Deep Learning for Python Developers [Video]

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Deep Learning for Python Developers [Video]

Will Ballard

Wednesday, December 26, 2018

New Release!

Use your Python skills to build powerful Deep Learning applications

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Video Details

ISBN 13 9781788993883

Course Length 2 hours 11 minutes

Getting Started with Deep Learning

The Course Overview

Fundamentals of Neural Networks

Training Deep Neural Networks

Using Forward Propagation, Backprop, and SGD

Logistic Regression with a Neural Network Mindset

Convolutional Neural Network Handwriting Recognition

Deep Models with MxNet and TensorFlow

Working with MxNet and Gluon

Defining and Training Neural Networks in MxNet/Gluon

Working with TensorFlow and Keras

Defining and Training Neural Networks in Keras/TensorFlow

Comparing the Two Frameworks

Mini Project - CIFAR Classification

Improving Deep Neural Networks

Weight Initialization for Deep Networks

Regularization and Dropout

Normalizing and Vanishing/Exploding Gradients

Mini Project – SIGNS Dataset

Optimization Algorithms

Understanding Stochastic Gradient Descent

Adaptive Learning Algorithms - RMSProp and Adam

Mini Project - Language Modeling

Hyperparameter Tuning

Hyperparameters

Tuning Hyperparameters - Grid Search

Tuning Hyperparameters - Random Search

Mini Project -Music Synthesis

Video Description

Deep learning is a new superpower which will let you build AI systems that just weren't possible a few years ago. It's time to utilize intelligent automation to help your business grow, keep organized, and stay on top of the competition.

This course is for Python developers who haven't worked with machine learning or data science, and want to build intelligent systems right away—without taking a math degree! You will learn about recurrent neural networks, Backprop, SGD, and more. You will work on code examples that are used in a developer's life on a daily basis; you'll not only master the theory, you'll also see how to applied it in the industry as a whole. You will practice all these ideas in MxNet, TensorFlow, Keras, and Gluon. Last but not the least, build Convolutional Neural Networks and apply them to image data.

Deep Learning is currently enabling numerous exciting applications in speech recognition, music synthesis, machine translation, natural language understanding, and many others. AI is transforming multiple industries. After finishing this course, you will likely find creative ways to apply it to your work. We will help you master Deep Learning, understand how to apply it, and build a career in AI.

All the code and supporting files for this course are available on GitHub at https://github.com/PacktPublishing/Deep-Learning-for-Python-Developers

Style and Approach

This video course adopts a practical approach and covers the basics of Deep Learning: perceptrons, activation functions, layering, backpropagation, and more. Each essential concept is immediately followed by hands-on labs which will implement the concept in code and show the practical aspects of Deep Learning theory. In the later sections, when covering deployment and best practices, a mini-project is implemented as a running example and brings together all the material learned before and applies it in a practical manner.

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What You Will Learn

Write software that reads handwriting, classifies images by what's in them, and decodes messages in sign language—even if you've never done machine learning before.
Build Deep Learning models that work at a much faster rate, never sleep, and get your tasks done quickly and efficiently
Develop an appreciation of Deep Learning models that are used in large scale, distributed settings in cloud computing
Apply your sequence models to natural language problems such as including text synthesis and audio applications, speech recognition, and music synthesis.
Build Convolutional Neural Networks, including recent variations such as residual networks
Understand the strengths and weaknesses of various frameworks and when it is best to apply them
Use intelligent automation to avoid errors and save the time normally spent fixing them.

Authors

Will Ballard

Will Ballard is the chief technology officer at GLG, responsible for engineering and IT. He was also responsible for the design and operation of large data centers that helped run site services for customers including Gannett, Hearst Magazines, NFL, NPR, The Washington Post, and Whole Foods. He has also held leadership roles in software development at NetSolve (now Cisco), NetSpend, and Works (now Bank of America).

Getting Started with Deep Learning

The Course Overview

Fundamentals of Neural Networks

Training Deep Neural Networks

Using Forward Propagation, Backprop, and SGD

Logistic Regression with a Neural Network Mindset

Convolutional Neural Network Handwriting Recognition

Deep Models with MxNet and TensorFlow

Working with MxNet and Gluon

Defining and Training Neural Networks in MxNet/Gluon

Working with TensorFlow and Keras

Defining and Training Neural Networks in Keras/TensorFlow