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 |
|---|---|---|
| ELU Activation | Apply the ELU activation element-wise, retaining positive inputs and exponentially transforming negative values. | https://www.tensortonic.com/problems/elu-activation |
| Implement Leaky ReLU (with α) | Apply Leaky ReLU element-wise with a configurable negative slope while retaining positive inputs. | https://www.tensortonic.com/problems/leaky-relu |
| Implement Min-Max Normalization | Normalize each NumPy feature to the zero-to-one range with explicit handling for constant columns. | https://www.tensortonic.com/problems/minmax-normalization |
| 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 |
| 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 Tanh Activation | Implement the hyperbolic tangent activation element-wise with outputs bounded between minus one and one. | https://www.tensortonic.com/problems/tanh-activation |
| Word Count Dictionary | Count token occurrences in text and return a dictionary mapping each distinct word to its frequency. | https://www.tensortonic.com/problems/word-count-dict |
View my verified ML profile: TensorTonic profile