply a convolution to an image using a kernel

【代码】14、反向传播(Backpropagation)

【代码】13、反向传播和MLP(Backpropagation & MLP)

In this lesson, we start by discussing the CLIP Interrogator, a Hugging Face Spaces Gradio app that generates text prompts for creating CLIP embeddings. We then dive back into matrix multiplication, using Einstein summation notation and torch.einsum to sim

In this lesson, we discuss various techniques and experiments shared by students on the forum, such as interpolating between prompts for visually appealing transitions and improving the update process in text-to-image generation, and a novel approach to de

【代码】10、深入挖掘(Diving Deeper)

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Building embeddings from scratch

Gradient accumulation and GPU memory

【代码】摘要Summaries--课时六(Lesson 6)

Linear model and neuralnet from scratch

New and Exciting Content Why Hugging Face transformer Will we in this lecture fine-tune a pretrained NLP model with HF rather than fastai library? Why use transformer rather than fastai library? Is Jeremy in the process of integrating transformer int

Introduction and survey “Lesson 0” How to fast.ai Where is Lesson 0 video? What does it to do with the book ‘meta learning’ and fastai course? How to do a fastai lesson? Watch with note Run the notebook and experiment Reproduce the notes from t

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