TensorFlow 101: Introduction to Deep Learning
4.2 (153 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
4,851 students enrolled

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.
4.2 (153 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
4,851 students enrolled
Last updated 5/2020
English
English
Current price: $69.99 Original price: $99.99 Discount: 30% off
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This course includes
  • 4 hours on-demand video
  • Full lifetime access
  • Access on mobile and TV
  • Assignments
  • Certificate of Completion
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What you'll 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.
  • Informed about tuning machine learning models to produce more successful results
  • Learn how face recognition works
Course content
Expand all 23 lectures 03:55:26
+ Perceptrons
2 lectures 14:01
We've tested perceptron for both AND and OR gates. You are expected to run perceptron for XOR Gate.
Testing regular perceptron for XOR Gate
1 question
+ Introduction
3 lectures 21:25

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

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

Preview 02:43

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/tree/master/course

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
+ 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
+ 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
+ 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
+ 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
+ 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
+ Consuming TensorFlow via Keras
3 lectures 19:38

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

Preview 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

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

Storing and restoring a trained neural networks model with Keras
08:57
+ Advanced applications
6 lectures 01:15:26

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

Handwritten Digit Recognition Using Neural Networks
17:05

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.

Handwritten Digit Recognition Using Convolutional Neural Networks with Keras
18:58

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.

Transfer Learning: Consuming InceptionV3 to Classify Cat and Dog Images in Keras
10:06
Requirements
  • Familiar with machine learning concepts
  • 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. 

We will also focus on the advanced topics in this lecture such as transfer learning, autoencoders, face recognition (including those models: VGG-Face, Google FaceNet, OpenFace and Facebook DeepFace).

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 this course is for:
  • One who interested in Machine Learning, Data Science and AI
  • Anyone who would like to learn TensorFlow framework