diff --git a/README.en.md b/README.en.md new file mode 100644 index 0000000..a886139 --- /dev/null +++ b/README.en.md @@ -0,0 +1,293 @@ +# Learn PyTorch for Deep Learning + +Welcome to the [Zero to Mastery Learn PyTorch for Deep Learning course](https://dbourke.link/ZTMPyTorch), the second best place to learn PyTorch on the internet (the first being the [PyTorch documentation](https://pytorch.org/docs/stable/index.html)). + +* **Update April 2023:** New [tutorial for PyTorch 2.0](https://www.learnpytorch.io/pytorch_2_intro/) is live! And because PyTorch 2.0 is an additive (new features) and backward-compatible release, all previous course materials will *still* work with PyTorch 2.0. + +
+ + pytorch deep learning by zero to mastery cover photo with different sections of the course + +
+ +## Contents of this page + +* [Course materials/outline](https://github.com/mrdbourke/pytorch-deep-learning#course-materialsoutline) +* [About this course](https://github.com/mrdbourke/pytorch-deep-learning#about-this-course) +* [Status](https://github.com/mrdbourke/pytorch-deep-learning#status) (the progress of the course creation) +* [Log](https://github.com/mrdbourke/pytorch-deep-learning#log) (a log of the course material creation process) + +## Course materials/outline + +* 📖 **Online book version:** All of course materials are available in a readable online book at [learnpytorch.io](https://learnpytorch.io). +* 🎥 **First five sections on YouTube:** Learn PyTorch in a day by watching the [first 25 hours of material](https://youtu.be/Z_ikDlimN6A). +* 🔬 **Course focus:** code, code, code, experiment, experiment, experiment. +* 🏃‍♂️ **Teaching style:** [https://sive.rs/kimo](https://sive.rs/kimo). +* 🤔 **Ask a question:** See the [GitHub Discussions page](https://github.com/mrdbourke/pytorch-deep-learning/discussions) for existing questions/ask your own. + +| **Section** | **What does it cover?** | **Exercises & Extra-curriculum** | **Slides** | +| ----- | ----- | ----- | ----- | +| [00 - PyTorch Fundamentals](https://www.learnpytorch.io/00_pytorch_fundamentals/) | Many fundamental PyTorch operations used for deep learning and neural networks. | [Go to exercises & extra-curriculum](https://www.learnpytorch.io/00_pytorch_fundamentals/#exercises) | [Go to slides](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/slides/00_pytorch_and_deep_learning_fundamentals.pdf) | +| [01 - PyTorch Workflow](https://www.learnpytorch.io/01_pytorch_workflow/) | Provides an outline for approaching deep learning problems and building neural networks with PyTorch. | [Go to exercises & extra-curriculum](https://www.learnpytorch.io/01_pytorch_workflow/#exercises) | [Go to slides](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/slides/01_pytorch_workflow.pdf) | +| [02 - PyTorch Neural Network Classification](https://www.learnpytorch.io/02_pytorch_classification/) | Uses the PyTorch workflow from 01 to go through a neural network classification problem. | [Go to exercises & extra-curriculum](https://www.learnpytorch.io/02_pytorch_classification/#exercises) | [Go to slides](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/slides/02_pytorch_classification.pdf) | +| [03 - PyTorch Computer Vision](https://www.learnpytorch.io/03_pytorch_computer_vision/) | Let's see how PyTorch can be used for computer vision problems using the same workflow from 01 & 02. | [Go to exercises & extra-curriculum](https://www.learnpytorch.io/03_pytorch_computer_vision/#exercises) | [Go to slides](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/slides/03_pytorch_computer_vision.pdf) | +| [04 - PyTorch Custom Datasets](https://www.learnpytorch.io/04_pytorch_custom_datasets/) | How do you load a custom dataset into PyTorch? Also we'll be laying the foundations in this notebook for our modular code (covered in 05). | [Go to exercises & extra-curriculum](https://www.learnpytorch.io/04_pytorch_custom_datasets/#exercises) | [Go to slides](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/slides/04_pytorch_custom_datasets.pdf) | +| [05 - PyTorch Going Modular](https://www.learnpytorch.io/05_pytorch_going_modular/) | PyTorch is designed to be modular, let's turn what we've created into a series of Python scripts (this is how you'll often find PyTorch code in the wild). | [Go to exercises & extra-curriculum](https://www.learnpytorch.io/05_pytorch_going_modular/#exercises) | [Go to slides](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/slides/05_pytorch_going_modular.pdf) | +| [06 - PyTorch Transfer Learning](https://www.learnpytorch.io/06_pytorch_transfer_learning/) | Let's take a well performing pre-trained model and adjust it to one of our own problems. | [Go to exercises & extra-curriculum](https://www.learnpytorch.io/06_pytorch_transfer_learning/#exercises) | [Go to slides](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/slides/06_pytorch_transfer_learning.pdf) | +| [07 - Milestone Project 1: PyTorch Experiment Tracking](https://www.learnpytorch.io/07_pytorch_experiment_tracking/) | We've built a bunch of models... wouldn't it be good to track how they're all going? | [Go to exercises & extra-curriculum](https://www.learnpytorch.io/07_pytorch_experiment_tracking/#exercises) | [Go to slides](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/slides/07_pytorch_experiment_tracking.pdf) | +| [08 - Milestone Project 2: PyTorch Paper Replicating](https://www.learnpytorch.io/08_pytorch_paper_replicating/) | PyTorch is the most popular deep learning framework for machine learning research, let's see why by replicating a machine learning paper. | [Go to exercises & extra-curriculum](https://www.learnpytorch.io/08_pytorch_paper_replicating/#exercises) | [Go to slides](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/slides/08_pytorch_paper_replicating.pdf) | +| [09 - Milestone Project 3: Model Deployment](https://www.learnpytorch.io/09_pytorch_model_deployment/) | So we've built a working PyTorch model... how do we get it in the hands of others? Hint: deploy it to the internet. | [Go to exercises & extra-curriculum](https://www.learnpytorch.io/09_pytorch_model_deployment/#exercises) | [Go to slides](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/slides/09_pytorch_model_deployment.pdf) | +| [PyTorch Extra Resources](https://www.learnpytorch.io/pytorch_extra_resources/) | This course covers a large amount of PyTorch and deep learning but the field of machine learning is vast, inside here you'll find recommended books and resources for: PyTorch and deep learning, ML engineering, NLP (natural language processing), time series data, where to find datasets and more. | - | - | +| [PyTorch Cheatsheet](https://www.learnpytorch.io/pytorch_cheatsheet/) | A very quick overview of some of the main features of PyTorch plus links to various resources where more can be found in the course and in the PyTorch documentation. | - | - | +| [A Quick PyTorch 2.0 Tutorial](https://www.learnpytorch.io/pytorch_2_intro/) | A fasssssst introduction to PyTorch 2.0, what's new and how to get started along with resources to learn more. | - | - | + +## Status + +All materials completed and videos published on Zero to Mastery! + +See the project page for work-in-progress board - https://github.com/users/mrdbourke/projects/1 + +* **Total video count:** 321 +* **Done skeleton code for:** 00, 01, 02, 03, 04, 05, 06, 07, 08, 09 +* **Done annotations (text) for:** 00, 01, 02, 03, 04, 05, 06, 07, 08, 09 +* **Done images for:** 00, 01, 02, 03, 04, 05, 06, 07, 08, 09 +* **Done keynotes for:** 00, 01, 02, 03, 04, 05, 06, 07, 08, 09 +* **Done exercises and solutions for:** 00, 01, 02, 03, 04, 05, 06, 07, 08, 09 + +See the [log](https://github.com/mrdbourke/pytorch-deep-learning#log) for almost daily updates. + +## About this course + +### Who is this course for? + +**You:** Are a beginner in the field of machine learning or deep learning and would like to learn PyTorch. + +**This course:** Teaches you PyTorch and many machine learning concepts in a hands-on, code-first way. + +If you already have 1-year+ experience in machine learning, this course may help but it is specifically designed to be beginner-friendly. + +### What are the prerequisites? + +1. 