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zyphre's Projects

crop-health-detection icon crop-health-detection

Example of plant image classification with a neural network built in numpy using data from https://www.kaggle.com/fpeccia/weed-detection-in-soybean-crops. Original source: https://www.kaggle.com/datduyn/2-layer-net-on-weeds-discriminant/

deep-q-learning-example icon deep-q-learning-example

Example inspired from https://keon.io/deep-q-learning/ to gain a better understanding of deep-q-learning

emotion-classification-ravdess icon emotion-classification-ravdess

Understanding emotions with Neural Networks (Python, Scikit-Learn, Keras) and the Ravdess dataset: 95% accuracy on the training set and 92% on the test set.

flmidi-nihia icon flmidi-nihia

Abstraction layer of the Native Instruments' Host Integration Agent API for the FL Studio MIDI Scripting API

gpt-3 icon gpt-3

GPT-3: Language Models are Few-Shot Learners

keras-stripe-auth-login-webapp icon keras-stripe-auth-login-webapp

Keras-stripe-auth-login-webapp incorporates image classification, user authentication, login functionality, and payment functionality. Heavily inspired by other great repositories, this is my meager attempt to piece them all together into something deployable with render.io

klecks icon klecks

Community funded painting tool powering Kleki.com

open-assistant icon open-assistant

OpenAssistant is a chat-based assistant that understands tasks, can interact with third-party systems, and retrieve information dynamically to do so.

pneumonia-detection-using-inception-v3 icon pneumonia-detection-using-inception-v3

Pneumonia Detection using inception v3, Ensure you have CUDA 10.0, CUDA Tookit, cuDNN SDK (>= 7.4.1) https://www.tensorflow.org/install/gpu#software_requirements

simple-image-classifier icon simple-image-classifier

Simple image classifier web app that uses your webcam. Buttons A,B,C will capture a frame and add it to that classification group. Calibrate None should be used to establish a baseline (no objects present for classification)

speech_signal_processing_and_classification icon speech_signal_processing_and_classification

Front-end speech processing aims at extracting proper features from short- term segments of a speech utterance, known as frames. It is a pre-requisite step toward any pattern recognition problem employing speech or audio (e.g., music). Here, we are interesting in voice disorder classification. That is, to develop two-class classifiers, which can discriminate between utterances of a subject suffering from say vocal fold paralysis and utterances of a healthy subject.The mathematical modeling of the speech production system in humans suggests that an all-pole system function is justified [1-3]. As a consequence, linear prediction coefficients (LPCs) constitute a first choice for modeling the magnitute of the short-term spectrum of speech. LPC-derived cepstral coefficients are guaranteed to discriminate between the system (e.g., vocal tract) contribution and that of the excitation. Taking into account the characteristics of the human ear, the mel-frequency cepstral coefficients (MFCCs) emerged as descriptive features of the speech spectral envelope. Similarly to MFCCs, the perceptual linear prediction coefficients (PLPs) could also be derived. The aforementioned sort of speaking tradi- tional features will be tested against agnostic-features extracted by convolu- tive neural networks (CNNs) (e.g., auto-encoders) [4]. The pattern recognition step will be based on Gaussian Mixture Model based classifiers,K-nearest neighbor classifiers, Bayes classifiers, as well as Deep Neural Networks. The Massachussets Eye and Ear Infirmary Dataset (MEEI-Dataset) [5] will be exploited. At the application level, a library for feature extraction and classification in Python will be developed. Credible publicly available resources will be 1used toward achieving our goal, such as KALDI. Comparisons will be made against [6-8].

webchemy icon webchemy

Cross-Platform Web-App for Sketching - based on Alchemy

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