This repository encapsulates in-depth explorations and implementations centered around advanced TensorFlow techniques, architectures, and customizations. Diving deep into the functional API, custom training routines, sophisticated computer vision techniques, and generative models, the contents within provide a comprehensive look at cutting-edge neural network paradigms.
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🔧 Custom Models, Layers, and Loss Functions
- Explored the depths of TensorFlow's functional API to craft custom models tailored for specific tasks.
- Designed and implemented custom layers to introduce novel functionalities into neural networks.
- Formulated unique loss functions to cater to specific optimization challenges.
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🚄 Custom and Distributed Training Techniques
- Engineered bespoke training loops, allowing for granular control over the training process.
- Integrated advanced optimizers and fine-tuned training routines for optimal model performance.
- Delved into distributed training methods to harness the power of multiple GPUs and TPUs, ensuring efficient large-scale model training.
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👁 Advanced Computer Vision Techniques
- Implemented state-of-the-art architectures for a multitude of vision tasks, ranging from object detection to image segmentation.
- Explored and applied advanced techniques like attention mechanisms within vision models.
- Developed models capable of handling diverse image data, ranging from standard RGB photos to more complex multi-channel inputs.
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🎨 Generative Deep Learning Paradigms
- Designed and trained various GAN architectures, exploring both foundational GANs and their numerous variants.
- Investigated autoencoders, variational autoencoders (VAEs), and their applications in unsupervised learning and data generation.
- Pushed the boundaries of generative models, exploring text-to-image synthesis, style transfer, and other cutting-edge applications.
| Library/Tool | Usage |
|---|---|
| TensorFlow (2.x) | Primary deep learning framework |
| TensorFlow Extended (TFX) | Tools to help deploy TensorFlow models |
| TensorFlow Hub | Reusable machine learning modules |
| TensorFlow Lite | Deployment on mobile and edge devices |
| Local GPU clusters | High-performance training |
| Google Cloud TPUs | Distributed training on cloud-based tensor processing units |
To set up and run any of the experiments or implementations:
git clone https://github.com/AshyScripts/advanced-tensorflow.git
cd advanced-tensorflow
# Follow specific instructions within each module or scriptThis repository is an evolving testament to my deep dive into advanced neural network techniques. Constructive feedback, suggestions, or contributions to enhance these implementations are highly valued and welcome.