Deep Learning in python
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Updated
May 22, 2024 - Jupyter Notebook
Deep Learning in python
This repository contains notes, slides, labs, assignments and projects for the Deep Learning Specialization by DeepLearning.AI and Coursera.
I constructed a knowledge graph of stakeholders of Bavarian state ministries and used network analysis to calculate statistics. Furthermore time-series feature forecasting and topological link prediction was employed to analyze the evolution of the network.
Next–Generation Intrusion Detection for IoT EVCS: Integrating CNN, LSTM, and GRU Models
Participants in this Specialization have the opportunity to construct and train various neural network architectures, including Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, and Transformers. They learn to enhance these networks with techniques such as Dropout, BatchNorm, Xavier/He initialization, among others.
Microsoft Stock Deep Learning Model Prediction and Story Timeline Analysis.
Deep Learning Machine Learning Templates
Introductory-Gru
Hotel review sentiment analysis using GRU (Gated Recurrent Unit) model
Predict the next 24 hours' temperatures by GRU and Transformer
CoreML compatible GRU neural network for dynamic prediction
Implementation of the model inversion attack on the Gated-Recurrent-Unit neural network
STM32F429 Online handwritten character classification with Gated Recurrent Unit Neural Network
Codes for EEE 443 Neural Networks Projects
Recurrent neural network with GRUs for trigger word detection from an audio clip
This repository includes course assignments of Natural Language Processing in TensorFlow on Coursera by DeepLearning.AI
Named Entity Recognition (NER) with different combinations of BiGRU, Self-Attention and CRF
Implementation of LSTM time series tuned with GRU.
Code for Multi-dimensional Gated Recurrent Units for the Segmentation of Biomedical Data
Sequence Models repository for all projects and programming assignments of Course 5 of 5 of the Deep Learning Specialization offered on Coursera and taught by Andrew Ng, covering topics such as Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), Long Short Term Memory (LSTM), Natural Language Processing, Word Embeddings and Attention Model.
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