Long Short-Term Memory (LSTM) recurrent neural networks are one of the most interesting types of deep learning at the moment. They have been used to demonstrate world-class results in complex problem domains such as language translation, automatic image captioning, and text generation. LSTMs are different to multilayer Perceptrons and convolutional neural networks in that they […]
Recurrent neural networks, or RNNs, are a type of artificial neural network that add additional weights to the network to create cycles in the network graph in an effort to maintain an internal state. The promise of adding state to neural networks is that they will be able to explicitly learn and exploit context in […]
The choice of optimization algorithm for your deep learning model can mean the difference between good results in minutes, hours, and days. The Adam optimization algorithm is an extension to stochastic gradient descent that has recently seen broader adoption for deep learning applications in computer vision and natural language processing. In this post, you will […]
Recurrent neural networks are able to learn the temporal dependence across multiple timesteps in sequence prediction problems. Modern recurrent neural networks like the Long Short-Term Memory, or LSTM, network are trained with a variation of the Backpropagation algorithm called Backpropagation Through Time. This algorithm has been modified further for efficiency on sequence prediction problems with […]
Long Short-Term Memory or LSTM recurrent neural networks are capable of learning and remembering over long sequences of inputs. LSTMs work very well if your problem has one output for every input, like time series forecasting or text translation. But LSTMs can be challenging to use when you have very long input sequences and only […]
Exploring and applying machine learning algorithms to datasets that are too large to fit into memory is pretty common. This leads to questions like: How do I load my multiple gigabyte data file? Algorithms crash when I try to run my dataset; what should I do? Can you help me with out-of-memory errors? In this […]
It is good practice to gather a population of results when comparing two different machine learning algorithms or when comparing the same algorithm with different configurations. Repeating each experimental run 30 or more times gives you a population of results from which you can calculate the mean expected performance, given the stochastic nature of most […]
From Developer to Time Series Forecaster in 7 Days. Python is one of the fastest-growing platforms for applied machine learning. In this mini-course, you will discover how you can get started, build accurate models and confidently complete predictive modeling time series forecasting projects using Python in 7 days. This is a big and important post. […]
4-Step Process for Getting Started and Getting Good at Competitive Machine Learning. Kaggle is a community and site for hosting machine learning competitions. Competitive machine learning can be a great way to develop and practice your skills, as well as demonstrate your capabilities. In this post, you will discover a simple 4-step process to get […]
The Python ecosystem is growing and may become the dominant platform for applied machine learning. The primary rationale for adopting Python for time series forecasting is because it is a general-purpose programming language that you can use both for R&D and in production. In this post, you will discover the Python ecosystem for time series […]
