by Will Ballard | Released December 2018 | ISBN: 9781788993883

Use your Python skills to build powerful Deep Learning applicationsAbout This VideoGet up and running with Deep Learning and build complex models layer by layer with increasing complexityExplore the art of applying your Deep Learning models to image recognition, Natural Language Processing problems, and voice applicationsWork with TensorFlow, MxNet and higher-level libraries such as Keras and Gluon and understand the full life cycle: defining, training, testing, and deploying your Deep Learning models.In DetailDeep 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-DevelopersDownloading the example code for this course: You can download the example code files for all Packt video courses you have purchased from your account at http://www.PacktPub.com. If you purchased this course elsewhere, you can visit http://www.PacktPub.com/support and register to have the files e-mailed directly to you. Show and hide more

1. Chapter 1 : Getting Started with Deep Learning
   • The Course Overview 00:03:20
   • Fundamentals of Neural Networks 00:06:17
   • Training Deep Neural Networks 00:04:37
   • Using Forward Propagation, Backprop, and SGD 00:04:44
   • Logistic Regression with a Neural Network Mindset 00:04:16
   • Convolutional Neural Network Handwriting Recognition 00:07:08
2. Chapter 2 : Deep Models with MxNet and TensorFlow
   • Working with MxNet and Gluon 00:06:20
   • Defining and Training Neural Networks in MxNet/Gluon 00:07:22
   • Working with TensorFlow and Keras 00:05:21
   • Defining and Training Neural Networks in Keras/TensorFlow 00:04:38
   • Comparing the Two Frameworks 00:03:04
   • Mini Project - CIFAR Classification 00:12:01
3. Chapter 3 : Improving Deep Neural Networks
   • Weight Initialization for Deep Networks 00:03:35
   • Regularization and Dropout 00:03:26
   • Normalizing and Vanishing/Exploding Gradients 00:03:29
   • Mini Project – SIGNS Dataset 00:07:50
4. Chapter 4 : Optimization Algorithms
   • Understanding Stochastic Gradient Descent 00:06:28
   • Adaptive Learning Algorithms - RMSProp and Adam 00:04:05
   • Mini Project - Language Modeling 00:09:05
5. Chapter 5 : Hyperparameter Tuning
   • Hyperparameters 00:04:23
   • Tuning Hyperparameters - Grid Search 00:04:37
   • Tuning Hyperparameters - Random Search 00:04:50
   • Mini Project - Music Synthesis 00:10:45
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