Welcome to my TensorTonic solutions repository!
Here you'll find my solutions to various machine learning and deep learning problems from TensorTonic.
TensorTonic is a platform where you can implement core algorithms of Machine Learning from scratch.
This repository contains my personal solutions to these problems, automatically synchronized from the platform.
Verified machine learning implementations completed on TensorTonic.
| Problem | Description | Link |
|---|---|---|
| Implement GELU Activation (Gaussian Error Linear Unit) | Implement the Gaussian Error Linear Unit activation element-wise using the required GELU approximation. | https://www.tensortonic.com/problems/gelu |
| Implement ReLU Activation | Apply the ReLU activation element-wise by replacing negative values with zero and preserving nonnegative inputs. | https://www.tensortonic.com/problems/relu-activation |
| SELU Activation | Apply SELU activation element-wise with scaled positive values and exponential negative values. | https://www.tensortonic.com/problems/selu-activation |
| Implement Sigmoid in NumPy | Implement a vectorized sigmoid activation in NumPy for scalars, lists, vectors, and matrices, including large positive and negative inputs. | https://www.tensortonic.com/problems/sigmoid-numpy |
| Implement Softmax Function | Implement numerically stable softmax by shifting logits before exponentiation and normalizing probabilities. | https://www.tensortonic.com/problems/softmax-function |
| Scaled Dot-Product Attention | Implement scaled dot-product attention in PyTorch using query-key scores, softmax weights, and value aggregation. | https://www.tensortonic.com/research/transformer/transformers-attention |
| Embedding Layer | Create PyTorch token embeddings and scale each lookup by the square root of the Transformer model dimension. | https://www.tensortonic.com/research/transformer/transformers-embedding |
View my verified ML profile: TensorTonic profile