https://github.com/isikdogan/deep_learning_tutorials

deep learning: theory + practice
https://github.com/isikdogan/deep_learning_tutorials

deep-learning exercises lectures machine-learning tutorial videos

Last synced: 2 months ago
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deep learning: theory + practice

• Host: GitHub
• URL: https://github.com/isikdogan/deep_learning_tutorials
• Owner: isikdogan
• Created: 2018-10-11T22:14:15.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2019-03-12T06:19:11.000Z (over 5 years ago)
• Last Synced: 2024-02-01T10:24:26.559Z (5 months ago)
• Topics: deep-learning, exercises, lectures, machine-learning, tutorial, videos
• Language: Python
• Size: 352 KB
• Stars: 77
• Watchers: 13
• Forks: 29
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

Lists

• awesome-tensorflow-2 - Deep Learning Crash Course-(S9)

README

        


## Hands-on Deep Learning: TensorFlow Coding Sessions

This repository has the code for the Hands-on Deep Learning: TensorFlow Coding Sessions. The videos will be uploaded on a weekly basis.

The series consist of the introductory TensorFlow tutorials outlined below:

| # | Tutorial                                                             | Code | Video            |

|-|------------------------------------------------------------------------|------|------------------|

|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) |

|2| Training a multilayer perceptron                                       |[S2_live.py](S2_live.py)| [Video #2](https://youtu.be/b7ykcBzz9wo) |

|3| Setting up the training and validation pipeline                        |[S3_live.py](S3_live.py)| [Video #3](https://youtu.be/l_ZvxKBToWs) |

|4| Regularization, saving and resuming from checkpoints, and TensorBoard  |[S4_live.py](S4_live.py)| [Video #4](https://youtu.be/ni9FZtF_gLs) |

|5| Convolutional neural networks, batchnorm, learning rate schedules, optimizers|[S5_live.py](S5_live.py)| [Video #5](https://youtu.be/ULX1nWPAJbM) |

|6| Converting a dataset into TFRecords, training an image classifier, and freezing the model for deployment|[S6](S6/)| [Video #6](https://youtu.be/tzKqjPdAf8M) |

|7| Transfer learning: fine tuning a model in TensorFlow                   |[S7](S7/)| [Video #7](https://youtu.be/jccBP_uA98k) |

|8| Using a Python iterator as a data generator and training a denoising autoencoder  |[S8](S8/)| N/A |

|9| What is new in TensorFlow 2.0 **[new]**                   |[S9](S9/)| [Video #8](https://youtu.be/GI_QVLNCgPo) |

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## Deep Learning Crash Course

A series of mini-lectures on the fundamentals of machine learning, with a focus on neural networks and deep learning.

* [Lecture #1: Introduction](https://youtu.be/nmnaO6esC7c)

* [Lecture #2: Artificial Neural Networks Demystified](https://youtu.be/oS5fz_mHVz0)

* [Lecture #3: Artificial Neural Networks: Going Deeper](https://youtu.be/_XPkAxm0Yx0)

* [Lecture #4: Overfitting, Underfitting, and Model Capacity](https://youtu.be/ms-Ooh9mjiE)

* [Lecture #5: Regularization](https://youtu.be/NRCZJUviZN0)

* [Lecture #6: Data Collection and Preprocessing](https://youtu.be/dAg-_gzFo14)

* [Lecture #7: Convolutional Neural Networks Explained](https://youtu.be/-I0lry5ceDs)

* [Lecture #8: How to Design a Convolutional Neural Network](https://youtu.be/fTw3K8D5xDs)

* [Lecture #9: Transfer Learning](https://youtu.be/_2EHcpg52uU)

* [Lecture #10: Optimization Tricks: momentum, batch-norm, and more](https://youtu.be/kK8-jCCR4is)

* [Lecture #11: Recurrent Neural Networks](https://youtu.be/k97Jrg_4tFA)

* [Lecture #12: Deep Unsupervised Learning](https://youtu.be/P8_W5Wc4zeg)

* [Lecture #13: Generative Adversarial Networks](https://youtu.be/7tFBoxex4JE)

* [Lecture #14: Practical Methodology in Deep Learning](https://youtu.be/9Sl_t_GxX6w)

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