TensorFlow 101: Introduction to Deep Learning
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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 for Machine Learning.
New
4.1 (25 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
3,921 students enrolled
Last updated 9/2017
English
English
Current price: $10 Original price: $120 Discount: 92% off
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Includes:
  • 2.5 hours on-demand video
  • Full lifetime access
  • Access on mobile and TV
  • Assignments
  • Certificate of Completion
What Will I Learn?
  • 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
View Curriculum
Requirements
  • This course appeals to ones who is familiar with machine learning concepts, and would like to learn TensorFlow
  • Basic Python
Description

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.

Who is the target audience?
  • One who interested in Machine Learning, Data Science and AI
  • Anyone who would like to learn TensorFlow framework
Compare to Other TensorFlow Courses
Curriculum For This Course
14 Lectures
02:29:25
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Introduction
2 Lectures 18:42

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.

Preview 06:03

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/

Preview 12:39

You are expected to build deep neural networks classifier for both AND and OR gates
Building Deep Neural Networks Classifier for both AND and OR gates
1 question
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Reusability in TensorFlow
2 Lectures 26:41

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/

Restoring and Working on Already Trained Deep Neural Networks In TensorFlow
10:00

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/

Importing Saved TensorFlow DNN Classifier Model in Java
16:41
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Monitoring and Evaluating
1 Lecture 14:03

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/

Monitoring Model Evaluation Metrics in TensorFlow and TensorBoard
14:03
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Building regression and time series models
2 Lectures 30:53

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/

Building a DNN Regressor for Non-Linear Time Series in TensorFlow
21:19

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/

Visualizing ML Results with matplotlib and Embedding in TensorBoard
09:34

Reuse trained deep neural networks regressor model in Java
Importing Saved DNNRegressor Model in Java
1 question
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Building Unsupervised Learning Models
2 Lectures 17:40

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/

Unsupervised learning and k-means clustering with TensorFlow
12:05

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/

Applying k-means clustering to n-dimensional datasets in TensorFlow
05:35
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Tuning Deep Neural Network Models
2 Lectures 15:39

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/

Optimization Algorithms in TensorFlow
07:48

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/

Activation Functions in TensorFlow
07:51

You are expected to apply different optimization algorithms for sine wave dnn regressor
Applying different optimization algorithms while running regressor for sine wave
1 question

In this section, we've applied differet activation functions for XOR example. Implement same duty for sine wave example and monitor.
Applying different activation functions for sine wave example
1 question
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Real World Applications
1 Lecture 15:06

Deep neural networks is modeled for detecting handwritten digits

Handwritten Digit Classification (MNIST)
15:06
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Consuming TensorFlow via Keras
2 Lectures 10:41

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

Installing Keras
03:35

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

Building DNN Classifier with Keras
07:06
About the Instructor
Sefik Ilkin Serengil
4.1 Average rating
25 Reviews
3,921 Students
1 Course
Software Developer, Data Scientist

This is Sefik.

I received my MSc in Computer Science from Galatasaray University in 2011.

I have been working as a software developer for a Finance IT company since 2010. Currently, I am member of AI team as a Data Scientist.

My current research interests are Machine Learning and Cryptography. I’ve published several research papers about these motivations. Also, I enjoy speaking to communities about these disciplines.

BTW, I am blogging and creating online courses related to my research interest.