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W tym poście chciałbym przybliżyć temat statystyki dla tych osób które nie miały z nią do czynienia, ale także tym którzy posługują się pewnymi narzędziami ale niewiedzą jaka intuicja za nimi stoi. Ten post będzie być może początkiem dłuższej serii o tym jak ja rozumiem tę dziedzinę matematyki. Read more
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This thesis describes the problem of generating jazz music chords using recurrent neural networks (RNN) and numerical vector representations - embeddings. To create the embedding I use techniques known from the field of natural language processing (NLP). It is possible because of certain structural similarities between spoken language and music. I study the relationship between the chords in the hidden space generated by the algorithms: Word2Vec, FastText and Multi-hot. Visualization of chords in reduced vector representation space using t-SNE and PCA algorithms illustrates a lot of dependencies between the chords derived from the principles of jazz harmony. The test results confirmed the highest performance of chord generation when using the Word2Vec model in the Skip-Gram variant. To generate chords I use their vector representation. I analyze the performance of sixteen models based on the recursive neural networks in terms of their hyper parameters and architecture. I also study the impact of the algorithm used to create the numerical representation on the results of recurrent models. To train neural networks I use a set of jazz standards obtained from publicly available Internet sources in a format allowing for the extraction of chord sequences. The experiments shows the highest performance of deep models with recurrent layers - GRU and LSTM, but the one with the shortest runtime is the model with two GRU layers, Dropout regularization layer and Dense layer. I use trained models to build a simple program capable of generating continuous chord sequences in the unsupervised variant and with partially controlling this generation by the user. For implementation of all algorithms, visualization and data processing I use Python language and its libraries: tensorflow, keras, gensim, scikit-learn, numpy, pychord, music21 etc. Read more
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Notes that I’ve made with help of professors and collegues during my Erasmus Exchange in University of Barcelona. Read more
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This repository contains simple python implementation of genetic algorithm. There heuristic algorithm searches the space for alternative solutions to the problem in order to find the best solutions. Read more
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In this post I want to introduce you to chatbots. What is a chatbot? How do they work? And how you can create them? You can find here both, technical descriptions and business solutions. Enjoy! Read more
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Short explanation of what is feature extraction and how it differs from other data preprocessing techniques in area of Natural Language Processing. Read more
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I had a pleasure to star in Daftcode clip, telling my story to encourage young people to develop their passions. I hope you’ll enjoy. Read more
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Classify questions to 4 different classes, using NLP methods and ML tools for classification. See it on GitHub Read more
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I’ve won Hack UPC “Use of AI in real-life cases” challenge. My team created a web app that makes your travel safer using ML model predicting severity of accidents. Read more
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Hack UPC 2019 ML Report for McKinsey. Predicting damage inflicted in traffic accidents. Read more
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Implementation of DCGAN music generating software using it’s image format - piano roll. It’s a part of my diploma thesis. I’ve presented its results on ICAISC 2019 in Zakopane. Read more
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Published in Wydział Elektroniki i Technik Infromacyjnych, Politechnika Warszawska, 2018
PL: Artykuł opisuje zastosowanie sieci neuronowych w generowaniu muzyki, zawiera także przegląd dostępnych technologii oraz opis istniejących architektur sieci neuronowych, które znalazłyby zastosowanie w tej dziedzinie. Read more
Published in , 2019
PL: Praca omawia zagadnienie generowania muzyki z wykorzystaniem głębokich splotowych generatywnych sieci przeciwstawnych (DCGAN). Read more
Published in International Conference on Artificial Intelligence and Soft Computing, 2019
This paper describes method for generating original musical phrases using Deep Convolutional Generative Adversarial Networks (DCGAN) based on image representation of music - piano roll. Read more
Published in , 2020
This thesis describes the problem of generating jazz music chords using recurrent neural networks (RNN) and numerical vector representations - embeddings. To create the embedding I use techniques known from the field of natural language processing (NLP). It is possible because of certain structural similarities between spoken language and music. I study the relationship between the chords in the hidden space generated by the algorithms: Word2Vec, FastText and Multi-hot. Visualization of chords in reduced vector representation space using t-SNE and PCA algorithms illustrates a lot of dependencies between the chords derived from the principles of jazz harmony. The test results confirmed the highest performance of chord generation when using the Word2Vec model in the Skip-Gram variant. To generate chords I use their vector representation. I analyze the performance of sixteen models based on the recursive neural networks in terms of their hyper parameters and architecture. I also study the impact of the algorithm used to create the numerical representation on the results of recurrent models. To train neural networks I use a set of jazz standards obtained from publicly available Internet sources in a format allowing for the extraction of chord sequences. The experiments shows the highest performance of deep models with recurrent layers - GRU and LSTM, but the one with the shortest runtime is the model with two GRU layers, Dropout regularization layer and Dense layer. I use trained models to build a simple program capable of generating continuous chord sequences in the unsupervised variant and with partially controlling this generation by the user. For implementation of all algorithms, visualization and data processing I use Python language and its libraries: tensorflow, keras, gensim, scikit-learn, numpy, pychord, music21 etc. Read more
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PL: Moje wystąpienie na PyWaw#84 na którym opowiadałem o Generowaniu muzyki z użyciem sieci neuronowych EN: My presentation at PyWaw#84, where I was talking about music generation with neural networks. Read more
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PL: Warsztaty z Uczenia Maszynowego na wydziale MINI Politechniki Warszawskiej - 28 maja 2019 EN: Workshop on Machine Learning on faculty of Mathematics Warsaw University of Technology - 28 may 2019 Read more
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A presentation of my master thesis project using language models to represent Jazz Chords and Recurrent Neural Networks to generate jazz chords progressions. Event was held by Python Barcelona. Read more
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Wystąpiłem jako prelegent na konferencji Data Science Summit 2020. Jest to jedno z największych i najważniejszych polskich wydarzeń data science w Polsce. Agenda DSS20 jest bardzo bogata (120+ wystąpień, 15 ścieżek) - każdy znajdzie coś dla siebie (od przetwarzania, poprzez analizę i uczenie maszynowe, aż po wizualizację danych). Read more
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ML in PL 2020 odbyło się w formie online. Moim zdaniem najlepiej zorganizowane wydarzenie AI 2020 roku. Kudos dla organizatorów. Read more
Machine Learning Workshops Materials.
Workshop on Machine Learning at the Faculty of Mathematics, Warsaw University of Technology - 28 May 2019. Run the notebook from workshops here: Read more
Python Beginner to Intermediate Course materials and tasks for my students.
I use Python in my everyday work as a tool for building applications, web scraping, artificial intelligence, music, and many other interesting things. My Python-Course repository is a handy database with learning materials and tasks for my students. Read more