Filtering on a one-dimensional input signal is one of the common operations of deep learning. Here, we use Conv1D for data inference to predict stock price. 
Xgboost (eXtreme Gradient Boosting) is a gradient boosting decision tree that can be used for classification or regression problems.
The LSTM is suitable for processing and predicting important events with very long intervals and delays in the time series, so we use LSTM to predict the inference of s
This solution is using the Caffe framework, InceptionV2 network architecture is used to train the model and to classify the images through and to classify the images t
This solution is using the Caffe framework, InceptionV1 network architecture is used to train the model and to classify the images through and to classify the images t
Image-Classification-DenseNet-Keras-OCR uses Keras and densenet to judge the text in the image.
Image-Object-Detection-YOLOv3-Keras is the use of Keras's YOLO third version-yolov3 to judge the target for category, detection.
This Image_40Labels_SSD512_Caffe is in the framework of deep learning Caffe, first using the SSD (Single Shot Multibox Detector) algorithm to train the model.
This Image_Face_SSD_Keras is through Keras library, using the SSD (Single Shot Multibox detector) method for face recognition.
This Image_Classify_VGG16_Caffe is using the Caffe framework, VGG16 network architecture is used to train the model and to classify the images through trained models.
Mask R-CNN is an extended application of Faster R-CNN, adding a branch more than Faster r-cnn. The target pixels are segmented while the target is being detected.
Image-Classification-Full-Connection-Caffe-XOR is to use the Caffe framework to determine the XOR value of two numbers.
The purpose of this solution : The user selects the image through the Web page and obtains the frame that the result message of the object detects.
ForLoop refers to the for loop in Loop. If there is some basicity for the program, it should be familiar to the loop.
The function of HttpServer_DeepLearning is to read the already trained caffemodel file.
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