{"id":14958742,"url":"https://github.com/farshidnooshi/tensorflow-notebooks","last_synced_at":"2025-10-24T16:30:23.143Z","repository":{"id":43785106,"uuid":"502670000","full_name":"FarshidNooshi/TensorFlow-Notebooks","owner":"FarshidNooshi","description":"A collection of notebooks with TensorFlow and the Keras API for various deep-learning and machine learning problems","archived":false,"fork":false,"pushed_at":"2023-06-16T09:46:55.000Z","size":4535,"stargazers_count":29,"open_issues_count":5,"forks_count":3,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-01-31T02:37:19.183Z","etag":null,"topics":["cnn-keras","deep-learning","keras","lstm","machine-learning","neural-network","nlp-machine-learning","rnn","rnn-tensorflow","tensorflow","tensorflow-examples","tensorflow-tutorials"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/FarshidNooshi.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","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":"2022-06-12T16:31:19.000Z","updated_at":"2024-09-24T01:05:23.000Z","dependencies_parsed_at":"2024-09-24T13:28:56.691Z","dependency_job_id":"57ff84a2-4ae6-4e30-a31d-8dfc5eaa5655","html_url":"https://github.com/FarshidNooshi/TensorFlow-Notebooks","commit_stats":{"total_commits":151,"total_committers":1,"mean_commits":151.0,"dds":0.0,"last_synced_commit":"5e76e5f6e98a077dd45012438f65c8b81a85ffd2"},"previous_names":[],"tags_count":2,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/FarshidNooshi%2FTensorFlow-Notebooks","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/FarshidNooshi%2FTensorFlow-Notebooks/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/FarshidNooshi%2FTensorFlow-Notebooks/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/FarshidNooshi%2FTensorFlow-Notebooks/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/FarshidNooshi","download_url":"https://codeload.github.com/FarshidNooshi/TensorFlow-Notebooks/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":237999434,"owners_count":19399880,"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":["cnn-keras","deep-learning","keras","lstm","machine-learning","neural-network","nlp-machine-learning","rnn","rnn-tensorflow","tensorflow","tensorflow-examples","tensorflow-tutorials"],"created_at":"2024-09-24T13:18:11.457Z","updated_at":"2025-10-24T16:30:22.573Z","avatar_url":"https://github.com/FarshidNooshi.png","language":"Jupyter Notebook","readme":"\u003cdiv align=\"center\"\u003e\n  \u003ch1\u003e In The Name Of GOD \u003c/h11\u003e\n\u003c/div\u003e\n  \n  \n[![CI](https://github.com/FarshidNooshi/TensorFlow-Notebooks/actions/workflows/action.yml/badge.svg)](https://github.com/FarshidNooshi/TensorFlow-Notebooks/actions/workflows/action.yml)\n\n# TensorFlow Notebooks\n\nThis repository hosts my extra works and projects in the field of Machine Learning and deep-learning problems with the **TensorFlow platform**. the repository contains several folders in which each of them is for an specific course(or specialization) or project.\n\n## TensorFlow Developer\n\nThis folder is for my works(assignments\u0026labs) at TensorFlow Developer Coursera Specialization program and courses which I have taken for that specialization. below is the list of all the specializations and courses with their respective certificates That I have had.\n\n- [**Machine Learning**](https://www.coursera.org/account/accomplishments/certificate/8YFX6GGF8PB9) by Stanford University\n- [**TensorFlow Developer Specilization**](https://www.coursera.org/account/accomplishments/specialization/certificate/GS2KGD5NEU3D) by DeepLearning.AI \n  - Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning\n  - Convolutional Neural Networks in TensorFlow\n  - Natural Language Processing in TensorFlow\n  - Sequences, Time Series and Prediction\n- [**Deep Learning Specialization**](https://www.coursera.org/account/accomplishments/specialization/certificate/KAC9TXFGAVPA) by DeepLearning.AI\n  - Neural Networks and Deep Learning\n  - Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization\n  - Structuring Machine Learning Projects\n  - Convolutional Neural Networks\n  - Sequence Models\n- **Reinforcement Learning Specialization** by University of Alberta\n  - Fundamentals of Reinforcement Learning\n- [**Introduction to Artificial Intelligence (AI)**](https://www.coursera.org/account/accomplishments/certificate/4BBSHBTDPSXR) by IBM\n\n## Seeds Dataset Classifier\n\nThis folder is for a classifier for the Seeds dataset from [here](https://archive.ics.uci.edu/ml/datasets/seeds). the data is first preprocessed with standard normalization and then feeded to various architectures of neural networks to see the overfitting effect and learning curves.\nfor better understanding of the classfier **Tensorboard** is used to analyze the results of the learning, and other callbacks such as\nearly stopping is also used to compile the models. for pre-processing the data _Pandas_ library were used.\n\n## RCV1 Dataset Visualization\nIn this project, we have used the RCV1 dataset to visualize the data. \nThe dataset is available in the following link: [RCV1 Dataset](https://scikit-learn.org/0.18/datasets/rcv1.html) \nThe dataset is a collection of news articles from BBC. 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