{"id":26681290,"url":"https://github.com/unpackai/dl201","last_synced_at":"2025-07-05T00:36:17.022Z","repository":{"id":39970763,"uuid":"451048636","full_name":"unpackAI/DL201","owner":"unpackAI","description":"Deep learning 201","archived":false,"fork":false,"pushed_at":"2023-01-13T05:56:57.000Z","size":100743,"stargazers_count":6,"open_issues_count":11,"forks_count":5,"subscribers_count":4,"default_branch":"main","last_synced_at":"2025-04-12T12:44:53.661Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"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/unpackAI.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}},"created_at":"2022-01-23T08:44:35.000Z","updated_at":"2023-01-19T15:27:56.000Z","dependencies_parsed_at":"2023-02-09T14:32:41.292Z","dependency_job_id":null,"html_url":"https://github.com/unpackAI/DL201","commit_stats":null,"previous_names":[],"tags_count":1,"template":false,"template_full_name":null,"purl":"pkg:github/unpackAI/DL201","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/unpackAI%2FDL201","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/unpackAI%2FDL201/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/unpackAI%2FDL201/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/unpackAI%2FDL201/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/unpackAI","download_url":"https://codeload.github.com/unpackAI/DL201/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/unpackAI%2FDL201/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":263640772,"owners_count":23493387,"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":[],"created_at":"2025-03-26T07:15:13.222Z","updated_at":"2025-07-05T00:36:17.003Z","avatar_url":"https://github.com/unpackAI.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# DL201\n# 4-Weeks Advanced Deep Learning Bootcamp from Unpack AI\n### Deep dive into working with data, data processing and building predictive models.\n\n🎉Congratulations on continuing your learning path to become a versatile AI Practitioner.\u003cbr/\u003e\nAt unpackAI, we are strong believers in a top-down approach when it comes to learning technical\nmulti-disciplinary AI skills in order to give a completed picture of the current state-of-the-art and\nhow you can apply it to your personal needs or work. You might have recently completed DL101\nand learned about machine learning use cases and built your first AI mini-projects. That was a\nreally good start to build up \"I can do it\" confidence and excitement about the possibilities of AI.\nAt the same time, you also encountered some limitations in exploring, buidling and validating\ngood AI projects, such as :\u003cbr/\u003e\n- data collection, data preparation and data quality issues\n- scarcity of computing power to train big models on Colab or Kaggle\n- and above all, your own limited coding skills and lack of strong technical foundation.\n\n🔥🔥🔥That's why we invite you to take another BIG leap forward by joining your next\nadventure in harnessing artificial intelligence: DL201 Bootcamp with deep dive into working\nwith data, data processing and building predictive models.\nLet's learn what the course is about :👇\n\n![Course Goals](https://raw.githubusercontent.com/unpackAI/DL201/main/img/Ai201%20concept%20map.png?raw=true)\n\n\n### 1. 🎳Goals\n\nBe able to build AI proof of a concept to demonstrate to a software developer what should\nbe done in a production environment. You will be able to connect the dots between the\nbusiness requirements and technical feasibility of the AI project.\n\n - Be able to work with the most common data types to load, analyze and prepare them for\nbuilding predictive models. You will be able to parse images (jpg, png etc), text (pdf, word\netc), web pages (html, json) and convert them into data frames, a required step before\nstarting your ML experimentation.\u003cbr/\u003e\u003cbr/\u003e \n - Be able to experiment with the most promising neural network architectures, traditional\nML algorithms and pretrained models to find out which one fits data best. You will able to\ncompare the results by understanding the mechanics of the model training and\nevaluation.\u003cbr/\u003e\u003cbr/\u003e\n - Be able to work with many python packages and ML and AutoML tools, e.g pandas,\nnumpy, pytorch, fast.ai, tai-chi, pycaret, unpackai and many more.\u003cbr/\u003e\u003cbr/\u003e\n - Get familiar with the everyday techniques and skills of machine learning engineers and software developers, such as \ncheck github repos for code, read documentation, google as a pro etc\u003cbr/\u003e\u003cbr/\u003e\n - Become a contributor to unpackai's AutoML packages: unpackai and tai-chi that simplifies\nthe code and accelerates the development cycles of AI models. Unlock the opportunity to\nbecome the mentor for DL101s to enable more business professionals to learn and apply\nAIML.