{"id":17045345,"url":"https://github.com/adicherlavenkatasai/ml-workspace","last_synced_at":"2025-04-11T01:37:05.071Z","repository":{"id":89512670,"uuid":"278558373","full_name":"AdicherlaVenkataSai/ml-workspace","owner":"AdicherlaVenkataSai","description":"Machine Learning (Beginners Hub), information(courses, books, cheat sheets, live sessions) related to machine learning, data science and python is available","archived":false,"fork":false,"pushed_at":"2023-05-19T11:35:51.000Z","size":234,"stargazers_count":375,"open_issues_count":2,"forks_count":109,"subscribers_count":45,"default_branch":"master","last_synced_at":"2025-03-24T22:43:15.552Z","etag":null,"topics":["cheat-sheets","convolutional-networks","data-science","deep-learning","deep-neural-networks","gans","harvard-edx","interview-questions","machine-learning","python"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/AdicherlaVenkataSai.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.md","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2020-07-10T06:38:10.000Z","updated_at":"2025-03-23T09:45:32.000Z","dependencies_parsed_at":"2023-10-20T16:20:52.948Z","dependency_job_id":null,"html_url":"https://github.com/AdicherlaVenkataSai/ml-workspace","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/AdicherlaVenkataSai%2Fml-workspace","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AdicherlaVenkataSai%2Fml-workspace/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AdicherlaVenkataSai%2Fml-workspace/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AdicherlaVenkataSai%2Fml-workspace/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/AdicherlaVenkataSai","download_url":"https://codeload.github.com/AdicherlaVenkataSai/ml-workspace/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248326233,"owners_count":21085067,"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":["cheat-sheets","convolutional-networks","data-science","deep-learning","deep-neural-networks","gans","harvard-edx","interview-questions","machine-learning","python"],"created_at":"2024-10-14T09:37:08.549Z","updated_at":"2025-04-11T01:37:05.049Z","avatar_url":"https://github.com/AdicherlaVenkataSai.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# One Stop for machine learning\n\n\t\n\u003c!--\n1. [Papers](https://github.com/AdicherlaVenkataSai/ml-workspace/blame/master/README.md#L35)\t\t\n2. [Languages](https://github.com/AdicherlaVenkataSai/ml-workspace/blame/master/README.md#L69)\t\t\n3. [Platforms](https://github.com/AdicherlaVenkataSai/ml-workspace/blame/master/README.md#L79)\t\t\n4. [Courses](https://github.com/AdicherlaVenkataSai/ml-workspace/blame/master/README.md#L94)\t\t\n5. [Live Sessions](https://github.com/AdicherlaVenkataSai/ml-workspace/blame/master/README.md#L157)\t\t\n6. [Books](https://github.com/AdicherlaVenkataSai/ml-workspace/blame/master/README.md#L164)\t\t\n7. [Cheat sheets](https://github.com/AdicherlaVenkataSai/ml-workspace/blame/master/README.md#L176)\t\t\n8. [Interview Questions](https://github.com/AdicherlaVenkataSai/ml-workspace/blame/master/README.md#L200)\t\t\n9. [DataSets](https://github.com/AdicherlaVenkataSai/ml-workspace/blame/master/README.md#L209)\t\t\n10. [Blogs and Community](https://github.com/AdicherlaVenkataSai/ml-workspace/blame/master/README.md#L221)\t\t\n11. [Online Competitions](https://github.com/AdicherlaVenkataSai/ml-workspace/blame/master/README.md#L230)\t\t\n12. [Other Sources](https://github.com/AdicherlaVenkataSai/ml-workspace/blame/master/README.md#L239)\t\t\n13. [About CNNs](https://github.com/AdicherlaVenkataSai/ml-workspace/blame/master/README.md#L282)\t\t\n14. [About GANs](https://github.com/AdicherlaVenkataSai/ml-workspace/blame/master/README.md#L318)\n--\u003e\n\n**Sections:**\t\t\n1. Papers\t\t\n2. Languages\t\n3. Platforms\t\n4. Courses\t\t\n5. Live Sessions\t\t\n6. Books\t\t\n7. Cheat sheets\t\t\n8. Interview Questions\t\t\n9. DataSets\t\n10. Blogs and Community\t\t\n11. Online Competitions\t\t\n12. Other Sources\n13. About CNNs\t\t\n14. About GANs\n\n\n**Steps in approaching a Machine learning problem:**      \nBelow are the steps that I follow while approaching a ML problem.    \n-  Defining and understanding the problem statement    \n-  Gathering the Data    \n-  Initial Exploration of Data    \n-  In-depth EDA    \n-  Building the model    \n-  Analyzing the results with different models and shortlisting the ones which gives good performance measures     \n-  Fine-tuning the selected model    \n-  Document the code     \n-  Deployment      \n-  Monitoring the deployed model performance in real time.\n\n\n\n## Papers | 2020 \n\u003c!--Natural Language Processing --\u003e\n-  [Towards a Human-like Open-Domain Chatbot](https://arxiv.org/abs/2001.09977)\n-  [ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators](https://arxiv.org/abs/2003.10555)\n-  [Reformer: The Efficient Transformer](https://arxiv.org/abs/2001.04451)\n-  [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150)\n-  [You Impress Me: Dialogue Generation via Mutual Persona Perception](https://arxiv.org/abs/2004.05388)\n-  [Recipes for building an open-domain chatbot](https://arxiv.org/abs/2004.13637)\n-  [ToD-BERT: Pre-trained Natural Language Understanding for Task-Oriented Dialogues](https://arxiv.org/abs/2004.06871)\n-  [SOLOIST: Few-shot Task-Oriented Dialog with A Single Pre-trained Auto-regressive Model](https://arxiv.org/abs/2005.05298)\n-  [A Simple Language Model for Task-Oriented Dialogue](https://arxiv.org/abs/2005.00796)\n-  [FastBERT: a Self-distilling BERT with Adaptive Inference Time](https://arxiv.org/abs/2004.02178)\n-  [PoWER-BERT: Accelerating BERT Inference via Progressive Word-vector Elimination](https://arxiv.org/abs/2001.08950)\n-  [Data Augmentation using Pre-trained Transformer Models](https://arxiv.org/abs/2003.02245)\n-  [FLAT: Chinese NER Using Flat-Lattice Transformer](https://arxiv.org/abs/2004.11795)\n\n\u003c!--Object Detection --\u003e\n-  [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872) | [code](https://github.com/facebookresearch/detr)\n-  [Objects as Points](https://arxiv.org/abs/1904.07850) | [code](https://github.com/xingyizhou/CenterNet)\n-  [Acquisition of Localization Confidence for Accurate Object Detection](https://arxiv.org/abs/1807.11590) | [code](https://github.com/vacancy/PreciseRoIPooling)\n-  [Part-A^2 Net: 3D Part-Aware and Aggregation Neural Network for Object Detection from Point Cloud](https://arxiv.org/abs/1907.03670)\n-  [PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud](https://arxiv.org/abs/1812.04244) | [code](https://github.com/sshaoshuai/PointRCNN)\n-  [LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving](https://arxiv.org/abs/1903.08701)\n-  [Probabilistic 3D Multi-Object Tracking for Autonomous Driving](https://arxiv.org/abs/2001.05673) | [code](https://github.com/eddyhkchiu/mahalanobis_3d_multi_object_tracking)\n-  [A Baseline for 3D Multi-Object Tracking](https://arxiv.org/abs/1907.03961) | [code](https://github.com/xinshuoweng/AB3DMOT)\n\u003c!--other --\u003e\n-  [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239) | [code](https://github.com/hojonathanho/diffusion)\n-  [Joint Training of Variational Auto-Encoder and Latent Energy-Based Model](https://arxiv.org/abs/2006.06059)\n-  [Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors](https://arxiv.org/abs/2005.07186) | [code](https://github.com/google/edward2)\n-  [BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning](https://arxiv.org/abs/2002.06715) | [code](https://github.com/google/edward2)\n-  [Stable Neural Flows](https://arxiv.org/abs/2003.08063)\n-  [How Good is the Bayes Posterior in Deep Neural Networks Really](https://arxiv.org/abs/2002.02405)\n\n\n## Languages      \nKnowing one language couldn't help to work in multidomain,so its good to learn two or more languages like `python`, `c++`, `java`. For machine learning,one should be sound in any of the language with other language basics `python`, `R`, `matlab`, `julia`.