Deep Learning with Python

Dive into the future of data science and implement intelligent systems using deep learning with Python
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  • Lectures 19
  • Length 2 hours
  • Skill Level Beginner Level
  • Languages English
  • Includes Lifetime access
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    Available on iOS and Android
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About This Course

Published 3/2016 English

Course Description

Deep learning is currently one of the best providers of solutions regarding problems in image recognition, speech recognition, object recognition, and natural language with its increasing number of libraries that are available in Python. The aim of deep learning is to develop deep neural networks by increasing and improving the number of training layers for each network, so that a machine learns more about the data until it’s as accurate as possible. Developers can avail the techniques provided by deep learning to accomplish complex machine learning tasks, and train AI networks to develop deep levels of perceptual recognition.

Deep learning is the next step to machine learning with a more advanced implementation. Currently, it’s not established as an industry standard, but is heading in that direction and brings a strong promise of being a game changer when dealing with raw unstructured data. Deep learning is currently one of the best providers of solutions regarding problems in image recognition, speech recognition, object recognition, and natural language processing. Developers can avail the benefits of building AI programs that, instead of using hand coded rules, learn from examples how to solve complicated tasks. With deep learning being used by many data scientists, deeper neural networks are evaluated for accurate results.

This course takes you from basic calculus knowledge to understanding backpropagation and its application for training in neural networks for deep learning and understand automatic differentiation. Through the course, we will cover thorough training in convolutional, recurrent neural networks and build up the theory that focuses on supervised learning and integrate into your product offerings such as search, image recognition, and object processing. Also, we will examine the performance of the sentimental analysis model and will conclude with the introduction of Tensorflow.

By the end of this course, you can start working with deep learning right away. This course will make you confident about its implementation in your current work as well as further research.

About The Author

Eder Santana is a PhD candidate on Electrical and Computer Engineering. His thesis topic is on Deep and Recurrent neural networks. After working for 3 years with Kernel Machines (SVMs, Information Theoretic Learning, and so on), Eder moved to the field of deep learning 2.5 years ago, when he started learning Theano, Caffe, and other machine learning frameworks. Now, Eder contributes to Keras: Deep Learning Library for Python. Besides deep learning, he also likes data visualization and teaching machine learning, either on online forums or as a teacher assistant.

What are the requirements?

  • It is for senior undergrad and first year grad students beginning in the field of deep learning.

What am I going to get from this course?

  • Get a quick brief about backpropagation
  • Perceive and understand automatic differentiation with Theano
  • Exhibit the powerful mechanism of seamless CPU and GPU usage with Theano
  • Understand the usage and innards of Keras to beautify your neural network designs
  • Apply convolutional neural networks for image analysis
  • Discover the methods of image classification and harness object recognition using deep learning
  • Get to know recurrent neural networks for the textual sentimental analysis model

Who is the target audience?

  • This course is for developers who are looking for free, open source deep learning solutions for media (image and text) classification.

What you get with this course?

Not for you? No problem.
30 day money back guarantee.

Forever yours.
Lifetime access.

Learn on the go.
Desktop, iOS and Android.

Get rewarded.
Certificate of completion.

Curriculum

Section 1: Head First into Deep Learning
03:51

This video will provide an overview of the course.

04:08

What is Deep Learning, and when is it the way to go?

04:30

How to avoid programming Deep Learning from scratch? Let’s take a look at it in this video.

07:57

How to get our first deep neural network trained?

Section 2: Backpropagation and Theano for the Rescue
05:23

How are neural networks trained?

05:04

How can we avoid making a differentiation of functions and make backpropagation easier?

07:54

How do Keras and other libraries use Theano work behind the scenes?

Section 3: Keras – Making Theano Even Easier to Use
05:24

How does Keras work?

04:46

How does Keras work? How does one write a basic, fully connected neural network layer in Keras?

06:40

Understand what convolutional neural networks are and how to use them. How can we write convolutional layers with Python?

Section 4: Solving Cats Versus Dogs
05:17

How can we solve complex image datasets (for example, cats versus dogs) without training a full model from scratch?

05:15

How does Keras work?

07:22

We will solve complete image datasets with pretrained models: classifying cats versus dogs.

Section 5: "for" Loops and Recurrent Neural Networks in Theano
05:18

How to write a loop in Theano?

06:28

How can one define neural network layers with internal states?

03:42

Recurrent or convolutional: How can one know which layer they should use?

06:50

How can we classify sentiments from text?

Section 6: Bonus Challenge and TensorFlow
04:40

How can we automatically describe an image in English?

05:15

What is TensorFlow?

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Instructor Biography

Packt Publishing, 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.



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