{"id":20509053,"url":"https://github.com/seopbo/cs20","last_synced_at":"2025-08-16T01:35:06.794Z","repository":{"id":106518825,"uuid":"130573560","full_name":"seopbo/CS20","owner":"seopbo","description":"Refactoring contents and codes of CS20 : Tensorflow for Deep Learning Research","archived":false,"fork":false,"pushed_at":"2019-01-18T01:22:15.000Z","size":2471,"stargazers_count":62,"open_issues_count":0,"forks_count":27,"subscribers_count":7,"default_branch":"master","last_synced_at":"2025-04-13T22:39:21.449Z","etag":null,"topics":["tensorflow"],"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/seopbo.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}},"created_at":"2018-04-22T14:09:38.000Z","updated_at":"2023-08-22T05:09:40.000Z","dependencies_parsed_at":"2024-01-14T11:00:51.864Z","dependency_job_id":null,"html_url":"https://github.com/seopbo/CS20","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/seopbo/CS20","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/seopbo%2FCS20","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/seopbo%2FCS20/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/seopbo%2FCS20/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/seopbo%2FCS20/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/seopbo","download_url":"https://codeload.github.com/seopbo/CS20/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/seopbo%2FCS20/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":270656718,"owners_count":24623452,"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","status":"online","status_checked_at":"2025-08-15T02:00:12.559Z","response_time":110,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"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":["tensorflow"],"created_at":"2024-11-15T20:22:04.599Z","updated_at":"2025-08-16T01:35:06.751Z","avatar_url":"https://github.com/seopbo.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# CS 20 : Tensorflow for Deep Learning Research\nRefactoring code examples of CS 20 : Tensorflow for Deep Learning Research following tensorflow 2.0 (current tf 1.12)\n\n* notice\n\t+ `{filename}_kd.ipynb` is implemented by using `tf.keras` and `tf.data`\n\t+ `{filename}_de.ipynb` is implemented by using `tf.data` and `eager execution`\n\t+ `{filename}_kde.ipynb` is implemented by using `tf.keras`, `tf.data` and `eager execution` \n\n* syllabus : http://web.stanford.edu/class/cs20si/syllabus.html\n* github : https://github.com/chiphuyen/stanford-tensorflow-tutorials \n- - -\n\n### 01. Overview of Tensorflow\n- Lec01 Overview of Tensorflow example code\n\t- [Lec01_Overview of Tensorflow.ipynb](https://nbviewer.jupyter.org/github/aisolab/CS20/blob/master/Lec01_Overview%20of%20Tensorflow/Lec01_Overview%20of%20Tensorflow.ipynb)\n\n### 02. Operations\n- Lec02 Operations example code\n\t- [Lec02_Operations.ipynb](https://nbviewer.jupyter.org/github/aisolab/CS20/blob/master/Lec02_Operations/Lec02_Operations.ipynb)\n\n### 03. Linear and Logistic Regression\n- Simple usage of tf.data\n\t- [How to simply use tf.data.ipynb](https://nbviewer.jupyter.org/github/aisolab/CS20/blob/master/Lec03_Linear%20and%20Logistic%20Regression/How%20to%20simply%20use%20tf.data.ipynb)\n\n\n- Lec03 Linear and Logistic Regression example code\n\t- [Lec03_Linear Regression with mse loss.ipynb](https://nbviewer.jupyter.org/github/aisolab/CS20/blob/master/Lec03_Linear%20and%20Logistic%20Regression/Lec03_Linear%20Regression%20with%20mse%20loss.ipynb)\n\t- [Lec03_Linear