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TensorFlow 101: Introduction to Deep Learning

Ready to build the future with Deep Neural Networks? Stand on the shoulder of TensorFlow and Keras for Machine Learning.
Instructor:
Sefik Ilkin Serengil
4,407 students enrolled
English More
You will be able to build deep learning models for different business domains in TensorFlow
You can distinguish classification and regression problems, apply supervised learning, and can develop solutions
You can also apply segmentation analysis through unsupervised learning and clustering
You can consume TensorFlow via Keras in easier way.
Finally, you will be informed about tuning machine learning models to produce more successful results

This course provides you to be able to build Deep Neural Networks models for different business domains with one of the most common machine learning library TensorFlow provided by Google AI team. The both concept of deep learning and its applications will be mentioned in this course. Also, we will focus on Keras. This course appeals to ones who interested in Machine Learning, Data Science and AI. Also, you don’t have to be attend any ML course before.

Introduction

1
Installing Tensorflow and Prerequisites on Windows

This video includes installation of Deep Learning Framework Tensorflow and its prerequisites. Python 3.5.3, Anaconda 4.4.0 and Tensorflow 1.2.0 respectively on Windows 7 64-bit OS.

2
Jupyter notebook

Jupyter notebook is a pretty cute editor to develop python and tensorflow code. In this video, we will mention how to use it.

3
Hello, TensorFlow! Building Deep Neural Networks Classifier Model

In previous post, we've gotten TensorFlow up. In this video, we are going to mention how to build deep neural networks classifier with TensorFlow. Classification is applied on Exclusive OR (XOR) gate dataset. 

Actually, XOR gate solution is hello world program for machine learning studies. We will also focus the reason of it.

Repository: https://github.com/serengil/tensorflow-101/

4
Building Deep Neural Networks Classifier for both AND and OR gates

Reusability in TensorFlow

1
Restoring and Working on Already Trained Deep Neural Networks In TensorFlow

In this video, we have mentioned how to re-use already trained neural networks in TensorFlow. Thus, we can make predictions fast even though long learning time required systems.

Repository: https://github.com/serengil/tensorflow-101/

2
Importing Saved TensorFlow DNN Classifier Model in Java

In previous lecture, we've mentioned how to re-use trained neural networks in TensorFlow. 

In this video, we've mentioned how to load and re-use trained TensorFlow neural networks in external higher level systems such as Java. Learning is implemented in TensorFlow whereas predictions are made in Java. TensorFlow also supports to be used in C++.

Repository: https://github.com/serengil/tensorflow-101/

Monitoring and Evaluating

1
Monitoring Model Evaluation Metrics in TensorFlow and TensorBoard

In this lecture, we will mention how to evaluate a machine learning model and commonly used metrics in ML studies. We will also monitor the change of these metrics over learning. And finally, we will focus how to use TensorBoard to monitor these metrics easily.

Repository: https://github.com/serengil/tensorflow-101/

Building regression and time series models

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

Until now, we have built deep neural networks classifiers. Neural networks can also build models for regression studies. Today, we will focus on how to build a deep neural networks regressor in TensorFlow. Sine wave non-linear time series dataset will be used in the study. Finally, we will mention to monitor time series forecasts in TensorBoard.

Repository: https://github.com/serengil/tensorflow-101/

2
Visualizing ML Results with matplotlib and Embedding in TensorBoard

Until now, we have mentioned the out of the box drawing capabilities of TensorFlow and TensorBoard for monitoring. We can also consume python matplotlib library to monitor results of machine learning studies.

Repository: https://github.com/serengil/tensorflow-101/

3
Importing Saved DNNRegressor Model in Java

Building Unsupervised Learning Models

1
Unsupervised learning and k-means clustering with TensorFlow

Even though TensorFlow is developed as a Deep Learning Framework, it is also powerful about other ML algorithms. Today, we will mention how to handle unsupervised learning with TensorFlow. And we will apply k-means clustering algorithm a dataset. Also, we will use matplotlib to visualize clusters.

Repository: https://github.com/serengil/tensorflow-101/

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

In this video, we'll apply k-means clustering algorithm to n-dimensional wine data set in TensorFlow and visualize it in 3D.

Repository: https://github.com/serengil/tensorflow-101/

Tuning Deep Neural Network Models

1
Optimization Algorithms in TensorFlow

In this video, we will apply different optimization algorithms which are Gradient Descent, Adaptive Learning, Momentum and Adam (Adaptive Momentum) in TensorFlow and monitor loss changes and converge speed in TensorBoard.

Repository: https://github.com/serengil/tensorflow-101/

2
Activation Functions in TensorFlow

In this video, we will mention activation functions in deep neural networks. Also, we will focus on that what makes these function common. Finally, we will monitor loss change (mean squared error) for these functions in TensorBoard.

After all, softplus funtion would be winner for xor gate classification among sigmoid, tanh and relu.

Repository: https://github.com/serengil/tensorflow-101/

3
Applying different optimization algorithms while running regressor for sine wave
4
Applying different activation functions for sine wave example

Consuming TensorFlow via Keras

1
Installing Keras

In this video, we will install Keras. TensorFlow installation is expected before installing Keras.

2
Building DNN Classifier with Keras

We will build a keras deep neural networks classifier. Classifier still runs on TensorFlow in background.

3
Storing and restoring a trained neural networks model with Keras

In this video, we'll mention how to store a trained model and how to restore it in Keras

Advanced applications

1
Handwritten Digit Recognition Using Neural Networks

In this lecture, we would build a neural networks model to recognize handwritten digits.

2
Handwritten Digit Recognition Using Convolutional Neural Networks with Keras

Previously, we've applied fully connected neural networks to recognize handwriten digits. As an alternative to this approach, we can use convolutional neural networks (CNN) to do same duty. Training complexity reduces whereas more accurate predictions can be made with CNN.

3
Transfer Learning: Consuming InceptionV3 to Classify Cat and Dog Images in Keras

Keras supports common image classifiers such as Inception, VGG and ResNet. We would consume these high level classifiers' pre-constructed structures and pre-trained weights to classify images as cat or dog.

4
Tips and Tricks for Transfer Learning
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