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Dismiss alert {{ message }} jla524 / fromthetensor Public * Notifications You must be signed in to change notification settings * Fork 18 * Star 428 From the Tensor to Stable Diffusion, a rough outline for a 9 week course. 428 stars 18 forks Branches Tags Activity Star Notifications You must be signed in to change notification settings * Code * Issues 0 * Pull requests 0 * Actions * Projects 0 * Security * Insights Additional navigation options * Code * Issues * Pull requests * Actions * Projects * Security * Insights jla524/fromthetensor This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. main BranchesTags Go to file Code Folders and files Name Name Last commit message Last commit date Latest commit History 187 Commits examples examples README.md README.md View all files Repository files navigation * README From the Tensor to Stable Diffusion Inspired by From the Transistor. Machine learning is hard, a lot of tutorials are hard to follow, and it's hard to understand software 2.0 from first principles. You wanna be an ML engineer? Well, here's the steps to get good at that: 1. Download a paper 2. Implement it 3. Keep doing this until you have skills -- George Hotz Section 1: Intro: Cheating our way past the Tensor -- 1 week * So about those Tensors -- Course overview. Describe how Deep Learning models are buildable using Tensors, and how different architectures like CNNs and RNNs use Tensors in different ways. Understand the concept of backpropagation and gradient descent. [ video] Section 2: Deep Learning: What is deep learning anyway? -- 1 week * Building a simple Neural Network -- Your first little program! Getting the model working and learning the basics of deep learning. [code] [video] * Building a simple CNN -- An intro chapter to deep learning, learn how to build a simple CNN and understand the concepts of convolution and pooling. [code] [video] * Building a simple RNN -- Learn the basics of Recurrent Neural Networks and understand the concept of "memory" that helps them store states of previous inputs. [code] [video] Section 3: Implementing Papers (Part 1): Vision models -- 3 weeks * Implementing LeNet -- Learn about the LeNet architecture and its application. [code] [paper] * Implementing AlexNet -- Learn how to implement AlexNet for image classification tasks. [code] [paper] * Implementing ResNet -- Learn how to implement ResNet for image classification tasks. [code] [paper] * Building a DCGAN -- Learn how to build a DCGAN and the concept of adversarial training. [code] [paper] Section 4: Implementing Papers (Part 2): Language models -- 3 weeks * Implementing GRU and LSTM -- Learn about the concepts of LSTM and GRU cells. [code] [paper] * Implementing CBOW and Skip-Gram -- Learn about the word2vec architecture and its application. [code] [paper] * Building a Transformer -- Learn about the transformer architecture and its application. [code] [paper] * Fine-tuning a BERT -- Learn about the BERT architecture and fine-tuning a pre-trained model. [code] [paper] Section 5: Implementing Papers (Part 3): Vision-Language models -- 1 week * Building a Stable Diffusion model -- Learn about the Stable Diffusion architecture and its application in image generation tasks. [code] [paper] About From the Tensor to Stable Diffusion, a rough outline for a 9 week course. Topics deep-learning pytorch tinygrad Resources Readme Activity Stars 428 stars Watchers 5 watching Forks 18 forks Report repository Releases No releases published Packages 0 No packages published Contributors 3 * @jla524 jla524 Jacky Lee * @geohotstan geohotstan * @jackylee328 jackylee328 Jacky Lee Footer (c) 2024 GitHub, Inc. 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