This is the code repository for Real-World Python Deep Learning Projects [Video], published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.
Deep learning allows you to solve problems where traditional Machine Learning methods might have poor performance.Detecting and extracting objects from images, extracting meaning from text and predicting outcomes based on complex dependencies to name a few. In this course you will learn how to use deep learning in practice by going through real-world examples.
You will start of by creating neural networks to predict the demand for airline travel in the future. Then, you would run through a scenario where you have to identifying negative tweets for a celebrity by using Convolutional Neural Networks(CNN’s). Next we will create a neural network which will be able to identify smiles in your camera app. Finally, the last project will help you forecast the stock prices of a company for the next day using deep learning.
By the end of this course you will get a solid understanding of deep learning and the ability to build your own deep learning models.
- Build a solid understanding of common problems can you solve with Deep Learning
- Use different Deep Learning algorithms to solve specific types of problem and learn their strengths and weaknesses,
- Develop a clear understanding of how Deep Learning tools work and what you need to know to use them in practice
- Discover the practical pros and cons of using Deep Learning
- Save time by learning practical Deep Learning methods that you can immediately apply to real-world problems.
To fully benefit from the coverage included in this course, you will need:
To fully benefit from the coverage included in this course, you will need:
• Working Python knowledge
• The basics of Machine Learning
This course has the following software requirements:
This course has the following software requirements:
• Python 3.6 (https://www.python.org/downloads/)
• Anaconda for Python 3.6 version (https://www.anaconda.com/download/)
• Tensorflow (https://www.tensorflow.org/install/install_windows)
• Scikit-learn
• Keras
• Python package: keras (installed from command prompt using the following commands: “conda install -c conda-forge keras )
This course has been tested on the following system configuration:
• OS: macOS High Sierra
• Processor: 1,3 GHz Intel Core 5
• Memory: 4 GB
• Storage: 121 GBry
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