A2ZP30

Packt Applied Deep Learning with Keras

Packt Applied Deep Learning with Keras

Added Date: September 13, 2019

Downloads: 1986

Free

Packt Applied Deep Learning with Keras / Specification

Manufacturer: Packt Publishing Link to
English Name: Packt Applied Deep Learning with Keras
Name Cross Learning Deep Learning
Language Of Instruction:English
Teacher: Matthew Moocarme, Mahla Abdolahnejad, Et al
Educational Level:Primary, Secondary
Training Time:4 Hours + 49 Minutes
Size:11000 MB
Release Date:01:48|2019.09.13
Rating: 2.5 Star(s) 2.5/5

Packt Applied Deep Learning with Keras / Screenshots

Packt Applied Deep Learning with Keras
Packt Applied Deep Learning with Keras
Packt Applied Deep Learning with Keras
Packt Applied Deep Learning with Keras
Packt Applied Deep Learning with Keras

Packt Applied Deep Learning with Keras / Description

Understand the difference between single-layer and multi-layer neural network models
Use Keras to build simple logistic regression models, deep neural networks, recurrent neural networks, and convolutional neural networks
Apply L1, L2, and dropout regularization to improve the accuracy of your model
Implement cross-validate using Keras wrappers with scikit-learn
Understand the limitations of model accuracy
About
Though designing neural networks is a sought-after skill, it is not easy to master. With Keras, you can apply complex machine learning algorithms with minimum code.

Applied Deep Learning with Keras starts by taking you through the basics of machine learning and Python all the way to gaining an in-depth understanding of applying Keras to develop efficient deep learning solutions. To help you grasp the difference between machine and deep learning, the course guides you on how to build a logistic regression model, first with scikit-learn and then with Keras. You will delve into Keras and its many models by creating prediction models for various real-world scenarios, such as disease prediction and customer churning. You’ll gain knowledge on how to evaluate, optimize, and improve your models to achieve maximum information. Next, you’ll learn to evaluate your model by cross-validating it using Keras Wrapper and scikit-learn. Following this, you’ll proceed to understand how to apply L1, L2, and dropout regularization techniques to improve the accuracy of your model. To help maintain accuracy, you’ll get to grips with applying techniques including null accuracy, precision, and AUC-ROC score techniques for fine tuning your model.


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