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 perform poorly: 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'll run through a scenario where you have to identify negative tweets for a celebrity by using Convolutional Neural Networks (CNN's). Next you will create a neural network which will be able to identify smiles in your camera app. Finally, the last project will help you forecast a company's stock prices for the next day using Deep Learning.
By the end of this course, you will have a solid understanding of Deep Learning and the ability to build your own Deep Learning models.
The code bundle for this video course is available at - https://github.com/PacktPublishing/Real-World-Python-Deep-Learning-Projects
- Effectively pre-process data (structured or unstructured) before doing any analysis on the dataset.
- Retrieving data from different data sources (CSV, JSON, Excel, PDF) and parse them in Python to give them a meaningful shape.
- Learn about the amazing data storage places in an industry which are being highly optimized.
- Perform statistical analysis using in-built Python libraries.
- Hacks, tips, and techniques that will be invaluable throughout your Data Science career.
To fully benefit from the coverage included in this course, you will need:
This course is intended for developers, analysts, and data scientists who have a basic machine learning knowledge and want to now explore the possibilities of Deep Learning. Python programming knowledge is essential.
This course has the following software requirements:
Minimum Hardware Requirements
For successful completion of this course, students will require the computer systems with at least the following:
OS: Windows 10
Processor: Intel core i5
Memory: 4 GB
Storage: 256 GB
Recommended Hardware Requirements For an optimal experience with hands-on labs and other practical activities, we recommend the following configuration:
OS: Windows 10
Processor: Intel core i5
Memory: 4 GB
Storage: 256 GB
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 )