Key Features

• Discover neural network architectures, such as CNNs and LSTMs that are driving recent advancements in AI
• Build expert neural networks in Python using popular libraries such as Keras
• Explore projects such as object detection, face identification, and sentiment analysis

Book Description

Neural networks are at the core of recent artificial intelligence (AI) advances, providing some of the best solutions to many real-world problems, such as image recognition, medical diagnosis, and text analysis. This book takes you through the fundamental neural network and deep learning concepts, as well as popular Python libraries for implementing them.

You will discover practical demonstrations of neural networks in domains such as fare prediction, image classification, and sentiment analysis. In each case, the book will provide a problem statement, the specific neural network architecture required to tackle that problem, the reasoning behind the algorithm used, and even the associated Python code to implement the solution from scratch. In the process, you will gain hands-on experience in using popular Python libraries such as Keras to build and train your own neural networks from scratch.

By the end of this book, you’ll be well-versed with the different neural network architectures and have developed a variety of AI projects in Python that will strengthen your machine learning portfolio.

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