How can we reproduce the results reported in CNN research papers?

CNN-LSTM: An efficient hybrid deep learning architecture for predicting short-term photovoltaic power production. Electric Power Systems Research, 2022, 208 ... With the development of deep learning, object detection has achieved very promising results. There are several algorithms and models, such as R-CNN, Faster R- ... A convolutional neural network (CNN or ConvNet) is a network architecture for deep learning that learns directly from data. Jul 21, 2022 ... Predicting wind power generation using machine learning and CNN-LSTM approaches ... Hyperparameters are adjusted using all the machine learning ... Feb 10, 2020 ... The main novelty of this study was to build a coupled CNN–LSTM model to predict water quality variables. Two traditional machine learning models ... Mar 14, 2019 ... No. DQN and other deep RL methods work well with fully connected layers. Here's an implementation of DQN which doesn't use CNNs: ... Complex problems can be solved and objects can be recognized in images based on the Google TensorFlow ™ library - a widely used deep learning framework. Based ... Sep 25, 2023 ... Region-based Convolutional Neural Network (R-CNN) is a type of deep learning architecture used for object detection in computer vision tasks. May 12, 2022 ... Comments94 · Day 1- Live Deep Learning Community Session · Day 3- Optimizers And ANN Implementation| Live Deep Learning Community Session. Augmented graph representations are further input to a custom CNN deep learning architecture with a split of 70% train, 10% validation and 20% test ...