\n-  [Jet Brains](https://hyperskill.org/curriculum)    \nJet Brains provides basics to advance training on `Java`, `Koltin` and `Python` languages, can learn and apply in developing mini projects based on the level selected.    \n-  [HackerRank](https://www.hackerrank.com/dashboard)    \n-  [HackerEarth](https://www.hackerearth.com/practice/)\n-  [LeetCode](https://leetcode.com/explore/)\n-  [CodeChef](https://www.codechef.com/)\n-  [CP algorithms](https://cp-algorithms.com/)\n\n## Platforms      \nAs we know that machine(deep) learning models need high computational power, it might not be possible for an individual for purchase addtional `CPUs` or `GPUs`, so we use cloud platforms.\n-  [Jupyter Notebook](https://docs.anaconda.com/anaconda/install/) \n\n**Note:**  In general we use `Jupyter Notebook` to run our models. For Jupyter Notebook we need to install the software on local machine, for [windows](https://docs.anaconda.com/anaconda/install/), [linux](https://jupyter.org/install)\n-  [colab](https://colab.research.google.com/notebooks/)\n-  [kaggle](https://www.kaggle.com/)        \n-  [paperspace](https://www.paperspace.com/)\n-  [aws](https://aws.amazon.com/)  \n-  [gcp](https://cloud.google.com/)  \n-  [crestle](https://crestle.ai/)  \n-  [floydhub](https://www.floydhub.com/)\n\n**Note:**  Google Products like Colab, Kaggle are free and best platform for an individual.\n\n## Courses      \nMost of the courses are available in `Python` language. kindly re-check before taking the course of you have any language constraints.      \n**Note:** No organisation can be seen here, kindly pick the course based on your requirement.\n\n**Machine Learning**\t\t\n-  [Machine Learning (Stanford CS229)](https://www.youtube.com/playlist?list=PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU)\n-  [Python for Machine Learning (Great Learning)](https://olympus.greatlearning.in/courses/10899) \n-  [Statistics for Machine Learrning (Great Learning)](https://www.greatlearning.in/academy/learn-for-free/courses/statistics-for-machine-learning)     \n-  [Data Visualization using Python (Great Learning)](https://www.greatlearning.in/academy/learn-for-free/courses/data-visualization-using-python)      \n-  [Machine Learning Crash Course (Google)](https://developers.google.com/machine-learning/crash-course)    \n-  [Machine Learning - Linear Regression (LEAPS)](https://leapsapp.analyttica.com/courses/overview/Predictive-Modelling-Linear-Regression)     \n-  [Getting started with Decision Trees (Analytics Vidhya)](https://courses.analyticsvidhya.com/courses/getting-started-with-decision-trees)      \n-  [Machine Learning with Python: A Practical Introduction (Harvard EdX)](https://www.edx.org/course/machine-learning-with-python-a-practical-introduct)      \n-  [Machine Learning Fundamentals (Harvard EdX)](https://www.edx.org/course/machine-learning-fundamentals-2)     \n-  [Machine Learning with Python: from Linear Models to Deep Learning (Harvard EdX)](https://www.edx.org/course/machine-learning-with-python-from-linear-models-to)      \n-  [Machine Learning (Harvard EdX)](https://online-learning.harvard.edu/course/data-science-machine-learning)    \n-  [Machine Learning with Python (IBM)](https://cognitiveclass.ai/courses/machine-learning-with-python)    \n-  [Machine Learning – Dimensionality Reduction (IBM)](https://cognitiveclass.ai/courses/machine-learning-dimensionality-reduction)    \n-  [Data Visualization with Python (IBM)](https://cognitiveclass.ai/courses/data-visualization-with-python)  \n-  [Data Analysis with Python (IBM)](https://cognitiveclass.ai/courses/data-analysis-python)\n-  [Applied Machine Learning 2020 (Columbia)](https://www.youtube.com/playlist?list=PL_pVmAaAnxIRnSw6wiCpSvshFyCREZmlM)\n\n\n**DataScience**\n-  [Introduction to Python (Analytics Vidhya)](https://courses.analyticsvidhya.com/courses/introduction-to-data-science)     \n-  [Fundamentals of Data Analytics (LEAPS)](https://leapsapp.analyttica.com/courses/overview/Fundamentals-of-Data-Analytics)    \n-  [Pandas for Data Analysis in Python (Analytics Vidhya)](https://courses.analyticsvidhya.com/courses/pandas-for-data-analysis-in-python)    \n-  [Tableau for Beginners (Analytics Vidhya)](https://courses.analyticsvidhya.com/courses/pandas-for-data-analysis-in-python)     \n-  [Top Data Science Projects for Analysts and Data Scientists (Analytics Vidhya)](https://courses.analyticsvidhya.com/courses/take/top-data-science-projects-for-analysts-and-data-scientists/texts/13945900-about-the-data-science-projects-course)      \n-  [Machine Learning for Data Science and Analytics (Harvard EdX)](https://www.edx.org/course/machine-learning-for-data-science-and-analytics)    \n-  [A data science program for everyone (Harvard EdX)](https://www.edx.org/professional-certificate/berkeleyx-foundations-of-data-science)     \n-  [Data Science and Machine Learning Capstone Project (Harvard EdX)](https://www.edx.org/course/data-science-and-machine-learning-capstone-project)\n-  [R Basics (Harvard EdX)](https://www.edx.org/course/data-science-r-basics)      \n-  [Data Visualisation and EDA (Harvard EdX)](https://www.edx.org/course/data-science-visualization)    \n-  [Probability Theory (Harvard EdX)](https://www.edx.org/course/data-science-probability)     \n-  [Data Science Inference and Modeling (Harvard EdX)](https://www.edx.org/course/data-science-inference-and-modeling)    \n-  [Data Science Productivity Tools (Harvard EdX)](https://www.edx.org/course/data-science-productivity-tools)      \n-  [Data Science Wrangling (Harvard EdX)](https://www.edx.org/course/data-science-wrangling)      \n-  [Data Science Linear Regression (Harvard EdX)](https://www.edx.org/course/data-science-machine-learning)      \n-  [Capstone Project (Harvard EdX)](https://www.edx.org/course/data-science-capstone)    \n-  [Introduction to Data Science (IBM)](https://cognitiveclass.ai/courses/data-science-101)\n-  [Python for Data Science (IBM)](https://cognitiveclass.ai/courses/python-for-data-science)      \n-  [SQL and Relational Databases 101 (IBM)](https://cognitiveclass.ai/courses/learn-sql-relational-databases)\n\n**APIs**\n-  [Deep Learning Fundamentals (IBM)](https://cognitiveclass.ai/courses/introduction-deep-learning)      \n-  [Deep Learning with TensorFlow (IBM)](https://cognitiveclass.ai/courses/deep-learning-tensorflow)     \n-  [Accelerating Deep Learning with GPU (IBM)](https://cognitiveclass.ai/courses/accelerating-deep-learning-gpu)\n-  [Convolutional Neural Networks for Visual Recognition (Stanford CS231n)](https://www.youtube.com/playlist?list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv)\n\n**Artificial Intelligence**\n-  [Introduction to Artificial Intelligence with Python (Harvard EdX)](https://www.edx.org/course/cs50s-introduction-to-artificial-intelligence-with-python)\n-  [Introduction to Artificial Intelligence (UC Berkeley CS188)](https://www.youtube.com/playlist?list=PL7k0r4t5c108AZRwfW-FhnkZ0sCKBChLH)\n\n\n**Natural Language Processing**\n-  [Introduction to Natural Language Processing](https://courses.analyticsvidhya.com/courses/Intro-to-NLP)      \n-  [Natural Language Processing (NLP) - Microsoft](https://www.edx.org/course/natural-language-processing-nlp-2)\n-  [Natural Language Processing with Deep Learning (Stanford CS224N)](https://www.youtube.com/playlist?list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z)\n-  [Natural Language Understanding (Stanford CS224U)](https://www.youtube.com/playlist?list=PLoROMvodv4rObpMCir6rNNUlFAn56Js20)\n\n**Commputer Vision**\n-  [Computer Vision Fundamentals with Watson and OpenCV](https://www.edx.org/course/computer-vision-fundamentals)     \n-  [Computer Vision and Image Analysis - Microsoft](https://www.edx.org/course/computer-vision-and-image-analysis-2)\n\n**Matlab**\n-  [Matlab Onramp](https://matlabacademy.mathworks.com/R2020a/portal.html?course=gettingstarted)      \n-  [Machine learning Onramp](https://matlabacademy.mathworks.com/R2020a/portal.html?course=machinelearning)     \n-  [Deep learning Onramp](https://matlabacademy.mathworks.com/R2020a/portal.html?course=deeplearning) \n\n\n## Live Sessions\n-  [source 1](https://www.greatlearning.in/academy/learn-for-free/live-sessions)     \n-  [source 2](https://datahack.analyticsvidhya.com/events/)\n\n\n**Note: In the below available sections if the required book/cheatsheet is not present or for more info check the `more` .