#Welcome to my journey of learning PyTorch! This Blog documents my progress, insights, and projects as I delve into the world of deep learning using PyTorch.
Table of Contents
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Introduction
I am currently exploring PyTorch, a powerful deep learning library developed by Facebook’s AI Research lab. My goal is to gain a solid understanding of its capabilities, build machine learning models, and eventually contribute to real-world AI projects.
Learning Goals
- Understand the basics of PyTorch, including tensors, autograd, and neural networks.
- Implement basic machine learning algorithms from scratch.
- Explore and use pre-trained models for various tasks.
- Gain hands-on experience with PyTorch’s neural network modules.
- Experiment with custom models for specific tasks, including handwriting recognition.
Progress Tracker
| Date | Topic | Notes |
|---|---|---|
| 2024-01-01 | Introduction to Tensors | Learned about tensors and basic operations. |
| 2024-09-05 | Autograd and Backpropagation | Explored how PyTorch handles gradients and automatic differentiation. |
| 2024-09-10 | Neural Networks | Built a simple neural network for classification tasks. |
| 2024-09-15 | CNNs and Image Classification | Implemented a CNN for image classification using the CIFAR-10 dataset. |
Source: Link
Projects
- Image to Text Converter: A Flask application that converts images to text. I’m working on integrating a custom model trained in PyTorch to recognize handwritten text.
Resources
- Official PyTorch Documentation
- Deep Learning with PyTorch: A 60 Minute Blitz
- CS50P - For foundational knowledge.
Future Plans
- Advanced Model Training: Delve deeper into training custom models with larger datasets.
- PyTorch Lightning: Explore this high-level wrapper to streamline the training process.
- Contribution: Contribute to open-source PyTorch projects and share my work with the community.
Contact: ekrishnachaitanya2004@gmail.com ‘or’ snakesnnetworks@gmail.com