Human activity recognition is the problem of classifying sequences of accelerometer data recorded by specialized harnesses or smart phones into known well-defined movements. Classical approaches to the problem involve hand crafting features from the time series data based on fixed-sized windows and training machine learning models, such as ensembles of decision trees. The difficulty is […]
Human activity recognition is the problem of classifying sequences of accelerometer data recorded by specialized harnesses or smart phones into known well-defined movements. It is a challenging problem given the large number of observations produced each second, the temporal nature of the observations, and the lack of a clear way to relate accelerometer data to […]
Human activity recognition is the problem of classifying sequences of accelerometer data recorded by specialized harnesses or smart phones into known well-defined movements. It is a challenging problem given the large number of observations produced each second, the temporal nature of the observations, and the lack of a clear way to relate accelerometer data to […]
Deep Learning for Time Series Forecasting Crash Course. Bring Deep Learning methods to Your Time Series project in 7 Days. Time series forecasting is challenging, especially when working with long sequences, noisy data, multi-step forecasts and multiple input and output variables. Deep learning methods offer a lot of promise for time series forecasting, such as […]
Deep Learning for Time Series Forecasting Predict the Future with MLPs, CNNs and LSTMs in Python …why deep learning? The Promise of Deep Learning for Time Series Forecasting Traditionally, time series forecasting has been dominated by linear methods because they are well understood and effective on many simpler forecasting problems. Deep learning neural networks are […]
A When should I use an MLP, CNN and RNN? A Multilayer Perceptron or MLP can approximate a mapping function from inputs to outputs. They are flexible and can be adapted to most problems, nevertheless, they are perhaps more suited to classification and regression problems. A Convolutional Neural Network or CNN was developed and is […]
A What deep learning library do you recommend? I recommend the Keras library for deep learning. It provides an excellent trade-off of power and ease-of-use. Keras wraps powerful computational engines, such as Google’s TensorFlow library, and allows you to create sophisticated neural network models such as Multilayer Perceptrons, Convolutional Neural Networks and Recurrent Neural Networks […]
A standard deep learning model for text classification and sentiment analysis uses a word embedding layer and one-dimensional convolutional neural network. The model can be expanded by using multiple parallel convolutional neural networks that read the source document using different kernel sizes. This, in effect, creates a multichannel convolutional neural network for text that reads […]
Caption generation is a challenging artificial intelligence problem that draws on both computer vision and natural language processing. The encoder-decoder recurrent neural network architecture has been shown to be effective at this problem. The implementation of this architecture can be distilled into inject and merge based models, and both make different assumptions about the role […]
Transfer learning is a machine learning method where a model developed for a task is reused as the starting point for a model on a second task. It is a popular approach in deep learning where pre-trained models are used as the starting point on computer vision and natural language processing tasks given the vast […]
