Deep Learning with TensorFlow
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Deep Learning with TensorFlow

Channel the power of deep learning with Google's TensorFlow!
3.6 (159 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.
1,671 students enrolled
Created by Packt Publishing
Last updated 7/2017
English
Current price: $10 Original price: $130 Discount: 92% off
5 hours left at this price!
30-Day Money-Back Guarantee
Includes:
  • 2 hours on-demand video
  • 1 Supplemental Resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • Set up your computing environment and install TensorFlow
  • Build simple TensorFlow graphs for everyday computations
  • Apply logistic regression for classification with TensorFlow
  • Design and train a multilayer neural network with TensorFlow
  • Understand intuitively convolutional neural networks for image recognition
  • Bootstrap a neural network from simple to more accurate models
  • See how to use TensorFlow with other types of networks
  • Program networks with SciKit-Flow, a high-level interface to TensorFlow
View Curriculum
Requirements
  • Some familiarity with C++ or Python is assumed.
Description

Deep learning is the intersection of statistics, artificial intelligence, and data to build accurate models and TensorFlow is one of the newest and most comprehensive libraries for implementing deep learning. With deep learning going mainstream, making sense of data and getting accurate results using deep networks is possible. This course is your guide to exploring the possibilities with deep learning; it will enable you to understand data like never before. With the efficiency and simplicity of TensorFlow, you will be able to process your data and gain insights that will change how you look at data.

With this video course, you will dig your teeth deeper into the hidden layers of abstraction using raw data. This course will offer you various complex algorithms for deep learning and various examples that use these deep neural networks. You will also learn how to train your machine to craft new features to make sense of deeper layers of data. During the video course, you will come across topics such as logistic regression, convolutional neural networks, recurrent neural networks, training deep networks, high level interfaces, and more.

With the help of novel practical examples, you will become an ace at advanced multilayer networks, image recognition, and beyond.

About The Author

Dan Van Boxel is a Data Scientist and Machine Learning Engineer with over 10 years of experience. He is most well-known for "Dan Does Data," a YouTube livestream demonstrating the power and pitfalls of neural networks. He has developed and applied novel statistical models of machine learning to topics such as accounting for truck traffic on highways, travel time outlier detection, and other areas. Dan has also published research and presented findings at the Transportation Research Board and other academic journals.

Who is the target audience?
  • If you are a data scientist who performs machine learning on a regular basis, are familiar with deep neural networks, and now want to gain expertise working with convoluted neural networks, then this course is for you.
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Curriculum For This Course
22 Lectures
02:00:12
+
Getting Started
5 Lectures 25:54

This video gives an overview of the entire course.

Preview 02:59

TensorFlow is a new machine library that is probably not installed on our operating system by default. This video will guide you through installing TensorFlow locally or remotely.

Installing TensorFlow
05:33

Before we can use TensorFlow for deep learning, we need to understand how TensorFlow handles basic objects and operations. This video will walk you through a few computations.

Simple Computations
05:31

Learning any library from documentation can be challenging, so we're going to build a practical machine learning classifier with TensorFlow. We'll start with a simple logistic regression classifier and build up from there. 

Logistic Regression Model Building
06:58

Though we have a classifier, we need to compute weights so that our model is accurate. For this, we can use TensorFlow to specify and optimize a loss function. TensorFlow will then use this to find good weights. 

Logistic Regression Training
04:53
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Deep Neural Networks
5 Lectures 24:56

Using single pixels as features limits us to model essentially linear phenomena. To model non-linear things such as font styles involving several pixels, we will use neural networks to transform our inputs into non-linear combinations for use in a logistic regression classifier. 

Preview 05:14

Now that we understand neural networks, we can see how TensorFlow makes them easy to implement and train. 

Single Hidden Layer Model
05:05

Now that we've trained a neural network, we should inspect it closely to understand the accuracy and weights. 

Single Hidden Layer Explained
04:32

A single hidden layer is good, but you may find the number of neurons growing prohibitive in order to model very complex features. To combine features more easily, we expand the network in depth rather than width. It's true deep learning with multiple hidden layers. 

Multiple Hidden Layer Model
05:22

With our deep neural net trained, we should take time to check its accuracy and understand the features it's extracting. 

Multiple Hidden Layer Results
04:43
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Convolutional Neural Networks
7 Lectures 35:44

Particularly in images, the features that we want to find can occur anywhere among the pixels. Convolutional neural nets allow us to train one set of weights to search small windows of an image for a feature. 

Preview 05:03

Understanding the theory of convolutional layers is useless without learning the tools to actually use them. This video walks through a simple example with TensorFlow. 

Convolutional Layer Application
06:55

Convolutions can find a feature anywhere in an image, but with all the overlap, we need to make sure we don't find the same feature in the same place multiple times. A pooling layer reduces the size of our input, taking only relevant information. 

Pooling Layer Motivation
03:58

Having learned how Max Pooling works in theory, it's time to put it into practice by adding it to our simple example in TensorFlow. 

Pooling Layer Application
04:17

We've learned about convolutional layers and used them in an example, but now let's use them for real by adding a convolutional layer to the font classification model. 

Deep CNN
06:28

Convolutional layers often work well when chained together. Let's add another to our font classification model. 

Deeper CNN
04:08

After our deep model has trained for a model, it's time to see how well it performs. 

Wrapping Up Deep CNN
04:55
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Recurrent Neural Networks
3 Lectures 22:25

Some problems have time-based inputs. Features from the recent past might matter to the current prediction. To address these, researchers have developed recurrent neural networks. TensorFlow natively supports these. 

Preview 09:02

TensorFlow models can be cumbersome to specify, yet follow a common pattern. skflow provides a simple interface for typical models. 

skflow
09:19

RNNs can be hard to specify, but skflow will let us quickly build a model. 

RNNs in skflow
04:04
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Wrapping Up
2 Lectures 11:13

After learning so many methods and building all these models, it's helpful to look back and see how far we've come. 

Preview 06:55

TensorFlow is changing very quickly and is being adopted by more researchers and professionals. But at its core are the contributions submitted by new users. 

The Future of TensorFlow
04:18
About the Instructor
Packt Publishing
3.9 Average rating
7,264 Reviews
51,798 Students
616 Courses
Tech Knowledge in Motion

Packt has been committed to developer learning since 2004. A lot has changed in software since then - but Packt has remained responsive to these changes, continuing to look forward at the trends and tools defining the way we work and live. And how to put them to work.

With an extensive library of content - more than 4000 books and video courses -Packt's mission is to help developers stay relevant in a rapidly changing world. From new web frameworks and programming languages, to cutting edge data analytics, and DevOps, Packt takes software professionals in every field to what's important to them now.

From skills that will help you to develop and future proof your career to immediate solutions to every day tech challenges, Packt is a go-to resource to make you a better, smarter developer.

Packt Udemy courses continue this tradition, bringing you comprehensive yet concise video courses straight from the experts.