In the preceding steps, given that the filter moved one step at a time both horizontally and vertically, the strides for the filter are (1, 1). The higher the number of strides, the higher the number of values that are skipped from the matrix multiplication.
- Title Page
- Copyright and Credits
- Dedication
- About Packt
- Contributors
- Preface
- Building a Feedforward Neural Network
- Building a Deep Feedforward Neural Network
- Training a vanilla neural network
- Scaling the input dataset
- Impact on training when the majority of inputs are greater than zero
- Impact of batch size on model accuracy
- Building a deep neural network to improve network accuracy
- Varying the learning rate to improve network accuracy
- Varying the loss optimizer to improve network accuracy
- Understanding the scenario of overfitting
- Speeding up the training process using batch normalization
- Applications of Deep Feedforward Neural Networks
- Building a Deep Convolutional Neural Network
- Transfer Learning
- Gender classification of the person in an image using CNNs
- Gender classification of the person in image using the VGG16 architecture-based model
- Visualizing the output of the intermediate layers of a neural network
- Gender classification of the person in image using the VGG19 architecture-based model
- Gender classification using the Inception v3 architecture-based model
- Gender classification of the person in image using the ResNet 50 architecture-based model
- Detecting the key points within image of a face
- Detecting and Localizing Objects in Images
- Introduction
- Creating the dataset for a bounding box
- Generating region proposals within an image, using selective search
- Calculating an intersection over a union between two images
- Detecting objects, using region proposal-based CNN
- Performing non-max suppression
- Detecting a person using an anchor box-based algorithm
- Image Analysis Applications in Self-Driving Cars
- Image Generation
- Introduction
- Generating images that can fool a neural network using adversarial attack
- DeepDream algorithm to generate images
- Neural style transfer between images
- Generating images of digits using Generative Adversarial Networks
- Generating images using a Deep Convolutional GAN
- Face generation using a Deep Convolutional GAN
- Face transition from one to another
- Performing vector arithmetic on generated images
- Encoding Inputs
- Text Analysis Using Word Vectors
- Introduction
- Building a word vector from scratch in Python
- Building a word vector using the skip-gram and CBOW models
- Performing vector arithmetic using pre-trained word vectors
- Creating a document vector
- Building word vectors using fastText
- Building word vectors using GloVe
- Building sentiment classification using word vectors
- Building a Recurrent Neural Network
- Applications of a Many-to-One Architecture RNN
- Sequence-to-Sequence Learning
- End-to-End Learning
- Audio Analysis
- Reinforcement Learning
- Other Books You May Enjoy
Strides
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