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Neural Network Projects with Python: The ultimate guide to using Python to explore the true power of neural networks through six projects
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About Neural Network Projects With Python: The Ultimate
Product Description Build your machine learning portfolio by developing six cutting-edge artificial intelligence projects using neural networks in Python 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. What you will learn Train an autoencoder from scratch and use it to remove noise from images Understand deep learning in Python by building and training neural networks Get up to speed with neural networks for regression and classification Apply convolutional neural networks (CNNs) for image recognition Learn sentiment analysis on textual data using long short-term memory (LSTM) Build and train a highly accurate facial recognition security system Who this book is for This book is for data scientists, machine learning engineers, and deep learning enthusiasts who want to develop practical neural network projects in Python. Basic knowledge of machine learning and neural networks is required to get the most out of this book. Table of Contents Machine Learning and Neural Networks 101 Predicting Diabetes with Multilayer Perceptrons Predicting Taxi Fares with Deep Feedforward Networks Cats Versus Dogs - Image Classification Using CNNs Removing Noise from Images Using Autoencoders Sentiment Analysis of Movie Reviews Using LSTM Implementing a Facial Recognition System with Neural Networks What's Next? About the Author 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.
