What type of deep learning models are best suited for image recognition?
CNN is best suited for image processing. Some of the applications areas of CNN are in image recognition, image classification, object detection, and face recognition.
Which ML algorithm is best for image classification?
In the image classification field, traditional machine learning algorithms, such as K-Nearest Neighbor (KNN) and Support Vector Machine (SVM), are widely adopted to solve classification problems and especially perform well on small datasets.
Which is the best framework for deep learning?
Those pre-trained models are implemented and trained on a particular deep learning framework/library such as TensorFlow, PyTorch, Caffe, etc. and might also be exported to the ONNX format (standard model format across frameworks). As of today, ML.NET supports TensorFlow and ONNX, while Pytorch is in our long-term roadmap, though.
How is deep learning used in computer vision?
Researchers can use deep learning models for solving computer vision tasks. Deep learning is a machine learning technique that focuses on teaching machines to learn by example. Since most deep learning methods use neural network architectures, deep learning models are frequently called deep neural networks.
How to train deep learning models in ML.NET?
Currently (2019), there are three possible ways in ML.NET for training an Image Classifier model: Native Deep Learning model training (TensorFlow) for Image Classification (Easy to use high-level API, GPU support – Released with ML.NET 1.4 GA)
Which is the best deep learning model for word embedding?
There are two popular pre-trained word embedding models: word2vec and GloVe [3]. Like the word2vec-keras model used in [4], these pre-trained word embedding models are usually combined with other supervised deep learning algorithms such as the recurrent neural network (RNN) LSTM for NLP such as text classification [4].