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 foundation in statistics is required to be effective as a machine learning practitioner. The book “All of Statistics” was written specifically to provide a foundation in probability and statistics for computer science undergraduates that may have an interest in data mining and machine learning. As such, it is often recommended as a book to […]
Statistical Methods for Machine Learning Discover how to Transform Data into Knowledge with Python Why do we need Statistics? Statistics is a collection of tools that you can use to get answers to important questions about data. You can use descriptive statistical methods to transform raw observations into information that you can understand and share. […]
In applied machine learning, we often need to determine whether two data samples have the same or different distributions. We can answer this question using statistical significance tests that can quantify the likelihood that the samples have the same distribution. If the data does not have the familiar Gaussian distribution, we must resort to nonparametric […]
An important decision point when working with a sample of data is whether to use parametric or nonparametric statistical methods. Parametric statistical methods assume that the data has a known and specific distribution, often a Gaussian distribution. If a data sample is not Gaussian, then the assumptions of parametric statistical tests are violated and nonparametric […]
Statistical methods are used at each step in an applied machine learning project. This means it is important to have a strong grasp of the fundamentals of the key findings from statistics and a working knowledge of relevant statistical methods. Unfortunately, statistics is not covered in many computer science and software engineering degree programs. Even […]
A How do I handle categorical data with string values? Your data may have a column that contains string values. Specifically, the string values are labels or categories. For example, a variable or column called “color” with the values in the column of “red“, “green“, or “blue“. If you have string data, such as addresses […]
A Why don’t you use or recommend notebooks and IDEs? I generally recommend that beginners to machine learning do not use notebooks such as IPython or Jupyter notebooks. I also recommend that beginners don’t use fancy integrated development environments (IDEs). Notebooks and IDEs are great tools once you know how things work and you have set […]
A What is the difference between the LSTM and the NLP books? The book “Long Short-Term Memory Networks With Python” focuses on how to implement different types of LSTM models. The book “Deep Learning for Natural Language Processing” focuses on how to use a variety of different networks (including LSTMs) for text prediction problems. The […]
A In what order should I read your books? What order should you read the books? That is a great question, my best suggestions are as follows: Consider starting with a book on a topic that you are most excited about. Consider starting with a book on a topic that you can apply on a […]
