{"id":15683686,"url":"https://github.com/ivanbongiorni/tensorflow2.0_notebooks","last_synced_at":"2025-09-03T23:33:25.859Z","repository":{"id":52822031,"uuid":"178187452","full_name":"IvanBongiorni/TensorFlow2.0_Notebooks","owner":"IvanBongiorni","description":"Implementation of a series of Neural Network architectures in TensorFow 2.0","archived":false,"fork":false,"pushed_at":"2021-04-18T01:02:34.000Z","size":1739,"stargazers_count":37,"open_issues_count":1,"forks_count":22,"subscribers_count":3,"default_branch":"master","last_synced_at":"2025-05-07T14:11:30.535Z","etag":null,"topics":["autoencoder","autograph","batch-gradient-descent","classifier","cnn-classifier","convolutional-neural-networks","data-science","deep-learning","dimensionality-reduction","forecast-model","lstm","machine-learning","neural-network","python","python-3","rnn","rnn-tensorflow","tensorflow","tensorflow-tutorials","tensorflow2"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","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/IvanBongiorni.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}},"created_at":"2019-03-28T11:14:25.000Z","updated_at":"2025-03-21T18:27:58.000Z","dependencies_parsed_at":"2022-08-17T15:35:42.793Z","dependency_job_id":null,"html_url":"https://github.com/IvanBongiorni/TensorFlow2.0_Notebooks","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/IvanBongiorni%2FTensorFlow2.0_Notebooks","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/IvanBongiorni%2FTensorFlow2.0_Notebooks/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/IvanBongiorni%2FTensorFlow2.0_Notebooks/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/IvanBongiorni%2FTensorFlow2.0_Notebooks/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/IvanBongiorni","download_url":"https://codeload.github.com/IvanBongiorni/TensorFlow2.0_Notebooks/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":252892504,"owners_count":21820648,"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":["autoencoder","autograph","batch-gradient-descent","classifier","cnn-classifier","convolutional-neural-networks","data-science","deep-learning","dimensionality-reduction","forecast-model","lstm","machine-learning","neural-network","python","python-3","rnn","rnn-tensorflow","tensorflow","tensorflow-tutorials","tensorflow2"],"created_at":"2024-10-03T17:08:12.206Z","updated_at":"2025-05-07T14:11:38.382Z","avatar_url":"https://github.com/IvanBongiorni.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"Author: Ivan Bongiorni, Data Scientist at GfK; [LinkedIn](https://www.linkedin.com/in/ivan-bongiorni-b8a583164/).\n\n# TensorFlow 2.0 Notebooks\n\n\nThis is a collection of my Notebooks on TensorFlow 2.0\n\nThe training of models is based on TensorFlow's **eager execution** method. I'll try to minimize referencese to Keras.\n## Summary of Contents:\n- Basic feed forward stuff\n- Autoencoders\n- Convolutional Neural Networks\n- Recurrent Neural Networks\n- Applications to NLP\n\n---\n---\n\n## Contents:\n\n**Basic feed forward stuff**:\n\n1. [Basic classifier](https://github.com/IvanBongiorni/TensorFlow2.0_Notebooks/blob/master/TensorFlow2.0__00.01_basic_Classifier.ipynb):  implementation of a **feed forward Classifier** with simple, full-Batch Gradient Descent in **Eager execution**.\n\n2. [Mini batch gradient descent](https://github.com/IvanBongiorni/TensorFlow2.0_Notebooks/blob/master/TensorFlow2.0__00.02_MiniBatch_Gradient_Descent.ipynb):  training a model with **Mini Batch Gradient Descent**.\n\n3. [Save and restore models](https://github.com/IvanBongiorni/TensorFlow2.0_Notebooks/blob/master/TensorFlow2.0__00.03_Save_and_Restore_models.ipynb):  how to train a model, save it, then restore it and keep training.\n\n0. Train a Neural Network with frozen layers\n\n---\n\n**Autoencoders**:\n\n1. [Autoencoder for dimensionality reduction](https://github.com/IvanBongiorni/TensorFlow2.0_Notebooks/blob/master/TensorFlow2.0__02.01_Autoencoder_for_Dimensionality_Reduction.ipynb):  implementation of a stacked **Autoencoder for dimensionality reduction** of datasets.\n\n2. Denoising Autoencoder (see CNN section below).\n\n0. Recurrent Autoencoder (see RNN section below).\n\n---\n\n**Convolutional Neural Networks**:\n\n1. [Basic CNN classifier](https://github.com/IvanBongiorni/TensorFlow2.0_Notebooks/blob/master/TensorFlow2.0__03.01_Convolutional_Neural_Network.ipynb): a basic **Convolutional Neural Network** for multiclass classification.\n\n2. Advanced CNN classifier with custom data augmentation.\n\n3. Mixed-CNN classifier.\n\n4. Denoising Autoencoder.\n\n---\n\n**Recurrent Neural Networks**:\n\n1. [LSTM many-to-one forecast model](https://github.com/IvanBongiorni/TensorFlow2.0_Notebooks/blob/master/TensorFlow2.0__04.01_RNN_many2one.ipynb)\n\n2. [LSTM many-to-many forecast model](https://github.com/IvanBongiorni/TensorFlow2.0_Notebooks/blob/master/TensorFlow2.0__04.02_RNN_many2many.ipynb)\n\n3. [Multivariate LSTM regression](https://github.com/IvanBongiorni/TensorFlow2.0_Notebooks/blob/master/TensorFlow2__04.03_RNN_multivariate_regression.ipynb).\n\n0. Seq2seq models.\n\n---\n\n**RNN + Natural Language Processing**\n\n1. LSTM [Text generator](https://github.com/IvanBongiorni/TensorFlow2-RNN_text_generator-Dante_DivineComedy/blob/master/RNN_text_generator_00.ipynb) from [this repository of mine](https://github.com/IvanBongiorni/TensorFlow2-RNN_text_generator-Dante_DivineComedy).\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fivanbongiorni%2Ftensorflow2.0_notebooks","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fivanbongiorni%2Ftensorflow2.0_notebooks","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fivanbongiorni%2Ftensorflow2.0_notebooks/lists"}