**  \n**Note: Few google links are getting crashing for few files, in such scenario, can direclty check `more` (folder) .**\n## EBooks/Books/CookBooks\n-  [What is Machine Learning](https://courses.analyticsvidhya.com/courses/ebook-machine-learning)     \n-  [Tree Based Algorithms](https://courses.analyticsvidhya.com/courses/ebook-tree-based-algorithm)      \n-  [Natural Language Processing](https://courses.analyticsvidhya.com/courses/ebook-nlp)      \n-  [Data Cleaning with Numpy and Pandas](https://drive.google.com/file/d/1pk1oO-bWFf9L02Z0W90LTTDDh1djSXDQ/view?usp=sharing)     \n-  [Data Engineering CookBook](https://drive.google.com/file/d/1qbEwEdQw-cB75ba1aFvQMJKzayso7GA7/view?usp=sharing)      \n-  [Machine Learning Projects in Python (Compiled)](https://drive.google.com/file/d/1AKUHhlz3T1uCSAvfxTTzI-I9_8E5y3eK/view?usp=sharing)\t\t\n-  [Natural Langugae with Python](https://drive.google.com/file/d/1DFriuH98MuJUIw48kMWjGae7wIibXYCt/view?usp=sharing)\t\t\n\n[more...](https://drive.google.com/drive/folders/1AtyaE8tYIz-COEbVGWWnXvggLshrU7TV?usp=sharing)\t\t    \n   \n   \n## Cheat Sheets\n-  [Python](https://drive.google.com/file/d/1yEg3Fj9Kz-axHfWUq6oQ0I6C6pKVTjua/view?usp=sharing)    \n-  [R](https://drive.google.com/file/d/1CTDB8XIbaGRY1SeQtJ0ZCou0kf4xfqvf/view?usp=sharing)      \n-  [SQL](https://drive.google.com/file/d/1VDx0iEg5PlSf_1Pzf1TRjjjq8d5Vp1D5/view?usp=sharing)    \n-  [Machine Learning](https://drive.google.com/file/d/1ZDLhNE9IJ2kW38vrdyzdX_kB13lzf0u7/view?usp=sharing)      \n-  [Supervised learning](https://drive.google.com/file/d/1qrJd04b1c2n00Xbb-9x6myJZHGiL-aBX/view?usp=sharing)      \n-  [Data Science](https://drive.google.com/file/d/1mxtQ_2JDfINiUHOPb-5ZM0g-ZQIu2c06/view?usp=sharing)    \n-  [Probability](https://drive.google.com/file/d/1bxAsXUrOQKA5PRcNy8TDjk1UJhtq_UNd/view?usp=sharing)\n-  [Statistics](https://drive.google.com/file/d/1_U6mDRVkR0dp4VgZNQoainsf8_5WblDa/view?usp=sharing)      \n-  [Data Engineering](https://drive.google.com/file/d/16atXb3Kz_zsUi2uYSu_sIH0n5CCNXmzV/view?usp=sharing)      \n-  [Git](https://drive.google.com/file/d/1A2mBL5dVuoWM7vlRJLNqJlFKqVGzxEC3/view?usp=sharing)    \n   \n[more...](https://drive.google.com/drive/folders/1kX_0frKyzmdpj7PXefTIMuZoKDKBlmtU?usp=sharing)\n   \n   \n\n## Exercises\n-  [Python](https://drive.google.com/file/d/1g4wssDtS6NxE66SUkXoZVJzAJV3RnZH7/view?usp=sharing)     \n-  [SQL](https://drive.google.com/file/d/1ZA2ouiOS9TaG-yev3uAFsrBHiAJ2p6jT/view?usp=sharing)\n-  [Matplotlib](https://drive.google.com/file/d/1LtTJrTWANziMkfJ6SLQuSLdbEpk9Es7S/view?usp=sharing)\n   \n[more...](https://drive.google.com/drive/folders/1wVFPGUaBX_snFskIXOg0av18Jvsd-rBz?usp=sharing)\n   \n\n## Interview Questions\n-  [Python](https://drive.google.com/file/d/1byBSx4oFvS3hx3OobDGjD3FCwzJlTZM1/view?usp=sharing)    \n-  [Data Science](https://drive.google.com/file/d/1Ufi-eB5m2OQyiXb62aTiUbdBdRxpnT0I/view?usp=sharing)    \n-  [Variance in Data Science](https://drive.google.com/file/d/1f6DHYVWHJUxhClblwOvGQ0Cj5BGVYwET/view?usp=sharing)\n-  [Interview question data science](https://github.com/iNeuronai/interview-question-data-science-)\n-  [Self prepared Interview Questions]()\n\n[more...](https://drive.google.com/drive/folders/1B1WYiKPH3-vYW-bxQA98Ncw4Cf_2eM2m?usp=sharing)\n\nNote: These Self prepared Interview Questions will be updated weekly.