Regression with huber loss by low-level.ipynb](https://nbviewer.jupyter.org/github/aisolab/CS20/blob/master/Lec03_Linear%20and%20Logistic%20Regression/Lec03_Linear%20Regression%20with%20huber%20loss%20by%20low-level.ipynb)\n\t- [Lec03_Linear Regression with huber loss by high-level.ipynb](https://nbviewer.jupyter.org/github/aisolab/CS20/blob/master/Lec03_Linear%20and%20Logistic%20Regression/Lec03_Linear%20Regression%20with%20huber%20loss%20by%20high-level.ipynb)\n\t- [Lec03_Linear Regression with tf.data.ipynb](https://nbviewer.jupyter.org/github/aisolab/CS20/blob/master/Lec03_Linear%20and%20Logistic%20Regression/Lec03_Linear%20Regression%20with%20tf.data.ipynb)\n\t- [Lec03_Linear Regression with tf.data_de.ipynb](https://nbviewer.jupyter.org/github/aisolab/CS20/blob/master/Lec03_Linear%20and%20Logistic%20Regression/Lec03_Linear%20Regression%20with%20tf.data_de.ipynb)\n\t- [Lec03_Logistic Regression with ce loss.ipynb](https://nbviewer.jupyter.org/github/aisolab/CS20/blob/master/Lec03_Linear%20and%20Logistic%20Regression/Lec03_Logistic%20Regression%20with%20ce%20loss.ipynb)\n\t- [Lec03_Logistic Regression with tf.data.ipynb](https://nbviewer.jupyter.org/github/aisolab/CS20/blob/master/Lec03_Linear%20and%20Logistic%20Regression/Lec03_Logistic%20Regression%20with%20tf.data.ipynb)\n\t- [Lec03_Logistic Regression with tf.data_de.ipynb](https://nbviewer.jupyter.org/github/aisolab/CS20/blob/master/Lec03_Linear%20and%20Logistic%20Regression/Lec03_Logistic%20Regression%20with%20tf.data_de.ipynb)\n\n### 04. Eager Execution\n+ Lec04 Eager execution example code\n\t* [Lec04_Eager execution.ipynb](https://nbviewer.jupyter.org/github/aisolab/CS20/blob/master/Lec04_Eager%20execution/Lec04_Eager%20execution.ipynb)\n\t* [Lec04_Automatic differentiation and gradient tape.ipynb](https://nbviewer.jupyter.org/github/aisolab/CS20/blob/master/Lec04_Eager%20execution/Lec04_Automatic%20differentiation%20and%20gradient%20tape.ipynb)\n\t* [Lec04_Custom training basics.ipynb](https://nbviewer.jupyter.org/github/aisolab/CS20/blob/master/Lec04_Eager%20execution/Lec04_Custom%20training%20basics.ipynb)\n\t* [Lec04_Custom training walkthrough.ipynb](https://nbviewer.jupyter.org/github/aisolab/CS20/blob/master/Lec04_Eager%20execution/Lec04_Custom%20training%20walkthrough.ipynb)\n\t* [Lec04_Custom training subclassing.ipynb](https://nbviewer.jupyter.org/github/aisolab/CS20/blob/master/Lec04_Eager%20execution/Lec04_Custom%20training%20subclassing.ipynb)\n### 05. Variable sharing and managing experiments\n- Simple usage of tf.keras\n\t- [How to use keras.ipynb](https://nbviewer.jupyter.org/github/aisolab/CS20/blob/master/Lec05_Variable%20sharing%20and%20managing%20experiments/How%20to%20use%20keras.ipynb) \n\n- Lec05 Variable sharing and managing experiments example code\n\t- [Lec05_Variable sharing.ipynb](https://nbviewer.jupyter.org/github/aisolab/CS20/blob/master/Lec05_Variable%20sharing%20and%20managing%20experiments/Lec05_Variable%20sharing.ipynb)\n\t- [Lec05_Randomization.ipynb](https://nbviewer.jupyter.org/github/aisolab/CS20/blob/master/Lec05_Variable%20sharing%20and%20managing%20experiments/Lec05_Randomization.ipynb)\n\t- [Lec05_Applied example with tf.placeholder.ipynb](https://nbviewer.jupyter.org/github/aisolab/CS20/blob/master/Lec05_Variable%20sharing%20and%20managing%20experiments/Lec05_Applied%20example%20with%20tf.placeholder.ipynb)\n\t- [Lec05_Applied example with