This is the code repository for Neural Network Projects with Python, published by Packt.
The ultimate guide to using Python to explore the true power of neural networks through six projects
Neural networks are at the core of recent AI advances, providing some of the best resolutions to many real-world problems, including image recognition, medical diagnosis, text analysis, and more. This book goes through some basic neural network and deep learning concepts, as well as some popular libraries in Python for implementing them.
This book covers the following exciting features:
- Learn various neural network architectures and its advancements in AI
- Master deep learning in Python by building and training neural network
- Master neural networks for regression and classification
- Discover convolutional neural networks for image recognition
- Learn sentiment analysis on textual data using Long Short-Term Memory
If you feel this book is for you, get your copy today!
All of the code is organized into folders. For example, Chapter02.
The code will look like the following:
def detect_faces(img, draw_box=True):
# convert image to grayscale
grayscale_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
Following is what you need for this book: This book is a perfect match for data scientists, machine learning engineers, and deep learning enthusiasts who wish to create practical neural network projects in Python. Readers should already have some basic knowledge of machine learning and neural networks.
With the following software and hardware list you can run all code files present in the book (Chapter 1-7).
| Chapter | Software required | OS required |
|---|---|---|
| 1-7 | Python, Jupyter Notebook | Windows, Mac OS X, and Linux (Any) |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
James Loy has more than five years, expert experience in data science in the finance and healthcare industries. He has worked with the largest bank in Singapore to drive innovation and improve customer loyalty through predictive analytics. He has also experience in the healthcare sector, where he applied data analytics to improve decision-making in hospitals. He has a master's degree in computer science from Georgia Tech, with a specialization in machine learning.
His research interest includes deep learning and applied machine learning, as well as developing computer-vision-based AI agents for automation in industry. He writes on Towards Data Science, a popular machine learning website with more than 3 million views per month.
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