\n\n\n## DataSets\n-  [Google](https://datasetsearch.research.google.com/)\n-  [Kaggle](https://www.kaggle.com/datasets)\n-  [Meta-Sim: Learning to Generate Synthetic Datasets](https://github.com/nv-tlabs/meta-sim)\n-  [UCI](https://archive.ics.uci.edu/ml/index.php)\n-  [Worldbank Data](https://github.com/mwouts/world_bank_data)\n-  [Tensorflow](https://www.tensorflow.org/datasets)\n-  [Movielens](https://grouplens.org/datasets/movielens/)\n-  [Reddit](https://www.reddit.com/r/datasets/)\n-  [Huggingface](https://huggingface.co/datasets)\n\n\n## Blogs and Community\n-  [Towards data science](https://towardsdatascience.com/)\n-  [Analyticsvidhya](https://www.analyticsvidhya.com/blog/?utm_source=feed\u0026utm_medium=navbar)\n-  [Machinelearningmastery](https://machinelearningmastery.com/blog/)\n-  [Medium](https://medium.com/)\n-  [PyTorch Discussion Forum](https://discuss.pytorch.org/)\n-  [StackOverflow PyTorch Tags](http://stackoverflow.com/questions/tagged/pytorch)\n-  [Website](https://www.ritchieng.com/the-incredible-pytorch/)\n## Online Competitions\n-  [Kaggle](https//www.kaggle.com/)\n-  [AnalyticsVidhya](https://datahack.analyticsvidhya.com/contest/all/)\n-  [Hackerearth](https://www.hackerearth.com/challenges/)\n-  [Crowdai](https://www.crowdai.org/)\n-  [Machinehack](https://www.machinehack.com/)\n-  [Drivendata](https://www.drivendata.org/)\n-  [Zindi](https://zindi.africa/competitions)\n\n## Other Resources\n-  [Paperswithcode](https://paperswithcode.com/methods)\n-  [Google research](https://github.com/google-research/google-research)\n-  [Madewithml](https://madewithml.com/topics/)\n-  [Deeplearning](https://course.fullstackdeeplearning.com/#course-content)\n-  [Huggingface](https://github.com/huggingface)\n-  [Arcgis](https://github.com/Esri/arcgis-python-api)\n-  [Pytorchdeeplearning](https://atcold.github.io/pytorch-Deep-Learning/)\n-  [Fastaibook](https://github.com/fastai/fastbook)\n-  [EasyOCR](https://github.com/JaidedAI/EasyOCR)\n-  [Awesome Data Science](https://github.com/academic/awesome-datascience)\n-  [NLP progress](https://github.com/sebastianruder/NLP-progress)\n-  [Recommenders](https://github.com/microsoft/recommenders)\n-  [Awesome pytorch list](https://github.com/bharathgs/Awesome-pytorch-list)\n-  [Free data science books](https://github.com/chaconnewu/free-data-science-books)\n-  [Deeplearningdrizzle](https://deep-learning-drizzle.github.io/)\n-  [Data science ipython notebooks](https://github.com/donnemartin/data-science-ipython-notebooks)\n-  [Julia](https://github.com/JuliaLang/julia)\n-  [Gym](https://github.com/openai/gym)\n-  [Regarding satellite images](https://www.esri.com/en-us/arcgis/about-arcgis/overview)\n-  [Monk Object Detection](https://github.com/Tessellate-Imaging/Monk_Object_Detection)\n-  [Awesome pytorch list](https://github.com/bharathgs/Awesome-pytorch-list)\n-  [Topdeeplearning](https://github.com/aymericdamien/TopDeepLearning)\n-  [NLPprogress](https://github.com/sebastianruder/NLP-progress)\n-  [Multi task NLP](https://github.com/hellohaptik/multi-task-NLP)\n-  [Opencv](https://github.com/opencv/opencv)\n-  [Transformers](https://github.com/huggingface/transformers)\n-  [Code implementations for research papers](https://chrome.google.com/webstore/detail/find-code-for-research-pa/aikkeehnlfpamidigaffhfmgbkdeheil)\n-  [Tool for visualizing attention in the Transformer model](https://github.com/jessevig/bertviz)\n-  [Powerful and efficient Computer Vision Annotation Tool (CVAT)](https://github.com/openvinotoolkit/cvat)\n-  [Powerful and efficient Computer Vision Annotation Tool (CVAT)](https://github.com/abreheret/PixelAnnotationTool)\n-  [TransCoder](https://github.com/facebookresearch/TransCoder)\n-  [Super Duper NLP](https://notebooks.quantumstat.com/)\n-  [Tessellate Imaging](https://github.com/Tessellate-Imaging/monk_v1)\n-  [Machine Learning with Python](https://github.com/tirthajyoti/Machine-Learning-with-Python)\n-  [GPT 2](https://github.com/openai/gpt-2)\n-  [Data augmentation for NLP](https://github.com/makcedward/nlpaug)\n-  [Mlops](https://github.com/visenger/awesome-mlops)\n-  [Reinforcement learning](https://github.com/dennybritz/reinforcement-learning)\n-  [keras applications](https://github.com/keras-team/keras-applications)\n\n\n\n\n## About CNNs\n-  [LegoNet: Efficient Convolutional Neural Networks with Lego Filters](https://github.com/huawei-noah/LegoNet)\n-  [MeshCNN, a convolutional neural network designed specifically for triangular meshes](https://github.com/ranahanocka/MeshCNN)\n-  [Octave Convolution](https://github.com/d-li14/octconv.pytorch)\n-  [PyTorch Image Models, ResNet/ResNeXT, DPN, MobileNet-V3/V2/V1, MNASNet, Single-Path NAS, FBNet](https://github.com/rwightman/pytorch-image-models)\n-  [Deep Neural Networks with Box Convolutions](https://github.com/shrubb/box-convolutions)\n-  [Invertible Residual Networks](https://github.com/jarrelscy/iResnet)\n-  [Stochastic Downsampling for Cost-Adjustable Inference and Improved Regularization in Convolutional Networks ](https://github.com/xternalz/SDPoint)\n-  [Faster Faster R-CNN Implementation](https://github.com/jwyang/faster-rcnn.pytorch)\n-  [Faster R-CNN Another Implementation](https://github.com/longcw/faster_rcnn_pytorch)\n-  [Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer](https://github.com/szagoruyko/attention-transfer)\n-  [Wide ResNet model in PyTorch](https://github.com/szagoruyko/functional-zoo)\n-  [DiracNets: Training Very Deep Neural Networks Without Skip-Connections](https://github.com/szagoruyko/diracnets)\n-  [An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition](https://github.com/bgshih/crnn)\n-  [Efficient Densenet](https://github.com/gpleiss/efficient_densenet_pytorch)\n-  [Video Frame Interpolation via Adaptive Separable Convolution](https://github.com/sniklaus/pytorch-sepconv)\n-  [Learning local feature descriptors with triplets and shallow convolutional neural networks](https://github.com/edgarriba/examples/tree/master/triplet)\n-  [Densely Connected Convolutional Networks](https://github.com/bamos/densenet.pytorch)\n-  [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://github.com/jcjohnson/pytorch-vgg)\n-  [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and \\\u003c0.5MB model size](https://github.com/gsp-27/pytorch_Squeezenet)\n-  [Deep Residual Learning for Image Recognition](https://github.com/szagoruyko/functional-zoo)\n-  [Training Wide ResNets for CIFAR-10 and CIFAR-100 in PyTorch](https://github.com/xternalz/WideResNet-pytorch)\n-  [Deformable Convolutional Network](https://github.com/oeway/pytorch-deform-conv)\n-  [Convolutional Neural Fabrics](https://github.com/vabh/convolutional-neural-fabrics)\n-  [Deformable Convolutional Networks in PyTorch](https://github.com/1zb/deformable-convolution-pytorch)\n-  [Dilated ResNet combination with Dilated Convolutions](https://github.com/fyu/drn)\n-  [Striving for Simplicity: The All Convolutional Net](https://github.com/utkuozbulak/pytorch-cnn-visualizations)\n-  [Convolutional LSTM Network](https://github.com/automan000/Convolution_LSTM_pytorch)\n-  [Big collection of pretrained classification models](https://github.com/osmr/imgclsmob)\n-  [PyTorch Image Classification with Kaggle Dogs vs Cats Dataset](https://github.com/rdcolema/pytorch-image-classification)\n-  [CIFAR-10 on Pytorch with VGG, ResNet and DenseNet](https://github.com/kuangliu/pytorch-cifar)\n-  [Base pretrained models and datasets in pytorch (MNIST, SVHN, CIFAR10, CIFAR100, STL10, AlexNet, VGG16, VGG19, ResNet, Inception, SqueezeNet)](https://github.com/aaron-xichen/pytorch-playground)\n-  [NVIDIA/unsupervised-video-interpolation](https://github.com/NVIDIA/unsupervised-video-interpolation)\n\n\n\n## About GANs\n-  [Mimicry, PyTorch Library for Reproducibility of GAN Research](https://github.com/kwotsin/mimicry)\n-  [Clean Readable CycleGAN](https://github.com/aitorzip/PyTorch-CycleGAN)\n-  [StarGAN](https://github.com/yunjey/stargan)\n-  [Block Neural Autoregressive