tf.data.ipynb](https://nbviewer.jupyter.org/github/aisolab/CS20/blob/master/Lec05_Variable%20sharing%20and%20managing%20experiments/Lec05_Applied%20example%20with%20tf.data.ipynb)\n\t- [Lec05_Applied example with tf.data_kde.ipynb](https://nbviewer.jupyter.org/github/aisolab/CS20/blob/master/Lec05_Variable%20sharing%20and%20managing%20experiments/Lec05_Applied%20example%20with%20tf.data_kde.ipynb)\n\t- [Lec05_Word2vec_simple.ipynb](https://nbviewer.jupyter.org/github/aisolab/CS20/blob/master/Lec05_Variable%20sharing%20and%20managing%20experiments/Lec05_Word2vec_simple.ipynb)\n\n### 06. Introduction to ConvNet\n### 07. ConvNet in TensorFlow\n- Lec07 ConvNet in TensorFlow example code\n\t- [Lec07_ConvNet mnist by low-level.ipynb](https://nbviewer.jupyter.org/github/aisolab/CS20/blob/master/Lec07_ConvNet%20in%20Tensorflow/Lec07_ConvNet%20mnist%20by%20low-level.ipynb)\n\t- [Lec07_ConvNet mnist by high-level.ipynb](https://nbviewer.jupyter.org/github/aisolab/CS20/blob/master/Lec07_ConvNet%20in%20Tensorflow/Lec07_ConvNet%20mnist%20by%20high-level.ipynb)\n\t- [Lec07_ConvNet mnist by high-level_kd.ipynb](https://nbviewer.jupyter.org/github/aisolab/CS20/blob/master/Lec07_ConvNet%20in%20Tensorflow/Lec07_ConvNet%20mnist%20by%20high-level_kd.ipynb)\n\t- [Lec07_ConvNet mnist by high-level_kde.ipynb](https://nbviewer.jupyter.org/github/aisolab/CS20/blob/master/Lec07_ConvNet%20in%20Tensorflow/Lec07_ConvNet%20mnist%20by%20high-level_kde.ipynb)\n\t- [Lec07_ConvNet mnist with Weight initialization and Drop out.ipynb](https://nbviewer.jupyter.org/github/aisolab/CS20/blob/master/Lec07_ConvNet%20in%20Tensorflow/Lec07_ConvNet%20mnist%20with%20Weight%20initialization%20and%20Drop%20out.ipynb)\n\t- [Lec07_ConvNet mnist with Weight initialization and Drop out_kde.ipynb](https://nbviewer.jupyter.org/github/aisolab/CS20/blob/master/Lec07_ConvNet%20in%20Tensorflow/Lec07_ConvNet%20mnist%20with%20Weight%20initialization%20and%20Drop%20out_kde.ipynb)\n\t- [Lec07_ConvNet mnist with Weight initialization and Batch norm.ipynb](https://nbviewer.jupyter.org/github/aisolab/CS20/blob/master/Lec07_ConvNet%20in%20Tensorflow/Lec07_ConvNet%20mnist%20with%20Weight%20initialization%20and%20Batch%20norm.ipynb)\n\t- [Lec07_ConvNet mnist with Weight initialization and Batch norm_kde.ipynb](https://nbviewer.jupyter.org/github/aisolab/CS20/blob/master/Lec07_ConvNet%20in%20Tensorflow/Lec07_ConvNet%20mnist%20with%20Weight%20initialization%20and%20Batch%20norm_kde.ipynb)\n\n### 08. Style Transfer\n### 09. Variational Auto-Encoders\n### 10. Generative Adversarial Networks\n### 11. Recurrent Neural Networks\n- Presentation\n\t- [To quickly implementing RNN.ipynb](https://nbviewer.jupyter.org/github/aisolab/CS20/blob/master/Lec11_Recurrent%20Neural%20Networks/To%20quickly%20implementing%20RNN.ipynb)\n\n- Lec11 Recurrent Neural Networks example code\n- many to one, word sentiment classification example\n\t- [Lec11_Many to One Classification by RNN.ipynb](https://nbviewer.jupyter.org/github/aisolab/CS20/blob/master/Lec11_Recurrent%20Neural%20Networks/Lec11_Many%20to%20One%20Classification%20by%20RNN.ipynb)\n\t- [Lec11_Many to One Classification by RNN_kde.ipynb](https://nbviewer.jupyter.org/github/aisolab/CS20/blob/master/Lec11_Recurrent%20Neural%20Networks/Lec11_Many%20to%20One%20Classification%20by%20RNN_kde.ipynb)\n\t- [Lec11_Many to One Classification by LSTM.ipynb](https://nbviewer.jupyter.org/github/aisolab/CS20/blob/master/Lec11_Recurrent%20Neural%20Networks/Lec11_Many%20to%20One%20Classification%20by%20LSTM.ipynb)\n\t- [Lec11_Many to One Classification