TensorFlow 101: Introduction to Deep Learning

In this repository, source codes will be shared while capturing "TensorFlow 101: Introduction to Deep Learning" online course published on Udemy.

Course: https://www.udemy.com/tensorflow-101-introduction-to-deep-learning/?couponCode=TF101-BLOG-201710 (90% OFF over regular price)

Post: https://sefiks.com/2017/07/11/hello-tensorflow/

Course content

The course consists of 18 lectures and includes 3 hours material.

Section 1 - Installing TensorFlow

1- Installing TensorFlow and Prerequisites

2- Jupyter notebook

3- Hello, TensorFlow! Building Deep Neural Networks Classifier Model

Section 2 - Reusability in TensorFlow

1- Restoring and Working on Already Trained DNN In TensorFlow

2- Importing Saved TensorFlow DNN Classifier Model in Java

Section 3 - Monitoring and Evaluating

1- Monitoring Model Evaluation Metrics in TensorFlow and TensorBoard

Section 4 - Building regression and time series models

1- Building a DNN Regressor for Non-Linear Time Series in TensorFlow

2- Visualizing ML Results with Matplotlib and Embed them in TensorBoard

Section 5 - Building Unsupervised Learning Models

1- Unsupervised learning and k-means clustering with TensorFlow

2- Applying k-means clustering to n-dimensional datasets in TensorFlow

Section 6 - Tuning Deep Neural Networks Models

1- Optimization Algorithms in TensorFlow

2- Activation Functions in TensorFlow

Section 7 - Consuming TensorFlow via Keras

1- Installing Keras

2- Building DNN Classifier with Keras

3- Storing and restoring a trained neural networks model with Keras

Section 8 - Advanced Applications

1- Handwritten Digit Classification Using Neural Networks ( Additional Tutorial )

2- Handwritten Digit Recognition Using Convolutional Neural Networks with Keras ( Additional Tutorial )

3- Transfer Learning: Consuming InceptionV3 to Classify Cat and Dog Images in Keras ( Additional Tutorial )

Unrecorded lectures

1- Facial Expression Recognition Including Training and Testing on a image ( Additional Tutorial )

2- Facial Expression Recognition Including Stream Data and Webcam ( Additional Tutorial )

3- How single layer perceptron works

4- Autoencoder and clustering ( Additional Tutorial )

5- Convolutional Autoencoder and clustering ( Additional Tutorial )

6- Face Recognition With Oxford VGG-Face Model ( Additional Tutorial )

7- Real Time Deep Face Recognition Implementation with VGG-Face ( Demo )

8- Face Recognition with Google FaceNet Model (Additional Tutorial)

9- Making Arts with Deep Learning: Artistic Style Transfer ( Additional Tutorial )

10- Gradient Vanishing Problem ( Additional Tutorial )