deep-learning-with-pytorch/dlwpt-code
5,224 Stars·2,155 Forks·Jupyter Notebook·10 Aufrufewww.manning.com/books/deep-learning-with-pytorch↗
Dlwpt Code
Dieses Projekt ist eine Bildungsressource für Deep Learning, bestehend aus PyTorch-Modellimplementierungen und Codebeispielen. Es bietet funktionale Python-Skripte und Notebooks für den Aufbau, das Training und die Optimierung neuronaler Netzwerke mittels Tensor-basierter Berechnung.
Das Repository enthält Implementierungen für den Entwurf benutzerdefinierter Netzwerkschichten und Verlustfunktionen sowie Beispiele für Transfer-Learning-Workflows, die vortrainierte Modellgewichte laden, um die Entwicklung zu beschleunigen.
Die Codebasis deckt ein breites Spektrum an Deep-Learning-Funktionen ab, einschließlich Training neuronaler Netzwerke, Design benutzerdefinierter Modellkomponenten und die Implementierung mehrschichtiger Architekturen zur Erkennung komplexer Muster in Datensätzen.
Features
- Deep Learning Models - Provides deep learning model implementations and architectures for recognizing complex patterns in datasets.
- PyTorch Deep Learning Examples - Offers a collection of PyTorch implementations and neural network architectures to demonstrate deep learning concepts.
- Automatic Differentiation Engines - Implements the core automatic differentiation engine used to compute gradients for model weight updates.
- Dynamic Graph Frameworks - Utilizes dynamic graph frameworks to allow network structures to change at runtime based on input data.
- Deep Learning Reference Implementations - Translates theoretical deep learning concepts into functional reference implementations in Python.
- Weight Optimizers - Provides weight optimizers that iteratively adjust parameters using loss functions to minimize error.
- Custom Layer and Loss Implementations - Implements custom network layers and loss functions to control data flow and measure accuracy.
- Modular Neural Network Design - Employs modular neural network design to organize architectures into reusable components and layers.
- Neural Network Design Frameworks - Provides tools and abstractions for the structural design of custom neural network architectures.
- Neural Network Training - Provides Python scripts for building and optimizing multi-layer neural network architectures using gradient-based methods.
- Neural Network Training Frameworks - Provides a framework for building and training multi-layer neural network architectures.
- Neural Network Training Toolkits - Offers a toolkit for the iterative development and optimization of neural network training.
- PyTorch Implementations - Implements functional neural network models and algorithms using the PyTorch framework for educational purposes.
- PyTorch Model Components - Implements modular and reusable PyTorch model components, including custom layers and loss functions.
- PyTorch Model Development - Provides comprehensive PyTorch implementations for building and training deep learning models.
- Tensor Computation Backends - Relies on tensor computation backends for efficient linear algebra and mathematical operations on multi-dimensional arrays.
- Deep Learning Education - Serves as an educational resource for learning neural network theory through practical code examples.
- Linear Algebra Routines - Implements fundamental linear algebra routines for performing operations on multi-dimensional tensors.
- Pretrained Model Integrations - Integrates pretrained models to leverage existing weights and accelerate the development process.
- Model State Serialization - Implements model state serialization to save and load trained network weights via binary files.
- Transfer Learning Implementations - Provides practical implementations and code examples for applying transfer learning techniques.
- Pretrained Weight Initializers - Includes pretrained weight initializers to accelerate model development through transfer learning.
- Weight Serialization - Provides weight serialization for exporting internal tensor values to portable files.