3-6 months coding Python. +2. At least one beginner machine learning course (however this might be able to be skipped, resources are linked for many different topics). +3. Experience using Jupyter Notebooks or Google Colab (though you can pick this up as we go along). +4. A willingness to learn (most important). + +For 1 & 2, I'd recommend the [Zero to Mastery Data Science and Machine Learning Bootcamp](https://dbourke.link/ZTMMLcourse), it'll teach you the fundamentals of machine learning and Python (I'm biased though, I also teach that course). + +### How is the course taught? + +All of the course materials are available for free in an online book at [learnpytorch.io](https://learnpytorch.io). If you like to read, I'd recommend going through the resources there. + +If you prefer to learn via video, the course is also taught in apprenticeship-style format, meaning I write PyTorch code, you write PyTorch code. + +There's a reason the course motto's include *if in doubt, run the code* and *experiment, experiment, experiment!*. + +My whole goal is to help you to do one thing: learn machine learning by writing PyTorch code. + +The code is all written via [Google Colab Notebooks](https://colab.research.google.com) (you could also use Jupyter Notebooks), an incredible free resource to experiment with machine learning. + +### What will I get if I finish the course? + +There's certificates and all that jazz if you go through the videos. + +But certificates are meh. + +You can consider this course a machine learning momentum builder. + +By the end, you'll have written hundreds of lines of PyTorch code. + +And will have been exposed to many of the most important concepts in machine learning. + +So when you go to build your own machine learning projects or inspect a public machine learning project made with PyTorch, it'll feel familiar and if it doesn't, at least you'll know where to look. + +### What will I build in the course? + +We start with the barebone fundamentals of PyTorch and machine learning, so even if you're new to machine learning you'll be caught up to speed. + +Then we’ll explore more advanced areas including PyTorch neural network classification, PyTorch workflows, computer vision, custom datasets, experiment tracking, model deployment, and my personal favourite: transfer learning, a powerful technique for taking what one machine learning model has learned on another problem and applying it to your own! + +Along the way, you’ll build three milestone projects surrounding an overarching project called FoodVision, a neural network computer vision model to classify images of food. + +These milestone projects will help you practice using PyTorch to cover important machine learning concepts and create a portfolio you can show employers and say "here's what I've done". + +### How do I get started? + +You can read the materials on any device but this course is best viewed and coded along within a desktop browser. + +The course uses a free tool called Google Colab. If you've got no experience with it, I'd go through the free [Introduction to Google Colab tutorial](https://colab.research.google.com/notebooks/basic_features_overview.ipynb) and then come back here. + +To start: + +1. Click on one of the notebook or section links above like "[00. PyTorch Fundamentals](https://www.learnpytorch.io/00_pytorch_fundamentals/)". +2. Click the "Open in Colab" button up the top. +3. Press SHIFT+Enter a few times and see what happens. + +### My question isn't answered + +Please leave a [discussion](https://github.com/mrdbourke/pytorch-deep-learning/discussions) or send me an email directly: daniel (at) mrdbourke (dot) com. + +## Log + +Almost daily updates of what's happening. + +* 15 May 2023 - PyTorch 2.0 tutorial finished + videos added to ZTM/Udemy, see code: https://www.learnpytorch.io/pytorch_2_intro/ +* 13 Apr 2023 - update PyTorch 2.0 notebook +* 30 Mar 2023 - update PyTorch 2.0 notebook with more info/clean code +* 23 Mar 2023 - upgrade PyTorch 2.0 tutorial with annotations and images +* 13 Mar 2023 - add starter code for PyTorch 2.0 tutorial +* 18 Nov 2022 - add a reference for 3 most common errors in PyTorch + links to course sections for more: https://www.learnpytorch.io/pytorch_most_common_errors/ +* 9 Nov 2022 - add PyTorch cheatsheet for a very quick overview of the main features of PyTorch + links to course sections: https://www.learnpytorch.io/pytorch_cheatsheet/ +* 9 Nov 2022 - full course materials (300+ videos) are now live on Udemy! You can sign up here: https://www.udemy.com/course/pytorch-for-deep-learning/?couponCode=ZTMGOODIES7 (launch deal code valid for 3-4 days from this line) +* 4 Nov 2022 - add a notebook for PyTorch Cheatsheet in `extras/` (a simple overview of many of the most important functionality of PyTorch) +* 2 Oct 2022 - all videos for section 08 and 09 published (100+ videos for the last two sections)! +* 30 Aug 2022 - recorded 15 videos for 09, total videos: 321, finished section 09 videos!!!! ... even bigger than 08!! +* 29 Aug 2022 - recorded 16 videos for 09, total videos: 306 +* 28 Aug 2022 - recorded 11 videos for 09, total videos: 290 +* 27 Aug 2022 - recorded 16 videos for 09, total videos: 279 +* 26 Aug 2022 - add finishing touchs to notebook 09, add slides for 09, create solutions and exercises for 09 +* 25 Aug 2022 - add annotations and cleanup 09, remove TK's, cleanup images, make slides for 09 +* 24 Aug 2022 - add annotations to 09, main takeaways, exercises and extra-curriculum done +* 23 Aug 2022 - add annotations to 09, add plenty of images/slides +* 22 Aug 2022 - add annotations to 09, start working on slides/images +* 20 Aug 2022 - add annotations to 09 +* 19 Aug 2022 - add annotations to 09, check out the awesome demos! +* 18 Aug 2022 - add annotations to 09 +* 17 Aug 2022 - add annotations to 09 +* 16 Aug 2022 - add annotations to 09 +* 15 Aug 2022 - add annotations to 09 +* 13 Aug 2022 - add annotations to 09 +* 12 Aug 2022 - add demo files for notebook 09 to `demos/`, start annotating notebook 09 with explainer text +* 11 Aug 2022 - finish skeleton code for notebook 09, course finishes deploying 2x models, one for FoodVision Mini & one for (secret) +* 10 Aug 2022 - add section for PyTorch Extra Resources (places to learn more about PyTorch/deep learning): https://www.learnpytorch.io/pytorch_extra_resources/ +* 09 Aug 2022 - add more skeleton code to notebook 09 +* 08 Aug 2022 - create draft notebook for 09, end goal to deploy FoodVision Mini model and make it publically accessible +* 05 Aug 2022 - recorded 11 videos for 08, total videos: 263, section 08 videos finished!... the biggest section so far +* 04 Aug 2022 - recorded 13 videos for 08, total videos: 252 +* 03 Aug 2022 - recorded 3 videos for 08, total videos: 239 +* 02 Aug 2022 - recorded 12 videos for 08, total videos: 236 +* 30 July 2022 - recorded 11 videos for 08, total videos: 224 +* 29 July 2022 - add exercises + solutions for 08, see live walkthrough on YouTube: https://youtu.be/tjpW_BY8y3g +* 28 July 2022 - add slides for 08 +* 27 July 2022 - cleanup much of 08, start on slides for 08, exercises and extra-curriculum next +* 26 July 2022 - add annotations and images for 08 +* 25 July 2022 - add annotations for 08 +* 24 July 2022 - launched first half of course (notebooks 00-04) in a single video (25+ hours!!!) on YouTube: https://youtu.be/Z_ikDlimN6A +* 21 July 2022 - add annotations and images for 08 +* 20 July 2022 - add annotations and images for 08, getting so close! this is an epic section +* 19 July 2022 - add annotations and images for 08 +* 15 July 2022 - add annotations and images for 08 +* 14 July 2022 - add annotations for 08 +* 12 July 2022 - add annotations for 08, woo woo this is bigggg section! +* 11 July 2022 - add annotations for 08 +* 9 July 2022 - add annotations for 08 +* 8 July 2022 - add a bunch of annotations to 08 +* 6 July 2022 - course launched on ZTM Academy with videos for sections 00-07! 