Tensor Micrograd: NumPy Autodiff Extension

🔑 Enhanced Key Takeaways

• •Micrograd++ (parsiad/micrograd-pp), another NumPy-based extension of micrograd, supports tensor inputs and includes examples for training feedforward networks on MNIST, n-gram models, and decoder-only transformers[3].
• •Tensorgrad (hkxIron/tensorgrad) implements a PyTorch-like API with NumPy tensors, supporting operations like addition, multiplication, powering, and training 2-layer MLPs on datasets like moon for binary classification[6].
• •MyGrad provides a NumPy-array-like Tensor class with full autodiff support, including broadcasting, views with memory sharing, and access to underlying .data array, designed as a drop-in replacement for NumPy[2].

🛠️ Technical Deep Dive

• •Supports tensor operations including +, -, *, /, **, matmul (@), reductions (sum, mean), and elementwise functions (ReLU, sigmoid, tanh) with backward functions accumulating gradients via chain rule[5][6].
• •Tensors maintain computational graph by storing parent inputs; backward pass uses reverse-mode autodiff over dynamically built DAG, similar to PyTorch[3][5][6].
• •Examples include 2-layer MLP with 16-node hidden layers using SGD and sigmoid cross-entropy loss, achieving decision boundaries on moon dataset[6].
• •Tensor views share memory like NumPy (e.g., slicing creates views with np.shares_memory=True), and .data provides direct access to underlying ndarray[2].

🔮 Future ImplicationsAI analysis grounded in cited sources

⏳ Timeline