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Copy file name to clipboardExpand all lines: README.md
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@@ -50,30 +50,35 @@ Developed by **Akshay Anand** as part of PhD research at **Florida State Univers
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This series consists of five interconnected tutorials that guide you from foundational concepts to advanced model deployment:
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1.**Introduction to Tensors**
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- Understanding Tensors
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- Basic Tensor Operations
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- Tensor Manipulation
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2.**Autograd and Automatic Differentiation**
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- Understanding Gradients
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- Computational Graphs
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- Gradient Tracking and Management
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3.**Neural Networks with PyTorch**
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- PyTorch's nn Module
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- Building Neural Network Layers
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- Creating Complete Network Architectures
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4.**Training Models**
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- Loss Functions
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- Optimizers
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- Training Loops
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5.**Saving and Loading Models**
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- Model Serialization
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- Loading Pretrained Models
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- Model Deployment Basics
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### 1. Introduction to Tensors
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- Understanding Tensors
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- Basic Tensor Operations
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- Tensor Manipulation
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-[](https://colab.research.google.com/github/anand-me/deep-learning-with-pytorch-tutorials/blob/main/PyTorchTuto_1.ipynb)
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### 2. Autograd and Automatic Differentiation
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- Understanding Gradients
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- Computational Graphs
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- Gradient Tracking and Management
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-[](https://colab.research.google.com/github/anand-me/deep-learning-with-pytorch-tutorials/blob/main/PyTorchTuto_2.ipynb)
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### 3. Neural Networks with PyTorch
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- PyTorch's nn Module
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- Building Neural Network Layers
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- Creating Complete Network Architectures
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-[](https://colab.research.google.com/github/anand-me/deep-learning-with-pytorch-tutorials/blob/main/PyTorchTuto_3.ipynb)
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### 4. Training Models
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- Loss Functions
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- Optimizers
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- Training Loops
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-[](https://colab.research.google.com/github/anand-me/deep-learning-with-pytorch-tutorials/blob/main/PyTorchTuto_4.ipynb)
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### 5. Saving and Loading Models
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- Model Serialization
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- Loading Pretrained Models
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- Model Deployment Basics
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-[](https://colab.research.google.com/github/anand-me/deep-learning-with-pytorch-tutorials/blob/main/PyTorchTuto_5.ipynb)
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### 🔍 Each tutorial includes:
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### Option 1: Run on Google Colab (Recommended for beginners)
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Each notebook has a "Open in Colab" button at the top that allows you to run the tutorial in your browser without any local setup.
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Each tutorial has an "Open in Colab" badge that allows you to run it directly in your browser:
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| Tutorial | Open in Colab |
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|----------|---------------|
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| 1. Introduction to Tensors |[](https://colab.research.google.com/github/anand-me/deep-learning-with-pytorch-tutorials/blob/main/PyTorchTuto_1.ipynb)|
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| 2. Autograd and Automatic Differentiation |[](https://colab.research.google.com/github/anand-me/deep-learning-with-pytorch-tutorials/blob/main/PyTorchTuto_2.ipynb)|
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| 3. Neural Networks with PyTorch |[](https://colab.research.google.com/github/anand-me/deep-learning-with-pytorch-tutorials/blob/main/PyTorchTuto_3.ipynb)|
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| 4. Training Models |[](https://colab.research.google.com/github/anand-me/deep-learning-with-pytorch-tutorials/blob/main/PyTorchTuto_4.ipynb)|
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| 5. Saving and Loading Models |[](https://colab.research.google.com/github/anand-me/deep-learning-with-pytorch-tutorials/blob/main/PyTorchTuto_5.ipynb)|
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