{"id":19209198,"url":"https://github.com/hrolive/deep-learning-nanodegree","last_synced_at":"2026-04-12T00:02:09.865Z","repository":{"id":104688368,"uuid":"176975137","full_name":"HROlive/Deep-Learning-Nanodegree","owner":"HROlive","description":"As one of the top 3% students from the first phase, \"PyTorch Scholarship Challenge\" by Facebook AI, I have earned a full scholarship to Udacity’s Deep Learning Nanodegree program","archived":false,"fork":false,"pushed_at":"2020-02-06T18:31:11.000Z","size":24447,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-01-04T16:24:05.331Z","etag":null,"topics":["api-gateway","aws","aws-lambda","aws-sagemaker","computer-vision","convolutional-neural-networks","deep-learning","deployment","machine-learning","natural-language-processing","numpy","pandas","python","pytorch","scikit-learn"],"latest_commit_sha":null,"homepage":"https://eu.udacity.com/course/deep-learning-nanodegree--nd101","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/HROlive.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":"2019-03-21T15:36:35.000Z","updated_at":"2022-10-07T11:41:05.000Z","dependencies_parsed_at":null,"dependency_job_id":"d01a898b-52af-4cea-aeb0-28d32708c4a6","html_url":"https://github.com/HROlive/Deep-Learning-Nanodegree","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/HROlive%2FDeep-Learning-Nanodegree","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/HROlive%2FDeep-Learning-Nanodegree/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/HROlive%2FDeep-Learning-Nanodegree/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/HROlive%2FDeep-Learning-Nanodegree/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/HROlive","download_url":"https://codeload.github.com/HROlive/Deep-Learning-Nanodegree/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":240281020,"owners_count":19776429,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","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":["api-gateway","aws","aws-lambda","aws-sagemaker","computer-vision","convolutional-neural-networks","deep-learning","deployment","machine-learning","natural-language-processing","numpy","pandas","python","pytorch","scikit-learn"],"created_at":"2024-11-09T13:29:30.514Z","updated_at":"2025-11-14T00:02:24.550Z","avatar_url":"https://github.com/HROlive.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# [Deep Learning Nanodegree Program](https://www.udacity.com/course/deep-learning-nanodegree--nd101)\n\n## Program Summary\n\nBecome an expert in neural networks, and learn to implement them using the deep learning framework PyTorch. Build convolutional networks for image recognition, recurrent networks for sequence generation, generative adversarial networks for image generation, and learn how to deploy models accessible from a website.\n\nThis repository contains the projects that I've developed during [this program.](https://www.udacity.com/course/deep-learning-nanodegree--nd101).\n\n## Projects\n\nProject 1 : [Predicting Bike-Sharing Patterns](https://github.com/HROlive/Deep-Learning-Nanodegree/tree/master/project_1-bikesharing)\n-\n![Project 1_Predicting Bike-Sharing Patterns](https://user-images.githubusercontent.com/14244685/55161620-2c301580-5190-11e9-8947-4535a4993201.PNG)\n\nIn this project, I have implemented a neural network in Numpy to predict bike rentals.\n\nProject 2 :  [Dog Breed Classifier](https://github.com/HROlive/Deep-Learning-Nanodegree/tree/master/project_2-dog-classification):\n-\n![dog breed](https://user-images.githubusercontent.com/14244685/56671322-40efc280-66d6-11e9-80b6-3a2fd9657d10.PNG)\n\nIn this project, I have built a convolutional neural network with PyTorch to classify any image (even an image of a face) as a specific dog breed.\n\nProject 3 :  [TV Script Generator](https://github.com/HROlive/Deep-Learning-Nanodegree/tree/master/project_3-tv-script-generation): \n-\n![Generating TV script](https://user-images.githubusercontent.com/14244685/56514538-5b4f6200-6557-11e9-9235-084928059a5a.PNG)\n\nIn this project, I have trained a recurrent neural network to generate scripts in the style of dialogue from Seinfeld.\n\nProject 4 :  [Face Generation](https://github.com/HROlive/Deep-Learning-Nanodegree/tree/master/project_4-face_generation): \n-\n![Face Generation](https://user-images.githubusercontent.com/14244685/56671622-cbd0bd00-66d6-11e9-9a8d-d8b8725a054e.PNG)\n\nIn this project, I have used a DCGAN on the CelebA dataset to generate images of new and realistic human faces.\n\nProject 5 :  [Sentiment Analysis SageMaker Deployment](https://github.com/HROlive/Deep-Learning-Nanodegree/tree/master/project_5-sentiment_analysis): \n-\n![Sentiment Analysis](https://www.marketmotive.com/market_motive/sentiment-analysis.jpg)\n\nIn this project, I used Amazon SageMaker, AWS Lambda and Amazon API Gateway to develop a complete project from end to end. The result is a simple web app which interacts with a deployed recurrent neural network performing sentiment analysis on movie reviews.