{"id":18339904,"url":"https://github.com/mxagar/deep_learning_udacity","last_synced_at":"2026-04-17T13:32:15.094Z","repository":{"id":41175016,"uuid":"489022598","full_name":"mxagar/deep_learning_udacity","owner":"mxagar","description":"These are my personal notes taken while following the Udacity Deep Learning Nanodegree.","archived":false,"fork":false,"pushed_at":"2024-11-01T17:03:01.000Z","size":68731,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-10-26T07:34:57.690Z","etag":null,"topics":["cnn","computer-vision","convolutional-neural-networks","deep-learning","deployment","gans","image-classification","natural-language-processing","neural-networks","object-detection","pytorch"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/mxagar.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2022-05-05T15:14:28.000Z","updated_at":"2024-11-01T17:03:06.000Z","dependencies_parsed_at":"2024-12-23T16:34:20.668Z","dependency_job_id":null,"html_url":"https://github.com/mxagar/deep_learning_udacity","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/mxagar/deep_learning_udacity","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mxagar%2Fdeep_learning_udacity","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mxagar%2Fdeep_learning_udacity/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mxagar%2Fdeep_learning_udacity/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mxagar%2Fdeep_learning_udacity/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/mxagar","download_url":"https://codeload.github.com/mxagar/deep_learning_udacity/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mxagar%2Fdeep_learning_udacity/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31931309,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-17T12:37:54.787Z","status":"ssl_error","status_checked_at":"2026-04-17T12:37:25.095Z","response_time":62,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["cnn","computer-vision","convolutional-neural-networks","deep-learning","deployment","gans","image-classification","natural-language-processing","neural-networks","object-detection","pytorch"],"created_at":"2024-11-05T20:19:47.702Z","updated_at":"2026-04-17T13:32:15.075Z","avatar_url":"https://github.com/mxagar.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Udacity Deep Learning Nanodegree: Personal Notes\n\nThese are my personal notes taken while following the [Udacity Deep Learning Nanodegree](https://www.udacity.com/course/deep-learning-nanodegree--nd101).\n\nThe nanodegree is composed of six modules:\n\n1. [Introduction to Deep Learning](01_Intro_Deep_Learning)\n2. [Neural Networks and Pytorch/Keras Guides](02_Neural_Networks)\n3. [Convolutional Neural Networks (CNN)](03_CNN)\n4. [Recurrent Neural Networks (RNN)](04_RNN)\n5. [Generative Adversarial Networks (GAN)](05_GAN)\n6. [Deploying a Model with AWS SageMaker](06_Deployment)\n\nAdditionally, I have added an extra module/subfolder which I will extend with *new* architectures, applications and tools that appeared post 2018: [Extra](07_Extra).\n\nEach module has a folder with its respective notes; **you need to go to each module folder and follow the Markdown file in them**.\n\nFinally, note that:\n\n- I have also notes on the [Udacity Computer Vision Nanodegree](https://www.udacity.com/course/computer-vision-nanodegree--nd891) in my repository [computer_vision_udacity](https://github.com/mxagar/computer_vision_udacity); that MOOC is strongly related and has complementary material.\n- In addition to the [Pytorch guide](02_Pytorch_Guide), I have a [Keras guide](02_Keras_Guide); both condense the most important features of both frameworks. Currently, the Pytorch guide is more detailed.\n- I have many hand-written notes you can check, too (see the PDFs).\n- I have a specific repository for **object detection** and **semantic segmentation**, where additionally **labeling** tools are covered: [detection_segmentation_pytorch](https://github.com/mxagar/detection_segmentation_pytorch).\n- The exercises are commented in the Markdown files and linked to their location; most of the exercises are located in other repositories, originally forked from Udacity and extended/completed by me:\n\t- [deep-learning-v2-pytorch](https://github.com/mxagar/deep-learning-v2-pytorch)\n\t- [CVND_Exercises](https://github.com/mxagar/CVND_Exercises)\n\t- [DL_PyTorch](https://github.com/mxagar/DL_PyTorch)\n\t- [CVND_Localization_Exercises](https://github.com/mxagar/CVND_Localization_Exercises)\n\t- [sagemaker-deployment](https://github.com/mxagar/sagemaker-deployment)\n\n## Projects\n\nUdacity requires the submission of a project for each module; these are the repositories of the projects I submitted:\n\n1. Predicting Bike Sharing Patterns with Neural Networks Written from Scratch with Numpy: [project-bikesharing](https://github.com/mxagar/deep-learning-v2-pytorch/tree/master/project-bikesharing).\n2. Dog Breed Classification with Convolutional Neural Networks (CNNs) and Transfer Learning: [project-dog-classification](https://github.com/mxagar/deep-learning-v2-pytorch/tree/master/project-dog-classification).\n3. Text Generation: TV Script Creation with a Recurrent Neural Network (RNN): [text_generator](https://github.com/mxagar/text_generator).\n4. Face Generation with a Convolutional Generative Adversarial Network (GAN): [face_generator_gan](https://github.com/mxagar/face_generator_gan).\n5. Sentiment Analysis RNN Deployed Using AWS SageMaker: [sentiment_rnn_aws_deployment](https://github.com/mxagar/sentiment_rnn_aws_deployment). \n\n\n## Practical Installation Notes\n\nI basically followed the installation \u0026 setup guide from [deep-learning-v2-pytorch](https://github.com/mxagar/deep-learning-v2-pytorch), which can be summarized with the following commands:\n\n```bash\n# Create new conda environment to be used for the nanodegree\nconda create -n dlnd python=3.6\nconda activate dlnd\nconda install pytorch torchvision -c pytorch\nconda install pip\n\n# Go to the folder where the Udacity DL exercises are cloned, after forking the original repo\ncd ~/git_repositories/deep-learning-v2-pytorch\npip install -r requirements.txt\n```\n\nMikel Sagardia, 2022.  \nNo guarantees.\n\nIf you find this repository helpful and use it, please link to the original source.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmxagar%2Fdeep_learning_udacity","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmxagar%2Fdeep_learning_udacity","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmxagar%2Fdeep_learning_udacity/lists"}