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Neural Network Projects with Python

Neural Network Projects with Python

By : James Loy
4.6 (15)
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Neural Network Projects with Python

Neural Network Projects with Python

4.6 (15)
By: James Loy

Overview of this book

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. It contains practical demonstrations of neural networks in domains such as fare prediction, image classification, sentiment analysis, and more. In each case, the book provides a problem statement, the specific neural network architecture required to tackle that problem, the reasoning behind the algorithm used, and the associated Python code to implement the solution from scratch. In the process, you will gain hands-on experience with using popular Python libraries such as Keras to build and train your own neural networks from scratch. By the end of this book, you will have mastered the different neural network architectures and created cutting-edge AI projects in Python that will immediately strengthen your machine learning portfolio.
Table of Contents (10 chapters)
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Sequential problems in machine learning

Sequential problems are a class of problem in machine learning in which the order of the features presented to the model is important for making predictions. Sequential problems are commonly encountered in the following scenarios:

  • NLP, including sentiment analysis, language translation, and text prediction
  • Time series predictions

For example, let's consider the text prediction problem, as shown in the following screenshot, which falls under NLP:

Human beings have an innate ability for this, and it is trivial for us to know that the word in the blank is probably the word Japanese. The reason for this is that as we read the sentence, we process the words as a sequence. The sequence of the words captures the information required to make the prediction. By contrast, if we discard the sequential information and only consider the words...

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