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
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
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
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