by GRU.ipynb](https://nbviewer.jupyter.org/github/aisolab/CS20/blob/master/Lec11_Recurrent%20Neural%20Networks/Lec11_Many%20to%20One%20Classification%20by%20GRU.ipynb)\n\t- [Lec11_Many to One Classification by Bi-directional RNN.ipynb](https://nbviewer.jupyter.org/github/aisolab/CS20/blob/master/Lec11_Recurrent%20Neural%20Networks/Lec11_Many%20to%20One%20Classification%20by%20Bi-directional%20RNN.ipynb)\n\t- [Lec11_Many to One Classification by Bi-directional LSTM.ipynb](https://nbviewer.jupyter.org/github/aisolab/CS20/blob/master/Lec11_Recurrent%20Neural%20Networks/Lec11_Many%20to%20One%20Classification%20by%20Bi-directional%20LSTM.ipynb)\n\t- [Lec11_Many to One Classification by Bi-directional GRU.ipynb](https://nbviewer.jupyter.org/github/aisolab/CS20/blob/master/Lec11_Recurrent%20Neural%20Networks/Lec11_Many%20to%20One%20Classification%20by%20Bi-directional%20GRU.ipynb)\n\t- [Lec11_Many to One Classification by Stacked RNN with Drop out.ipynb](https://nbviewer.jupyter.org/github/aisolab/CS20/blob/master/Lec11_Recurrent%20Neural%20Networks/Lec11_Many%20to%20One%20Classification%20by%20Stacked%20RNN%20with%20Drop%20out.ipynb)\n\t- [Lec11_Many to One Classification by Stacked LSTM with Drop out.ipynb](https://nbviewer.jupyter.org/github/aisolab/CS20/blob/master/Lec11_Recurrent%20Neural%20Networks/Lec11_Many%20to%20One%20Classification%20by%20Stacked%20LSTM%20with%20Drop%20out.ipynb)\n\t- [Lec11_Many to One Classification by Stacked GRU with Drop out.ipynb](https://nbviewer.jupyter.org/github/aisolab/CS20/blob/master/Lec11_Recurrent%20Neural%20Networks/Lec11_Many%20to%20One%20Classification%20by%20Stacked%20GRU%20with%20Drop%20out.ipynb)\n\t- [Lec11_Many to One Classification by Stacked Bi-directional RNN with Drop out.ipynb](https://nbviewer.jupyter.org/github/aisolab/CS20/blob/master/Lec11_Recurrent%20Neural%20Networks/Lec11_Many%20to%20One%20Classification%20by%20Stacked%20Bi-directional%20RNN%20with%20Drop%20out.ipynb)\n\t- [Lec11_Many to One Classification by Stacked Bi-directional LSTM with Drop out.ipynb](https://nbviewer.jupyter.org/github/aisolab/CS20/blob/master/Lec11_Recurrent%20Neural%20Networks/Lec11_Many%20to%20One%20Classification%20by%20Stacked%20Bi-directional%20LSTM%20with%20Drop%20out.ipynb)\n\t- [Lec11_Many to One Classification by Stacked Bi-directional GRU with Drop out.ipynb](https://nbviewer.jupyter.org/github/aisolab/CS20/blob/master/Lec11_Recurrent%20Neural%20Networks/Lec11_Many%20to%20One%20Classification%20by%20Stacked%20Bi-directional%20GRU%20with%20Drop%20out.ipynb)\n\n- many to many, simple pos-tagger example\n\t- [Lec11_Many to Many Classification by RNN.ipynb](https://nbviewer.jupyter.org/github/aisolab/CS20/blob/master/Lec11_Recurrent%20Neural%20Networks/Lec11_Many%20to%20Many%20Classification%20by%20RNN.ipynb)\n\t- [Lec11_Many to Many Classification by LSTM.ipynb](https://nbviewer.jupyter.org/github/aisolab/CS20/blob/master/Lec11_Recurrent%20Neural%20Networks/Lec11_Many%20to%20Many%20Classification%20by%20LSTM.ipynb)\n\t- [Lec11_Many to Many Classification by GRU.ipynb](https://nbviewer.jupyter.org/github/aisolab/CS20/blob/master/Lec11_Recurrent%20Neural%20Networks/Lec11_Many%20to%20Many%20Classification%20by%20GRU.ipynb)\n\t- [Lec11_Many to Many Classification by Bi-directional RNN.ipynb](https://nbviewer.jupyter.org/github/aisolab/CS20/blob/master/Lec11_Recurrent%20Neural%20Networks/Lec11_Many%20to%20Many%20Classification%20by%20Bi-directional%20RNN.ipynb)\n\t- [Lec11_Many to Many