\u003cbr/\u003e\n### 2. Schedule \n\n\u003ctable\u003e\n\u003ctbody\u003e\n\u003ctr class=\"odd\"\u003e\n\u003ctd\u003eWeek\u003c/td\u003e\n\u003ctd\u003eSkills\u003c/td\u003e\n\u003ctd\u003eLearning Content\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr class=\"even\"\u003e\n\u003ctd\u003e0\u003c/td\u003e\n\u003ctd\u003eCourse deliverables and project setup\u003c/td\u003e\n\u003ctd\u003e\u003col type=\"1\"\u003e\n\u003cli\u003e\u003cp\u003eMeet your mentors, unpackers\u003c/p\u003e\u003c/li\u003e\n\u003cli\u003e\u003cp\u003eUnderstand the bootcamp objectives and logistics\u003c/p\u003e\u003c/li\u003e\n\u003cli\u003e\u003cp\u003eReceive the framework for building your AI project in this\ncourse. Learn and build as you go over the first three weeks, finalize\nthe project by the end of 4th week.\u003c/p\u003e\u003c/li\u003e\n\u003c/ol\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr class=\"odd\"\u003e\n\u003ctd\u003e1\u003c/td\u003e\n\u003ctd\u003eData Loading and Exploratory Analysis\u003c/td\u003e\n\u003ctd\u003e\u003col type=\"1\"\u003e\n\u003cli\u003e\u003cp\u003eUnderstand how to load datasets and metadata in the most common\nfile formats\u003c/p\u003e\u003c/li\u003e\n\u003cli\u003e\u003cp\u003eExplore the datasets and discern if it is suitable for adaptation\nto our problem\u003c/p\u003e\u003c/li\u003e\n\u003cli\u003e\u003cp\u003eGain tools to manipulate metadata and tabular data using\nPandas\u003c/p\u003e\u003c/li\u003e\n\u003cli\u003e\u003cp\u003eHave an appreciation for how data can be represented as tensors\nExplore the data-centric approach in AI, and learn about its importance\nin Machine \u0026amp; Deep Learning.\u003c/p\u003e\u003c/li\u003e\n\u003cli\u003e\u003cp\u003eUtilize tools to label, improve and balance your\ndatasets.\u003c/p\u003e\u003c/li\u003e\n\u003c/ol\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr class=\"even\"\u003e\n\u003ctd\u003e2\u003c/td\u003e\n\u003ctd\u003eData Preprocessing and Transformations\u003c/td\u003e\n\u003ctd\u003e\u003col type=\"1\"\u003e\n\u003cli\u003e\u003cp\u003eExplore common data wrangling tasks\u003c/p\u003e\u003c/li\u003e\n\u003cli\u003e\u003cp\u003eLearn how to apply feature engineering methods to spreadsheet data, such as grouping into categories, feature decomposition, tabular data transformation methods\u003c/p\u003e\u003c/li\u003e\n\u003cli\u003e\u003cp\u003eMaster computer vision preprocessing techniques like label encoding, handling unbalanced classes; image data transformation like normalizing pixel values\u003c/p\u003e\u003c/li\u003e\n\u003cli\u003e\u003cp\u003eDive into text preprocessing for NLP tasks like encoding and embeddings\u003c/p\u003e\u003c/li\u003e\n\u003c/ol\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr class=\"odd\"\u003e\n\u003ctd\u003e3\u003c/td\u003e\n\u003ctd\u003eAlgorithms and Model Training\u003c/td\u003e\n\u003ctd\u003e\u003col type=\"1\"\u003e\n\u003cli\u003e\u003cp\u003eDiscuss the most common cutting edge DL algorithms and architectures in computer vision, NLP\u003c/p\u003e\u003c/li\u003e\n\u003cli\u003e\u003cp\u003eExplore the best performing machine learning models used in supervised machine learning for structured data\u003c/p\u003e\u003c/li\u003e\n\u003cli\u003e\u003cp\u003eLearn how to apply pretrained models on new ML tasks with hyperparameters optimization and most common fine-tuning approaches\u003c/p\u003e\u003c/li\u003e\n\u003cli\u003e\u003cp\u003eTrain Neural Network from Scratch using only Pytorch to undersand the mechanics of building ML model.\u003c/p\u003e\u003c/li\u003e\n\u003c/ol\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr class=\"even\"\u003e\n\u003ctd\u003e4\u003c/td\u003e\n\u003ctd\u003eProject Finalization\u003c/td\u003e\n\u003ctd\u003e\u003col type=\"1\"\u003e\n\u003cli\u003e\u003cp\u003eFully apply newly gained machine learning skills to your ongoing\nproject to deliver the final results.\u003c/p\u003e\u003c/li\u003e\n\u003cli\u003e\u003cp\u003eGet on 1:1 calls with mentors to get personalized feedback and\nrecommendations before presenting it as a proof-of-concept project on\nDemo Day.\u003c/p\u003e\u003c/li\u003e\n\u003c/ol\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr class=\"odd\"\u003e\n\u003ctd\u003e5\u003c/td\u003e\n\u003ctd\u003eGraduation and Demo Day\u003c/td\u003e\n\u003ctd\u003e\u003col type=\"1\"\u003e\n\u003cli\u003e\u003cp\u003ePresent your project in the final session, receive the final feedback from mentors in order to get yourself ready for Demo Day\u003c/p\u003e\u003c/li\u003e\n\u003cli\u003e\u003cp\u003eParticipate in Demo Day to showcase your achievement and feature your project in front of anyone. We will broadast this event on our social media to invite anyone interested in real AI use cases built by our graduates.\u003c/p\u003e\u003c/li\u003e\n\u003cli\u003e\u003cp\u003eReceive the certificate and get endorse for your skills on LinkedIn\u003c/p\u003e\u003c/li\u003e\n\u003c/ol\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Funpackai%2Fdl201","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Funpackai%2Fdl201","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Funpackai%2Fdl201/lists"}