I found the book quite didactic and entertaining. Theano and Tensorflow are explored briefly in some specific chapters at the beginning of the book, but most of the material covers how to use Keras effectively with CNNs and RNNs. I found the Time Series and model improvement chapters specially interesting. I recommend this book for newbies willing to get a soft landing into Deep Learning with Python&Keras. I think this is Jason's best book to date. Starting with no previous deep learning experience and little familiarity with Python, over the course of a weekend I was able to develop and train a Convolutional Neural Net that achieved a 0.8% error rate on the famous MNIST digit recognition task (best-in-class is 0.23%). If you're struggling to get up to speed with deep learning, this book is a great way to get started.

Feature selection is the process of reducing the number of input variables when developing a predictive model. It is desirable to reduce the number of input variables to both reduce the computational cost of modeling and, in some cases, to improve the performance of the model. Statistical-based feature selection methods involve evaluating the relationship between each input variable and the target variable using statistics and selecting those input variables that have the strongest relationship with the target variable. These methods can be fast and effective, although the choice of statistical measures depends on the data type of both the input and output variables. As such, it can be challenging for a machine learning practitioner to select an appropriate statistical measure for a dataset when performing filter-based feature selection.

Mixture of experts is an ensemble learning technique developed in the field of neural networks. It involves decomposing predictive modeling tasks into sub-tasks, training an expert model on each, developing a gating model that learns which expert to trust based on the input to be predicted, and combines the predictions. Although the technique was initially described using neural network experts and gating models, it can be generalized to use models of any type. As such, it shows a strong similarity to stacked generalization and belongs to the class of ensemble learning methods referred to as meta-learning. In this tutorial, you will discover the mixture of experts approach to ensemble learning.

A model with high variance will change a lot with small changes to the training dataset. Conversely, a model with low variance will change little with small or even large changes to the training dataset. The variance is always positive. On the whole, the error of a model consists of reducible error and irreducible error. The reducible error is the element that we can improve. It is the quantity that we reduce when the model is learning on a training dataset and we try to get this number as close to zero as possible. The irreducible error is the error that we can not remove with our model, or with any model. The error is caused by elements outside our control, such as statistical noise in the observations.

Classification is a predictive modeling problem that involves predicting a class label for a given example. It is generally assumed that the distribution of examples in the training dataset is even across all of the classes. In practice, this is rarely the case. Those classification predictive models where the distribution of examples across class labels is not equal (e.g. are skewed) are called "imbalanced classification." Typically, a slight imbalance is not a problem and standard machine learning techniques can be used. In those cases where the imbalance is severe, such as a 1:100, 1:1000, or higher ratio of the minority to the majority class, then specialized techniques are required.

Classification predictive modeling involves predicting a class label for a given observation. An imbalanced classification problem is an example of a classification problem where the distribution of examples across the known classes is biased or skewed. The distribution can vary from a slight bias to a severe imbalance where there is one example in the minority class for hundreds, thousands, or millions of examples in the majority class or classes. Imbalanced classifications pose a challenge for predictive modeling as most of the machine learning algorithms used for classification were designed around the assumption of an equal number of examples for each class. This results in models that have poor predictive performance, specifically for the minority class.

Feature selection is the process of reducing the number of input variables when developing a predictive model. It is desirable to reduce the number of input variables to both reduce the computational cost of modeling and, in some cases, to improve the performance of the model. Feature-based feature selection methods involve evaluating the relationship between each input variable and the target variable using statistics and selecting those input variables that have the strongest relationship with the target variable. These methods can be fast and effective, although the choice of statistical measures depends on the data type of both the input and output variables. As such, it can be challenging for a machine learning practitioner to select an appropriate statistical measure for a dataset when performing filter-based feature selection.

Classification is a predictive modeling problem that involves assigning a label to a given input data sample. The problem of classification predictive modeling can be framed as calculating the conditional probability of a class label given a data sample. Bayes Theorem provides a principled way for calculating this conditional probability, although in practice requires an enormous number of samples (very large-sized dataset) and is computationally expensive. Instead, the calculation of Bayes Theorem can be simplified by making some assumptions, such as each input variable is independent of all other input variables. Although a dramatic and unrealistic assumption, this has the effect of making the calculations of the conditional probability tractable and results in an effective classification model referred to as Naive Bayes.

Probability is a field of mathematics that is universally agreed to be the bedrock for machine learning. Although probability is a large field with many esoteric theories and findings, the nuts and bolts, tools and notations taken from the field are required for machine learning practitioners. With a solid foundation of what probability is, it is possible to focus on just the good or relevant parts. In this crash course, you will discover how you can get started and confidently understand and implement probabilistic methods used in machine learning with Python in seven days. This is a big and important post. You might want to bookmark it. Probability for Machine Learning (7-Day Mini-Course) Photo by Percita, some rights reserved.

Probability is a field of mathematics that quantifies uncertainty. It is undeniably a pillar of the field of machine learning, and many recommend it as a prerequisite subject to study prior to getting started. This is misleading advice, as probability makes more sense to a practitioner once they have the context of the applied machine learning process in which to interpret it. In this post, you will discover why machine learning practitioners should study probabilities to improve their skills and capabilities. Before we go through the reasons that you should learn probability, let's start off by taking a small look at the reason why you should not.