Classification by Bi-directional LSTM.ipynb](https://nbviewer.jupyter.org/github/aisolab/CS20/blob/master/Lec11_Recurrent%20Neural%20Networks/Lec11_Many%20to%20Many%20Classification%20by%20Bi-directional%20LSTM.ipynb)\n\t- [Lec11_Many to Many Classification by Bi-directional GRU.ipynb](https://nbviewer.jupyter.org/github/aisolab/CS20/blob/master/Lec11_Recurrent%20Neural%20Networks/Lec11_Many%20to%20Many%20Classification%20by%20Bi-directional%20GRU.ipynb)\n\t- [Lec11_Many to Many Classification by Stacked RNN with Drop out.ipynb](https://nbviewer.jupyter.org/github/aisolab/CS20/blob/master/Lec11_Recurrent%20Neural%20Networks/Lec11_Many%20to%20Many%20Classification%20by%20Stacked%20RNN%20with%20Drop%20out.ipynb)\n\t- [Lec11_Many to Many Classification by Stacked LSTM with Drop out.ipynb](https://nbviewer.jupyter.org/github/aisolab/CS20/blob/master/Lec11_Recurrent%20Neural%20Networks/Lec11_Many%20to%20Many%20Classification%20by%20Stacked%20LSTM%20with%20Drop%20out.ipynb)\n\t- [Lec11_Many to Many Classification by Stacked GRU with Drop out.ipynb](https://nbviewer.jupyter.org/github/aisolab/CS20/blob/master/Lec11_Recurrent%20Neural%20Networks/Lec11_Many%20to%20Many%20Classification%20by%20Stacked%20GRU%20with%20Drop%20out.ipynb)\n\t- [Lec11_Many to Many Classification by Stacked Bi-directional RNN with Drop out.ipynb](https://nbviewer.jupyter.org/github/aisolab/CS20/blob/master/Lec11_Recurrent%20Neural%20Networks/Lec11_Many%20to%20Many%20Classification%20by%20Stacked%20Bi-directional%20RNN%20with%20Drop%20out.ipynb)\n\t- [Lec11_Many to Many Classification by Stacked Bi-directional LSTM with Drop out.ipynb](https://nbviewer.jupyter.org/github/aisolab/CS20/blob/master/Lec11_Recurrent%20Neural%20Networks/Lec11_Many%20to%20Many%20Classification%20by%20Stacked%20Bi-directional%20LSTM%20with%20Drop%20out.ipynb)\n\t- [Lec11_Many to Many Classification by Stacked Bi-directional GRU with Drop out.ipynb](https://nbviewer.jupyter.org/github/aisolab/CS20/blob/master/Lec11_Recurrent%20Neural%20Networks/Lec11_Many%20to%20Many%20Classification%20by%20Stacked%20Bi-directional%20GRU%20with%20Drop%20out.ipynb)\n\n### 12. Seq2Seq with Attention\n- Lec12 Seq2Seq with Attention example code\n\n- encoder decoder (many to many), simple neural machine translation example\n\t- [Lec12_Seq2Seq by Encoder RNN and Decoder RNN.ipynb](https://nbviewer.jupyter.org/github/aisolab/CS20/blob/master/Lec12_Seq2Seq%20with%20Attention/Lec12_Seq2Seq%20by%20Encoder%20RNN%20and%20Decoder%20RNN.ipynb)\n\t- [Lec12_Seq2Seq by Encoder Bi-directional RNN and Decoder RNN.ipynb](https://nbviewer.jupyter.org/github/aisolab/CS20/blob/master/Lec12_Seq2Seq%20with%20Attention/Lec12_Seq2Seq%20by%20Encoder%20Bi-directional%20RNN%20and%20Decoder%20RNN.ipynb)\n\t- [Lec12_Seq2Seq with Attention by Encoder RNN and Decoder RNN.ipynb](https://nbviewer.jupyter.org/github/aisolab/CS20/blob/master/Lec12_Seq2Seq%20with%20Attention/Lec12_Seq2Seq%20with%20Attention%20by%20Encoder%20RNN%20and%20Decoder%20RNN.ipynb)\n\t- [Lec12_Seq2Seq with Attention by Encoder Bi-directional RNN and Decoder RNN.ipynb](https://nbviewer.jupyter.org/github/aisolab/CS20/blob/master/Lec12_Seq2Seq%20with%20Attention/Lec12_Seq2Seq%20with%20Attention%20by%20Encoder%20Bi-directional%20RNN%20and%20Decoder%20RNN.ipynb)\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fseopbo%2Fcs20","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fseopbo%2Fcs20","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fseopbo%2Fcs20/lists"}