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 Adam Optimizer Step | Implement one vectorized Adam optimizer step in NumPy with first and second moments, bias correction, and elementwise parameter updates. | https://www.tensortonic.com/problems/adam-optimizer |
| Bag-of-Words Vector | Build a NumPy bag-of-words count vector from an ordered vocabulary while ignoring out-of-vocabulary tokens. | https://www.tensortonic.com/problems/bag-of-words |
| Implement BM25 Ranking Score | Implement BM25 document ranking with term frequency saturation, inverse document frequency, and length normalization. | https://www.tensortonic.com/problems/bm25 |
| Implement Dot Product | Implement the dot product of equal-length numeric vectors by summing element-wise products without library shortcuts. | https://www.tensortonic.com/problems/dot-product |
| Edit Distance | Compute Levenshtein edit distance between two strings using dynamic programming over insertions, deletions, and substitutions. | https://www.tensortonic.com/problems/edit-distance |
| Implement Gradient Descent for a 1D Quadratic | Optimize a one-dimensional quadratic with iterative gradient descent and return the parameter trajectory. | https://www.tensortonic.com/problems/gradient-descent-quadratic |
| Build a Mini GRU Cell (Forward Pass) | Implement a GRU cell forward pass with reset, update, and candidate gates for one sequence timestep. | https://www.tensortonic.com/problems/gru-cell-forward |
| Matrix Transpose | Implement matrix transpose in NumPy without built-in transpose helpers, preserving rectangular shapes and the original input. | https://www.tensortonic.com/problems/matrix-transpose |
| Pad Sequences | Pad or truncate variable-length token ID sequences in NumPy with configurable maximum length and padding values. | https://www.tensortonic.com/problems/pad-sequences |
| Remove Stopwords | Remove tokens found in a supplied stopword collection while preserving the order of remaining words. | https://www.tensortonic.com/problems/remove-stopwords |
| RMSProp Optimizer (Single Update Step) | Implement one RMSProp update in NumPy using an exponential squared-gradient average and adaptive scaling. | https://www.tensortonic.com/problems/rmsprop-optimizer |
| 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 |
| Text Chunking | Split text into ordered chunks under the requested size and overlap rules without dropping content. | https://www.tensortonic.com/problems/text-chunking |
| Implement TF-IDF Vectorizer | Build TF-IDF document vectors from token counts and inverse document frequency across a text corpus. | https://www.tensortonic.com/problems/tfidf-vectorizer |
| 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