Flow](https://github.com/nicola-decao/BNAF)\n-  [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs](https://github.com/NVIDIA/pix2pixHD)\n-  [A Style-Based Generator Architecture for Generative Adversarial Networks](https://github.com/rosinality/style-based-gan-pytorch)\n-  [GANDissect, PyTorch Tool for Visualizing Neurons in GANs](https://github.com/CSAILVision/gandissect)\n-  [Learning deep representations by mutual information estimation and maximization](https://github.com/DuaneNielsen/DeepInfomaxPytorch)\n-  [Variational Laplace Autoencoders](https://github.com/yookoon/VLAE)\n-  [VeGANS, library for easily training GANs](https://github.com/unit8co/vegans)\n-  [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://github.com/github-pengge/PyTorch-progressive_growing_of_gans)\n-  [Conditional GAN](https://github.com/kmualim/CGAN-Pytorch/)\n-  [Wasserstein GAN](https://github.com/martinarjovsky/WassersteinGAN)\n-  [Adversarial Generator-Encoder Network](https://github.com/DmitryUlyanov/AGE)\n-  [Image-to-Image Translation with Conditional Adversarial Networks](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix)\n-  [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix)\n-  [On the Effects of Batch and Weight Normalization in Generative Adversarial Networks](https://github.com/stormraiser/GAN-weight-norm)\n-  [Improved Training of Wasserstein GANs](https://github.com/jalola/improved-wgan-pytorch)\n-  [Collection of Generative Models with PyTorch](https://github.com/wiseodd/generative-models)\n\t- Generative Adversarial Nets (GAN)\n\t\t1. [Vanilla GAN](https://arxiv.org/abs/1406.2661)\n\t\t2. [Conditional GAN](https://arxiv.org/abs/1411.1784)\n\t\t3. [InfoGAN](https://arxiv.org/abs/1606.03657)\n\t\t4. [Wasserstein GAN](https://arxiv.org/abs/1701.07875)\n\t\t5. [Mode Regularized GAN](https://arxiv.org/abs/1612.02136)\n\t- Variational Autoencoder (VAE)\n\t\t1. [Vanilla VAE](https://arxiv.org/abs/1312.6114)\n\t\t2. [Conditional VAE](https://arxiv.org/abs/1406.5298)\n\t\t3. [Denoising VAE](https://arxiv.org/abs/1511.06406)\n\t\t4. [Adversarial Autoencoder](https://arxiv.org/abs/1511.05644)\n\t\t5. [Adversarial Variational Bayes](https://arxiv.org/abs/1701.04722)\n-  [Improved Training of Wasserstein GANs](https://github.com/caogang/wgan-gp)\n-  [CycleGAN and Semi-Supervised GAN](https://github.com/yunjey/mnist-svhn-transfer)\n-  [Improving Variational Auto-Encoders using Householder Flow and using convex combination linear Inverse Autoregressive Flow](https://github.com/jmtomczak/vae_vpflows)\n-  [PyTorch GAN Collection](https://github.com/znxlwm/pytorch-generative-model-collections)\n-  [Generative Adversarial Networks, focusing on anime face drawing](https://github.com/jayleicn/animeGAN)\n-  [Simple Generative Adversarial Networks](https://github.com/mailmahee/pytorch-generative-adversarial-networks)\n-  [Adversarial Auto-encoders](https://github.com/fducau/AAE_pytorch)\n-  [torchgan: Framework for modelling Generative Adversarial Networks in Pytorch](https://github.com/torchgan/torchgan)\n\n\n\n**Thank You for visiting the repo, hope it helped you!**\n**I would like to hear suggestions from you!!**\n\n**Can reach me at :** \n[![Linkedin: venkatasaiadicherla](https://img.shields.io/badge/-venkatasaiadicherla-blue?style=flat-square\u0026logo=Linkedin\u0026logoColor=white\u0026link=https://www.linkedin.com/in/adicherlavenkatasai/)](https://www.linkedin.com/in/adicherlavenkatasai/)\t\t\n\u003c!-- [![Whatsapp: venkatasaiadicherla](https://img.shields.io/badge/-venkatasaiadicherla-%2325D366.svg?\u0026flat-square\u0026logo=whatsapp\u0026logoColor=white\u0026link=https://wa.me/+918008527755)](https://wa.me/+918008527755)\n--\u003e\n \n\n\n\n\n\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fadicherlavenkatasai%2Fml-workspace","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fadicherlavenkatasai%2Fml-workspace","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fadicherlavenkatasai%2Fml-workspace/lists"}