🚀 - https://dbourke.link/ZTMPyTorch +* 1 July 2022 - add annotations and images for 08 +* 30 June 2022 - add annotations for 08 +* 28 June 2022 - recorded 11 videos for section 07, total video count 213, all videos for section 07 complete! +* 27 June 2022 - recorded 11 videos for section 07, total video count 202 +* 25 June 2022 - recreated 7 videos for section 06 to include updated APIs, total video count 191 +* 24 June 2022 - recreated 12 videos for section 06 to include updated APIs +* 23 June 2022 - finish annotations for 07, add exercise template and solutions for 07 + video walkthrough on YouTube: https://youtu.be/cO_r2FYcAjU +* 21 June 2022 - make 08 runnable end-to-end, add images and annotations for 07 +* 17 June 2022 - fix up 06, 07 v2 for upcoming torchvision version upgrade, add plenty of annotations to 08 +* 13 June 2022 - add notebook 08 first version, starting to replicate the Vision Transformer paper +* 10 June 2022 - add annotations for 07 v2 +* 09 June 2022 - create 07 v2 for `torchvision` v0.13 (this will replace 07 v1 when `torchvision=0.13` is released) +* 08 June 2022 - adapt 06 v2 for `torchvision` v0.13 (this will replace 06 v1 when `torchvision=0.13` is released) +* 07 June 2022 - create notebook 06 v2 for upcoming `torchvision` v0.13 update (new transfer learning methods) +* 04 June 2022 - add annotations for 07 +* 03 June 2022 - huuuuuuge amount of annotations added to 07 +* 31 May 2022 - add a bunch of annotations for 07, make code runnable end-to-end +* 30 May 2022 - record 4 videos for 06, finished section 06, onto section 07, total videos 186 +* 28 May 2022 - record 10 videos for 06, total videos 182 +* 24 May 2022 - add solutions and exercises for 06 +* 23 May 2022 - finished annotations and images for 06, time to do exercises and solutions +* 22 May 2202 - add plenty of images to 06 +* 18 May 2022 - add plenty of annotations to 06 +* 17 May 2022 - added a bunch of annotations for section 06 +* 16 May 2022 - recorded 10 videos for section 05, finish videos for section 05 ✅ +* 12 May 2022 - added exercises and solutions for 05 +* 11 May 2022 - clean up part 1 and part 2 notebooks for 05, make slides for 05, start on exercises and solutions for 05 +* 10 May 2022 - huuuuge updates to the 05 section, see the website, it looks pretty: https://www.learnpytorch.io/05_pytorch_going_modular/ +* 09 May 2022 - add a bunch of materials for 05, cleanup docs +* 08 May 2022 - add a bunch of materials for 05 +* 06 May 2022 - continue making materials for 05 +* 05 May 2022 - update section 05 with headings/outline +* 28 Apr 2022 - recorded 13 videos for 04, finished videos for 04, now to make materials for 05 +* 27 Apr 2022 - recorded 3 videos for 04 +* 26 Apr 2022 - recorded 10 videos for 04 +* 25 Apr 2022 - recorded 11 videos for 04 +* 24 Apr 2022 - prepared slides for 04 +* 23 Apr 2022 - recorded 6 videos for 03, finished videos for 03, now to 04 +* 22 Apr 2022 - recorded 5 videos for 03 +* 21 Apr 2022 - recorded 9 videos for 03 +* 20 Apr 2022 - recorded 3 videos for 03 +* 19 Apr 2022 - recorded 11 videos for 03 +* 18 Apr 2022 - finish exercises/solutions for 04, added live-coding walkthrough of 04 exercises/solutions on YouTube: https://youtu.be/vsFMF9wqWx0 +* 16 Apr 2022 - finish exercises/solutions for 03, added live-coding walkthrough of 03 exercises/solutions on YouTube: https://youtu.be/_PibmqpEyhA +* 14 Apr 2022 - add final images/annotations for 04, begin on exercises/solutions for 03 & 04 +* 13 Apr 2022 - add more images/annotations for 04 +* 3 Apr 2022 - add more annotations for 04 +* 2 Apr 2022 - add more annotations for 04 +* 1 Apr 2022 - add more annotations for 04 +* 31 Mar 2022 - add more annotations for 04 +* 29 Mar 2022 - add more annotations for 04 +* 27 Mar 2022 - starting to add annotations for 04 +* 26 Mar 2022 - making dataset for 04 +* 25 Mar 2022 - make slides for 03 +* 24 Mar 2022 - fix error for 03 not working in docs (finally) +* 23 Mar 2022 - add more images for 03 +* 22 Mar 2022 - add images for 03 +* 20 Mar 