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Deep Learning with Python

Dive into the future of data science and implement intelligent systems using deep learning with Python
3.4 (29 ratings)
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Last updated 3/2016
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  • 2 hours on-demand video
  • 1 Supplemental Resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What Will I Learn?
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
View Curriculum
  • It is for senior undergrad and first year grad students beginning in the field of deep learning.

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.

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.
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Curriculum For This Course
Expand All 19 Lectures Collapse All 19 Lectures 01:45:44
Head First into Deep Learning
4 Lectures 20:26

This video will provide an overview of the course.

Preview 03:51

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

What Is Deep Learning?

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

Open Source Libraries for Deep Learning

How to get our first deep neural network trained?

Deep Learning "Hello World!" Classifying the MNIST Data
Backpropagation and Theano for the Rescue
3 Lectures 18:21

How are neural networks trained?

Preview 05:23

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

Understanding Deep Learning with Theano

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

Optimizing a Simple Model in Pure Theano
Keras – Making Theano Even Easier to Use
3 Lectures 16:50

How does Keras work?

Preview 05:24

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

Fully Connected or Dense Layers

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

Convolutional and Pooling Layers
Solving Cats Versus Dogs
3 Lectures 17:54

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

Preview 05:17

How does Keras work?

Loading Pre-trained Models with Theano

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

Reusing Pre-trained Models in New Applications
"for" Loops and Recurrent Neural Networks in Theano
4 Lectures 22:18

How to write a loop in Theano?

Preview 05:18

How can one define neural network layers with internal states?

Recurrent Layers

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

Recurrent Versus Convolutional Layers

How can we classify sentiments from text?

Recurrent Networks –Training a Sentiment Analysis Model for Text
Bonus Challenge and TensorFlow
2 Lectures 09:55

How can we automatically describe an image in English?

Preview 04:40

What is TensorFlow?

Captioning TensorFlow – Google's Machine Learning Library
About the Instructor
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