Convolutional Neural Networks In Python: Beginner's Guide To Convolutional Neural Networks In Python
Convolutional Neural Networks In Python: Beginner's Guide To Convolutional Neural Networks In Python by Frank Millstein
English | March 5, 2018 | ASIN: B07B8PQ8SJ | 121 pages | AZW3 | 0.30 MB
English | March 5, 2018 | ASIN: B07B8PQ8SJ | 121 pages | AZW3 | 0.30 MB
This book covers the basics behind Convolutional Neural Networks by introducing you to this complex world of deep learning and artificial neural networks in a simple and easy to understand way. It is perfect for any beginner out there looking forward to learning more about this machine learning field.
This book is all about how to use convolutional neural networks for various image, object and other common classification problems in Python. Here, we also take a deeper look into various Keras layer used for building CNNs we take a look at different activation functions and much more, which will eventually lead you to creating highly accurate models able of performing great task results on various image classification, object classification and other problems.
Therefore, at the end of the book, you will have a better insight into this world, thus you will be more than prepared to deal with more complex and challenging tasks on your own.
Here Is a Preview of What You’ll Learn In This Book…
- Convolutional neural networks structure
- How convolutional neural networks actually work
- Convolutional neural networks applications
- The importance of convolution operator
- Different convolutional neural networks layers and their importance
- Arrangement of spatial parameters
- How and when to use stride and zero-padding
- Method of parameter sharing
- Matrix multiplication and its importance
- Pooling and dense layers
- Introducing non-linearity relu activation function
- How to train your convolutional neural network models using backpropagation
- How and why to apply dropout
- CNN model training process
- How to build a convolutional neural network
- Generating predictions and calculating loss functions
- How to train and evaluate your MNIST classifier
- How to build a simple image classification CNN
- And much, much more!