2022 - add more annotations for 03 +* 18 Mar 2022 - add more annotations for 03 +* 17 Mar 2022 - add more annotations for 03 +* 16 Mar 2022 - add more annotations for 03 +* 15 Mar 2022 - add more annotations for 03 +* 14 Mar 2022 - start adding annotations for notebook 03, see the work in progress here: https://www.learnpytorch.io/03_pytorch_computer_vision/ +* 12 Mar 2022 - recorded 12 videos for 02, finished section 02, now onto making materials for 03, 04, 05 +* 11 Mar 2022 - recorded 9 videos for 02 +* 10 Mar 2022 - recorded 10 videos for 02 +* 9 Mar 2022 - cleaning up slides/code for 02, getting ready for recording +* 8 Mar 2022 - recorded 9 videos for section 01, finished section 01, now onto 02 +* 7 Mar 2022 - recorded 4 videos for section 01 +* 6 Mar 2022 - recorded 4 videos for section 01 +* 4 Mar 2022 - recorded 10 videos for section 01 +* 20 Feb 2022 - recorded 8 videos for section 00, finished section, now onto 01 +* 18 Feb 2022 - recorded 13 videos for section 00 +* 17 Feb 2022 - recorded 11 videos for section 00 +* 16 Feb 2022 - added setup guide +* 12 Feb 2022 - tidy up README with table of course materials, finish images and slides for 01 +* 10 Feb 2022 - finished slides and images for 00, notebook is ready for publishing: https://www.learnpytorch.io/00_pytorch_fundamentals/ +* 01-07 Feb 2022 - add annotations for 02, finished, still need images, going to work on exercises/solutions today +* 31 Jan 2022 - start adding annotations for 02 +* 28 Jan 2022 - add exercies and solutions for 01 +* 26 Jan 2022 - lots more annotations to 01, should be finished tomorrow, will do exercises + solutions then too +* 24 Jan 2022 - add a bunch of annotations to 01 +* 21 Jan 2022 - start adding annotations for 01 +* 20 Jan 2022 - finish annotations for 00 (still need to add images), add exercises and solutions for 00 +* 19 Jan 2022 - add more annotations for 00 +* 18 Jan 2022 - add more annotations for 00 +* 17 Jan 2022 - back from holidays, adding more annotations to 00 +* 10 Dec 2021 - start adding annotations for 00 +* 9 Dec 2021 - Created a website for the course ([learnpytorch.io](https://learnpytorch.io)) you'll see updates posted there as development continues +* 8 Dec 2021 - Clean up notebook 07, starting to go back through code and add annotations +* 26 Nov 2021 - Finish skeleton code for 07, added four different experiments, need to clean up and make more straightforward +* 25 Nov 2021 - clean code for 06, add skeleton code for 07 (experiment tracking) +* 24 Nov 2021 - Update 04, 05, 06 notebooks for easier digestion and learning, each section should cover a max of 3 big ideas, 05 is now dedicated to turning notebook code into modular code +* 22 Nov 2021 - Update 04 train and test functions to make more straightforward +* 19 Nov 2021 - Added 05 (transfer learning) notebook, update custom data loading code in 04 +* 18 Nov 2021 - Updated vision code for 03 and added custom dataset loading code in 04 +* 12 Nov 2021 - Added a bunch of skeleton code to notebook 04 for custom dataset loading, next is modelling with custom data +* 10 Nov 2021 - researching best practice for custom datasets for 04 +* 9 Nov 2021 - Update 03 skeleton code to finish off building CNN model, onto 04 for loading custom datasets +* 4 Nov 2021 - Add GPU code to 03 + train/test loops + `helper_functions.py` +* 3 Nov 2021 - Add basic start for 03, going to finish by end of week +* 29 Oct 2021 - Tidied up skeleton code for 02, still a few more things to clean/tidy, created 03 +* 28 Oct 2021 - Finished skeleton code for 02, going to clean/tidy tomorrow, 03 next week +* 27 Oct 2021 - add a bunch of code for 02, going to finish tomorrow/by end of week +* 26 Oct 2021 - update 00, 01, 02 with outline/code, skeleton code for 00 & 01 done, 02 next +* 23, 24 Oct 2021 - update 00 and 01 notebooks with more outline/code +* 20 Oct 2021 - add v0 outlines for 01 and 02, add rough outline of course to README, this course will focus on less but better +* 19 Oct 2021 - Start repo 🔥, add fundamentals notebook draft v0