{"id":13435209,"url":"https://github.com/isikdogan/deep_learning_tutorials","last_synced_at":"2025-03-18T02:31:18.765Z","repository":{"id":91342459,"uuid":"152662128","full_name":"isikdogan/deep_learning_tutorials","owner":"isikdogan","description":"deep learning: theory + practice","archived":false,"fork":false,"pushed_at":"2019-03-12T06:19:11.000Z","size":360,"stargazers_count":78,"open_issues_count":1,"forks_count":29,"subscribers_count":13,"default_branch":"master","last_synced_at":"2024-08-01T03:14:37.981Z","etag":null,"topics":["deep-learning","exercises","lectures","machine-learning","tutorial","videos"],"latest_commit_sha":null,"homepage":null,"language":"Python","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/isikdogan.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,"governance":null}},"created_at":"2018-10-11T22:14:15.000Z","updated_at":"2024-04-19T12:04:09.000Z","dependencies_parsed_at":"2023-03-13T17:47:05.790Z","dependency_job_id":null,"html_url":"https://github.com/isikdogan/deep_learning_tutorials","commit_stats":{"total_commits":15,"total_committers":1,"mean_commits":15.0,"dds":0.0,"last_synced_commit":"7a81d56c1b6e8bee715ddb08e85ea25562acbdd8"},"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/isikdogan%2Fdeep_learning_tutorials","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/isikdogan%2Fdeep_learning_tutorials/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/isikdogan%2Fdeep_learning_tutorials/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/isikdogan%2Fdeep_learning_tutorials/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/isikdogan","download_url":"https://codeload.github.com/isikdogan/deep_learning_tutorials/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":221704678,"owners_count":16866811,"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":["deep-learning","exercises","lectures","machine-learning","tutorial","videos"],"created_at":"2024-07-31T03:00:33.892Z","updated_at":"2024-10-27T16:32:06.940Z","avatar_url":"https://github.com/isikdogan.png","language":"Python","readme":"\u003ca href=\"#\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/isikdogan/deep_learning_tutorials/master/img/tfcs_github.png\" alt=\"TensorFlow Coding Sessions\"\u003e\u003c/a\u003e\r\n\r\n## Hands-on Deep Learning: TensorFlow Coding Sessions\r\n\r\nThis repository has the code for the Hands-on Deep Learning: TensorFlow Coding Sessions. The videos will be uploaded on a weekly basis.\r\n\r\nThe series consist of the introductory TensorFlow tutorials outlined below:\r\n\r\n| # | Tutorial                                                             | Code | Video            |\r\n|-|------------------------------------------------------------------------|------|------------------|\r\n|1| Introduction to TensorFlow: graphs, sessions, constants, and variables |[S1](S1/) and [S1_notebook.ipynb](S1/S1_notebook.ipynb)| [Video #1](https://youtu.be/1KzJbIFnVTE) |\r\n|2| Training a multilayer perceptron                                       |[S2_live.py](S2_live.py)| [Video #2](https://youtu.be/b7ykcBzz9wo) |\r\n|3| Setting up the training and validation pipeline                        |[S3_live.py](S3_live.py)| [Video #3](https://youtu.be/l_ZvxKBToWs) |\r\n|4| Regularization, saving and resuming from checkpoints, and TensorBoard  |[S4_live.py](S4_live.py)| [Video #4](https://youtu.be/ni9FZtF_gLs) |\r\n|5| Convolutional neural networks, batchnorm, learning rate schedules, optimizers|[S5_live.py](S5_live.py)| [Video #5](https://youtu.be/ULX1nWPAJbM) |\r\n|6| Converting a dataset into TFRecords, training an image classifier, and freezing the model for deployment|[S6](S6/)| [Video #6](https://youtu.be/tzKqjPdAf8M) |\r\n|7| Transfer learning: fine tuning a model in TensorFlow                   |[S7](S7/)| [Video #7](https://youtu.be/jccBP_uA98k) |\r\n|8| Using a Python iterator as a data generator and training a denoising autoencoder  |[S8](S8/)| N/A |\r\n|9| What is new in TensorFlow 2.0 **[new]**                   |[S9](S9/)| [Video #8](https://youtu.be/GI_QVLNCgPo) |\r\n\r\n---\r\n\r\n\u003ca href=\"https://www.youtube.com/watch?v=nmnaO6esC7c\u0026list=PLWKotBjTDoLj3rXBL-nEIPRN9V3a9Cx07\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/isikdogan/deep_learning_tutorials/master/img/dlcc_github.jpg\" alt=\"Deep Learning Crash Course\"\u003e\u003c/a\u003e\r\n\r\n## Deep Learning Crash Course\r\n\r\nA series of mini-lectures on the fundamentals of machine learning, with a focus on neural networks and deep learning.\r\n\r\n* [Lecture #1: Introduction](https://youtu.be/nmnaO6esC7c)\r\n* [Lecture #2: Artificial Neural Networks Demystified](https://youtu.be/oS5fz_mHVz0)\r\n* [Lecture #3: Artificial Neural Networks: Going Deeper](https://youtu.be/_XPkAxm0Yx0)\r\n* [Lecture #4: Overfitting, Underfitting, and Model Capacity](https://youtu.be/ms-Ooh9mjiE)\r\n* [Lecture #5: Regularization](https://youtu.be/NRCZJUviZN0)\r\n* [Lecture #6: Data Collection and Preprocessing](https://youtu.be/dAg-_gzFo14)\r\n* [Lecture #7: Convolutional Neural Networks Explained](https://youtu.be/-I0lry5ceDs)\r\n* [Lecture #8: How to Design a Convolutional Neural Network](https://youtu.be/fTw3K8D5xDs)\r\n* [Lecture #9: Transfer Learning](https://youtu.be/_2EHcpg52uU)\r\n* [Lecture #10: Optimization Tricks: momentum, batch-norm, and more](https://youtu.be/kK8-jCCR4is)\r\n* [Lecture #11: Recurrent Neural Networks](https://youtu.be/k97Jrg_4tFA)\r\n* [Lecture #12: Deep Unsupervised Learning](https://youtu.be/P8_W5Wc4zeg)\r\n* [Lecture #13: Generative Adversarial Networks](https://youtu.be/7tFBoxex4JE)\r\n* [Lecture #14: Practical Methodology in Deep Learning](https://youtu.be/9Sl_t_GxX6w)\r\n\r\n---\r\n","funding_links":[],"categories":["Tutorials \u003ca name=\"GitHub-tutorials\" /\u003e 📕 📘 📗 📓"],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fisikdogan%2Fdeep_learning_tutorials","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fisikdogan%2Fdeep_learning_tutorials","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fisikdogan%2Fdeep_learning_tutorials/lists"}