Deep learning with MXNet cookbook discover an extensive collection of recipes for creating and implementing AI models on MXNet
Gain practical, recipe-based insights into the world of deep learning using Apache MXNet for flexible and efficient research prototyping, training, and deployment to production Key Features Create scalable deep learning applications using MXNet products with step-by-step tutorials Implement tasks su...
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| Format: | eBook |
| Language: | English |
| Published: |
Birmingham
Packt Publishing
2023
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| Edition: | 1st edition |
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| Online Access: | |
| Collection: | O'Reilly - Collection details see MPG.ReNa |
Table of Contents:
- Cover
- Title Page
- Copyright and Credits
- Foreword
- Contributors
- Table of Contents
- Preface
- Chapter 1: Up and Running with MXNet
- Technical requirements
- Installing MXNet, Gluon, GluonCV, and GluonNLP
- Getting ready
- How to do it...
- How it works...
- There's more...
- NumPy and MXNet ND arrays
- Getting ready
- How to do it...
- How it works...
- There's more...
- Chapter 2: Working with MXNet and Visualizing Datasets
- Gluon and DataLoader
- Technical requirements
- Understanding regression datasets
- loading, managing, and visualizing the House Sales dataset
- Getting ready
- How to do it...
- How it works...
- There's more...
- Understanding classification datasets
- loading, managing, and visualizing the Iris dataset
- Getting ready
- How to do it...
- How it works...
- There's more...
- Understanding image datasets
- loading, managing, and visualizing the Fashion-MNIST dataset
- Getting ready
- How to do it...
- How it works...
- There's more...
- Understanding text datasets
- loading, managing, and visualizing the Enron Email dataset
- Getting ready
- How to do it...
- How it works...
- There's more...
- Chapter 3: Solving Regression Problems
- Technical requirements
- Understanding the math of regression models
- Getting ready
- How to do it...
- How it works...
- There's more...
- Defining loss functions and evaluation metrics for regression
- Getting ready
- How to do it...
- How it works...
- There's more...
- Training regression models
- Getting ready
- How to do it...
- How it works...
- There's more...
- Evaluating regression models
- Getting ready
- How to do it...
- How it works...
- There's more...
- Chapter 4: Solving Classification Problems
- Technical requirements
- Understanding math for classification models
- Getting ready
- How to do it
- How it works...
- There's more...
- Defining loss functions and evaluation metrics for classification
- Getting ready
- How to do it...
- How it works...
- There's more...
- Training for classification models
- Getting ready
- How to do it...
- How it works...
- There's more...
- Evaluating classification models
- Getting ready
- How to do it...
- How it works...
- There's more...
- Chapter 5: Analyzing Images with Computer Vision
- Technical requirements
- Understanding convolutional neural networks
- Getting ready
- How to do it...
- How it works...
- There's more...
- Classifying images with MXNet
- GluonCV Model Zoo, AlexNet, and ResNet
- Getting ready
- How to do it...
- How it works...
- There's more...
- Detecting objects with MXNet
- Faster R-CNN and YOLO
- Getting ready
- How to do it...
- How it works...
- There's more...
- Segmenting objects in images with MXNet
- PSPNet and DeepLab-v3
- Getting ready
- How to do it...
- How it works...
- There's more...
- Chapter 6: Understanding Text with Natural Language Processing
- Technical requirements
- Introducing NLP networks
- Getting ready
- How to do it...
- Introducing Recurrent Neural Networks (RNNs)
- Improving RNNs with Long Short-Term Memory (LSTM)
- Introducing GluonNLP Model Zoo
- Paying attention with Transformers
- How it works...
- There's more...
- Classifying news highlights with topic modeling
- Getting ready
- How to do it...
- How it works...
- There's more...
- Analyzing sentiment in movie reviews
- Getting ready
- How to do it...
- How it works...
- There's more...
- Translating text from Vietnamese to English
- Getting ready
- How to do it...
- How it works...
- There's more...
- Chapter 7: Optimizing Models with Transfer Learning and Fine-Tuning
- Technical requirements
- Understanding transfer learning and fine-tuning
- Getting ready
- How to do it...
- How it works...
- There's more...
- Improving performance for classifying images
- Getting ready
- How to do it...
- Revisiting the ImageNet-1k and Dogs vs Cats datasets
- How it works...
- There's more...
- Improving performance for segmenting images
- Getting ready
- How to do it...
- How it works...
- There's more...
- Improving performance for translating English to German
- Getting ready
- How to do it...
- How it works...
- There's more...
- Chapter 8: Improving Training Performance with MXNet
- Technical requirements
- Introducing training optimization features
- Getting ready
- How to do it...
- How it works...
- There's more...
- Optimizing training for image segmentation
- Getting ready
- How to do it...
- How it works...
- There's more...
- Optimizing training for translating text from English to German
- Getting ready
- How to do it...
- How it works...
- There's more...
- Chapter 9: Improving Inference Performance with MXNet
- Technical requirements
- Introducing inference optimization features
- Getting ready
- How to do it...
- How it works...
- There's more...
- Optimizing inference for image segmentation
- Getting ready
- How to do it...
- How it works...
- There's more...
- Optimizing inference when translating text from English to German
- Getting ready
- How to do it...
- How it works...
- There's more...
- Index
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