\n\n---\n\n# Dependencies\n\n## Configure and Manage Your Environment with Anaconda\n\nPer the Anaconda [docs](http://conda.pydata.org/docs):\n\n\u003e Conda is an open source package management system and environment management system \nfor installing multiple versions of software packages and their dependencies and \nswitching easily between them. It works on Linux, OS X and Windows, and was created \nfor Python programs but can package and distribute any software.\n\n## Overview\nUsing Anaconda consists of the following:\n\n1. Install [`miniconda`](http://conda.pydata.org/miniconda.html) on your computer, by selecting the latest Python version for your operating system. If you already have `conda` or `miniconda` installed, you should be able to skip this step and move on to step 2.\n2. Create and activate * a new `conda` [environment](http://conda.pydata.org/docs/using/envs.html).\n\n\\* Each time you wish to work on any exercises, activate your `conda` environment!\n\n---\n\n## 1. Installation\n\n**Download** the latest version of `miniconda` that matches your system.\n\n|        | Linux | Mac | Windows | \n|--------|-------|-----|---------|\n| 64-bit | [64-bit (bash installer)][lin64] | [64-bit (bash installer)][mac64] | [64-bit (exe installer)][win64]\n| 32-bit | [32-bit (bash installer)][lin32] |  | [32-bit (exe installer)][win32]\n\n[win64]: https://repo.continuum.io/miniconda/Miniconda3-latest-Windows-x86_64.exe\n[win32]: https://repo.continuum.io/miniconda/Miniconda3-latest-Windows-x86.exe\n[mac64]: https://repo.continuum.io/miniconda/Miniconda3-latest-MacOSX-x86_64.sh\n[lin64]: https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh\n[lin32]: https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86.sh\n\n**Install** [miniconda](http://conda.pydata.org/miniconda.html) on your machine. Detailed instructions:\n\n- **Linux:** http://conda.pydata.org/docs/install/quick.html#linux-miniconda-install\n- **Mac:** http://conda.pydata.org/docs/install/quick.html#os-x-miniconda-install\n- **Windows:** http://conda.pydata.org/docs/install/quick.html#windows-miniconda-install\n\n## 2. Create and Activate the Environment\n\nFor Windows users, these following commands need to be executed from the **Anaconda prompt** as opposed to a Windows terminal window. For Mac, a normal terminal window will work. \n\n#### Git and version control\nThese instructions also assume you have `git` installed for working with Github from a terminal window, but if you do not, you can download that first with the command:\n```\nconda install git\n```\n\n**Now, we're ready to create our local environment!**\n\n1. Clone the repository, and navigate to the downloaded folder.\n\n2. Create (and activate) a new environment, named `deep-learning` with Python 3.6. If prompted to proceed with the install `(Proceed [y]/n)` type y.\n\n\t- __Linux__ or __Mac__: \n\t```\n\tconda create -n deep-learning python=3.6\n\tsource activate deep-learning\n\t```\n\t- __Windows__: \n\t```\n\tconda create --name deep-learning python=3.6\n\tactivate deep-learning\n\t```\n\t\n\tAt this point your command line should look something like: `(deep-learning) \u003cUser\u003e:deep-learning-v2-pytorch \u003cuser\u003e$`. The `(deep-learning)` indicates that your environment has been activated, and you can proceed with further package installations.\n\n3. Install PyTorch and torchvision; this should install the latest version of PyTorch.\n\t\n\t- __Linux__ or __Mac__: \n\t```\n\tconda install pytorch torchvision -c pytorch \n\t```\n\t- __Windows__: \n\t```\n\tconda install pytorch -c pytorch\n\tpip install torchvision\n\t```\n\n6. Install a few required pip packages, which are specified in the requirements text file (including OpenCV).\n```\npip install -r requirements.txt\n```\n\n7. That's it!\n\nNow most of the `deep-learning` libraries are available to you. Very occasionally, you will see a repository with an addition requirements file, which exists should you want to use TensorFlow and Keras, for example. In this case, you're encouraged to install another library to your existing environment, or create a new environment for a specific project. \n\nNoe, assuming your `deep-learning` environment is still activated, you can navigate to the main repo and start looking at the notebooks:\n\n```\ncd\ncd ***YOUR PROJECT REPOSITORY***\njupyter notebook\n```\n\nTo exit the environment when you have completed your work session, simply close the terminal window.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhrolive%2Fdeep-learning-nanodegree","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhrolive%2Fdeep-learning-nanodegree","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhrolive%2Fdeep-learning-nanodegree/lists"}