Deep Learning with Python [Video]

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Deep Learning with Python [Video]

Eder Santana

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Dive into the future of data science and implement intelligent systems using deep learning with Python

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Video Details

ISBN 139781785883873
Course Length1 hour and 45 minutes

Video 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.

Style and Approach

An easy-to-follow and structured video tutorial with practical examples and coding with IPython notebooks to help you get to grips with each and every aspect of deep learning.

Table of Contents

Head First into Deep Learning
The Course Overview
What Is Deep Learning?
Open Source Libraries for Deep Learning
Deep Learning "Hello World!" Classifying the MNIST Data
Backpropagation and Theano for the Rescue
Introduction to Backpropagation
Understanding Deep Learning with Theano
Optimizing a Simple Model in Pure Theano
Keras – Making Theano Even Easier to Use
Keras Behind the Scenes
Fully Connected or Dense Layers
Convolutional and Pooling Layers
Solving Cats Versus Dogs
Large Scale Datasets, ImageNet, and Very Deep Neural Networks
Loading Pre-trained Models with Theano
Reusing Pre-trained Models in New Applications
"for" Loops and Recurrent Neural Networks in Theano
Theano "for" Loops – the "scan" Module
Recurrent Layers
Recurrent Versus Convolutional Layers
Recurrent Networks –Training a Sentiment Analysis Model for Text
Bonus Challenge and TensorFlow
Bonus Challenge – Automatic Image Captioning
Captioning TensorFlow – Google's Machine Learning Library

What You Will 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

Authors

Screenshots

Table of Contents

Head First into Deep Learning
The Course Overview
What Is Deep Learning?
Open Source Libraries for Deep Learning
Deep Learning "Hello World!" Classifying the MNIST Data
Backpropagation and Theano for the Rescue
Introduction to Backpropagation
Understanding Deep Learning with Theano
Optimizing a Simple Model in Pure Theano
Keras – Making Theano Even Easier to Use
Keras Behind the Scenes
Fully Connected or Dense Layers
Convolutional and Pooling Layers
Solving Cats Versus Dogs
Large Scale Datasets, ImageNet, and Very Deep Neural Networks
Loading Pre-trained Models with Theano
Reusing Pre-trained Models in New Applications
"for" Loops and Recurrent Neural Networks in Theano
Theano "for" Loops – the "scan" Module
Recurrent Layers
Recurrent Versus Convolutional Layers
Recurrent Networks –Training a Sentiment Analysis Model for Text
Bonus Challenge and TensorFlow
Bonus Challenge – Automatic Image Captioning
Captioning TensorFlow – Google's Machine Learning Library

Video Details

ISBN 139781785883873
Course Length1 hour and 45 minutes
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