TensorFlow 101: Introduction to Deep Learning

I have worked all my life in Machine Learning, and I've never seen one algorithm knock over its benchmarks like Deep Learning - Andrew Ng

This repository includes deep learning based project implementations I've done from scratch. You can find both the source code and documentation as a step by step tutorial. Model structrues and pre-trained weights are shared as well.

Facial Expression Recognition Code, Tutorial

This is a custom CNN model. Kaggle FER 2013 data set is fed to the model. This model runs fast and produces satisfactory results. It can be also run real time as well.

<p align="center"><img src="https://sefiks.com/wp-content/uploads/2017/12/pablo-facial-expression.png" width="70%"></p>

We can run emotion analysis in real time as well Real Time Code, Video

<p align="center"><img src="https://sefiks.com/wp-content/uploads/2019/01/real-time-emotion-mark.png" width="70%"></p>

Face Recognition Code, Tutorial

Face recognition is mainly based on convolutional neural networks. We feed two face images to a CNN model and it returns a multi-dimensional vector representations. We then compare these representations to determine these two face images are same person or not.

<p align="center"><img src="https://sefiks.com/wp-content/uploads/2019/01/face-recognition-demo.png" width="70%"></p>

You can find the most popular face recognition models below.

| Model | Creator | LFW Score | Code | Tutorial | | --- | --- | --- | --- | --- | | VGG-Face | The University of Oxford | 98.78 | Code | Tutorial | | FaceNet | Google | 99.65 | Code | Tutorial | | DeepFace | Facebook | - | Code | Tutorial | | OpenFace | Carnegie Mellon University | 93.80 | Code | Tutorial | | DeepID | The Chinese University of Hong Kong | - | Code | Tutorial | | Dlib | Davis E. King | 99.38 | Code | Tutorial | | OpenCV | OpenCV Foundation | - | Code | Tutorial | | OpenFace in OpenCV | Carnegie Mellon University | 92.92 | Code | Tutorial | | SphereFace | Georgia Institute of Technology | 99.30 | Code | Tutorial | | ArcFace | Imperial College London | 99.40 | Code | Tutorial |

All of those state-of-the-art face recognition models are wrapped in deepface library for python. You can build and run them with a few lines of code. To have more information, please visit the repo of the library.

Real Time Deep Face Recognition Implementation Code, Video

These are the real time implementations of the common face recognition models we've mentioned in the previous section. VGG-Face has the highest face recognition score but it comes with the high complexity among models. On the other hand, OpenFace is a pretty model and it has a close accuracy to VGG-Face but its simplicity offers high speed than others.

<p align="center"><img src="https://sefiks.com/wp-content/uploads/2020/02/deepface-cover.jpg" width="90%"></p>

| Model | Creator | Code | Demo | | --- | --- | --- | --- | | VGG-Face | Oxford University | Code | Video | | FaceNet | Google | Code | Video | | DeepFace | Facebook | Code | Video | | OpenFace | Carnegie Mellon University | Code | Video |

Large Scale Face Recognition

Face recognition requires to apply face verification several times. It has a O(n) time complexity and it would be problematic for very large scale data sets (millions or billions level data). Herein, if you have a really strong database, then you use relational databases and regular SQL. Besides, you can store facial embeddings in nosql databases. In this way, you can have the power of the map reduce technology. Besides, approximate nearest neighbor (a-nn) algorithm reduces time complexity dramatically. Spotify Annoy, Facebook Faiss and NMSLIB are amazing a-nn libraries. Besides, Elasticsearch wraps NMSLIB and it also offers highly scalablity. You should build and run face recognition models within those a-nn libraries if you have really large scale data sets.

| Library | Algorithm | Tutorial | Code | Demo | | --- | --- | --- | --- | --- | | Spotify Annoy | a-nn | Tutorial | - | Video | | Facebook Faiss | a-nn | Tutorial | - | - | | NMSLIB | a-nn | Tutorial | Code | - | | Elasticsearch | a-nn | Tutorial | Code | Video | | mongoDB | k-NN | Tutorial | Code | - | | Cassandra | k-NN | Tutorial | Code | Video | | Redis | k-NN | Tutorial | Code | Video | | Hadoop | k-NN | Tutorial | Code | - | | Relational Database | k-NN | Tutorial | Code | - | | Neo4j Graph| k-NN | Tutorial | [Code](https://github.com/serengil/tensorflow-