<div><div><div>Deploy deep learning applications into production across multiple platforms. You will work on computer vision applications that use the convolutional neural network (CNN) deep learning model and Python.&nbsp;This book<i>&nbsp;</i>starts by explaining the traditional machine-learning pipeline, where you will analyze an image dataset. Along the way you will cover artificial neural net...

1. Introduction 2. Recognition in Computer Vision 3. Artificial Neural Network 4. Classification using ANN with Engineered Features 5. ANN Parameters Optimization 6. Convolutional Neural Networks 7. TensorFlow Recognition Application 8. Deploying Pre-Trained Models 9. Cross-Platform Data Science Applications.Appendix: Uploading Projects to PyPI

Deploy deep learning applications into production across multiple platforms. You will work on computer vision applications that use the convolutional neural network (CNN) deep learning model and Python. This book starts by explaining the traditional machine-learning pipeline, where you will analyze an image dataset. Along the way you will cover artificial neural networks (ANNs), building one from ...

1. Introduction 2. Recognition in Computer Vision 3. Artificial Neural Network 4. Classification using ANN with Engineered Features 5. ANN Parameters Optimization 6. Convolutional Neural Networks 7. TensorFlow Recognition Application 8. Deploying Pre-Trained Models 9. Cross-Platform Data Science Applications.Appendix: Uploading Projects to PyPI

1. Introduction 2. Recognition in Computer Vision 3. Artificial Neural Network 4. Classification using ANN with Engineered Features 5. ANN Parameters Optimization 6. Convolutional Neural Networks 7. TensorFlow Recognition Application 8. Deploying Pre-Trained Models 9. Cross-Platform Data Science Applications.Appendix: Uploading Projects to PyPI

1. Introduction 2. Recognition in Computer Vision 3. Artificial Neural Network 4. Classification using ANN with Engineered Features 5. ANN Parameters Optimization 6. Convolutional Neural Networks 7. TensorFlow Recognition Application 8. Deploying Pre-Trained Models 9. Cross-Platform Data Science Applications.Appendix: Uploading Projects to PyPI

Deploy deep learning applications into production across multiple platforms. You will work on computer vision applications that use the convolutional neural network (CNN) deep learning model and Python. This book starts by explaining the traditional machine-learning pipeline, where you will analyze an image dataset. Along the way you will cover artificial neural networks (ANNs), building one from ...

1. Introduction 2. Recognition in Computer Vision 3. Artificial Neural Network 4. Classification using ANN with Engineered Features 5. ANN Parameters Optimization 6. Convolutional Neural Networks 7. TensorFlow Recognition Application 8. Deploying Pre-Trained Models 9. Cross-Platform Data Science Applications.Appendix: Uploading Projects to PyPI