|
Published:
May 14, 2019

Deep Learning For Dummies

Overview

Take a deep dive into deep learning

Deep learning provides the means for discerning patterns in the data that drive online business and social media outlets. Deep Learning for Dummies gives you the information you need to take the mystery out of the topic—and all of the underlying technologies associated with it.

In no time, you’ll make sense of those increasingly confusing algorithms, and find a simple and safe environment to experiment with deep learning. The book develops a sense

of precisely what deep learning can do at a high level and then provides examples of the major deep learning application types.

  • Includes sample code
  • Provides real-world examples within the approachable text
  • Offers hands-on activities to make learning easier
  • Shows you how to use Deep Learning more effectively with the right tools

This book is perfect for those who want to better understand the basis of the underlying technologies that we use each and every day.

Read More

About The Author

John Paul Mueller is the author of over 100 books including AI for Dummies, Python for Data Science for Dummies, Machine Learning for Dummies, and Algorithms for Dummies. Luca Massaron is a data scientist who interprets big data and transforms it into smart data by means of the simplest and most effective data mining and machine learning techniques. He is a Google Developer Expert (GDE) in machine learning.

Sample Chapters

deep learning for dummies

CHEAT SHEET

Deep learning affects every area of your life — everything from smartphone use to diagnostics received from your doctor. Python is an incredible programming language that you can use to perform deep learning tasks with a minimum of effort. By combining the huge number of available libraries with Python-friendly frameworks, you can avoid writing the low-level code normally needed to create deep learning applications.

HAVE THIS BOOK?

Articles from
the book

This article is too short. It can’t even begin to describe the ways in which deep learning will affect you in the future. Consider this article to be offering a tantalizing tidbit — an appetizer that can whet your appetite for exploring the world of deep learning further.These deep learning applications are already common in some cases.
There are a lot of different uses for deep learning — everything from the voice-activated features of your digital assistant to self-driving cars. Using deep learning to improve your daily life is nice, of course, but most people need other reasons to embrace a technology, such as getting a job. Fortunately, deep learning doesn’t just affect your ability to locate information faster but also offers some really interesting job opportunities, and with the “wow” factor that only deep learning can provide.
Deep learning affects every area of your life — everything from smartphone use to diagnostics received from your doctor. Python is an incredible programming language that you can use to perform deep learning tasks with a minimum of effort. By combining the huge number of available libraries with Python-friendly frameworks, you can avoid writing the low-level code normally needed to create deep learning applications.
As a simplification, you can view language as a sequence of words made of letters (as well as punctuation marks, symbols, emoticons, and so on). Deep learning processes language best by using layers of RNNs, such as LSTM or GRU. However, knowing to use RNNs doesn't tell you how to use sequences as inputs; you need to determine the kind of sequences.
Neural networks provide a transformation of your input into a desired output. Even in deep learning, the process is the same, although the transformation is more complex. In contrast to a simpler neural network made up of few layers, deep learning relies on more layers to perform complex transformations. The output from a data source connects to the input layer of the neural network, and the input layer starts processing the data.
Convolutional neural networks (CNN) are the building blocks of deep learning–based image recognition, yet they answer only a basic classification need: Given a picture, they can determine whether its content can be associated with a specific image class learned through previous examples. Therefore, when you train a deep neural network to recognize dogs and cats, you can feed it a photo and obtain output that tells you whether the photo contains a dog or cat.
Machine learning is an application of AI that can automatically learn and improve from experience without being explicitly programmed to do so. The machine learning occurs as a result of analyzing ever increasing amounts of data, so the basic algorithms don’t change, but the code's internal weights and biases used to select a particular answer do.
Given the embarrassment of riches that pertain to AI as a whole, such as large amounts of data, new and powerful computational hardware available to everyone, and plenty of private and public investments, you may be skeptical about the technology behind deep learning, which consists of neural networks that have more neurons and hidden layers than in the past.
Once you know how neural networks basically work, you need a better understanding of what differentiates them to understand their role in deep learning. Beyond the different neural network architectures, the choice of the activation functions, optimizers and the neural network's learning rate can make the difference.
Sentiment analysis computationally derives from a written text using the writer’s attitude (whether positive, negative, or neutral), toward the text topic. This kind of analysis proves useful for people working in marketing and communication because it helps them understand what customers and consumers think of a product or service and thus, act appropriately (for instance, trying to recover unsatisfied customers or deciding to use a different sales strategy).
What is deep learning? Deep learning is a subcategory of machine learning. With both deep learning and machine learning, algorithms seem as though they are learning. This is accomplished when the algorithms analyze huge amounts of data and then take actions or perform a function based on the derived information.
https://cdn.prod.website-files.com/6630d85d73068bc09c7c436c/69195ee32d5c606051d9f433_4.%20All%20For%20You.mp3

Frequently Asked Questions

No items found.