How to Fix FutureWarning Messages in scikit-learn

Last Updated on August 21, 2019 Upcoming changes to the scikit-learn library for machine learning are reported through the use of FutureWarning messages when the code is run. Warning messages can be confusing to beginners as it looks like there is a problem with the code or that they have done something wrong. Warning messages are also not good for operational code as they can obscure errors and program output. There are many ways to handle a warning message, including […]

September 29, 2020 Machine Learning

How to use Learning Curves to Diagnose Machine Learning Model Performance

Last Updated on August 6, 2019 A learning curve is a plot of model learning performance over experience or time. Learning curves are a widely used diagnostic tool in machine learning for algorithms that learn from a training dataset incrementally. The model can be evaluated on the training dataset and on a hold out validation dataset after each update during training and plots of the measured performance can created to show learning curves. Reviewing learning curves of models during training […]

September 29, 2020 Machine Learning

Why Training a Neural Network Is Hard

Last Updated on August 6, 2019 Or, Why Stochastic Gradient Descent Is Used to Train Neural Networks. Fitting a neural network involves using a training dataset to update the model weights to create a good mapping of inputs to outputs. This training process is solved using an optimization algorithm that searches through a space of possible values for the neural network model weights for a set of weights that results in good performance on the training dataset. In this post, […]

September 29, 2020 Machine Learning

What is a Hypothesis in Machine Learning?

Last Updated on September 4, 2020 Supervised machine learning is often described as the problem of approximating a target function that maps inputs to outputs. This description is characterized as searching through and evaluating candidate hypothesis from hypothesis spaces. The discussion of hypotheses in machine learning can be confusing for a beginner, especially when “hypothesis” has a distinct, but related meaning in statistics (e.g. statistical hypothesis testing) and more broadly in science (e.g. scientific hypothesis). In this post, you will […]

September 29, 2020 Machine Learning

3 Levels of Deep Learning Competence

Last Updated on August 19, 2019 Deep learning is not a magic bullet, but the techniques have shown to be highly effective in a large number of very challenging problem domains. This means that there is a ton of demand by businesses for effective deep learning practitioners. The problem is, how can the average business differentiate between good and bad practitioners? As a deep learning practitioner, how can you best demonstrate that you can deliver skillful deep learning models? In […]

September 29, 2020 Machine Learning

How to Demonstrate Your Basic Skills with Deep Learning

Last Updated on August 6, 2019 Skills in deep learning are in great demand, although these skills can be challenging to identify and to demonstrate. Explaining that you are familiar with a technique or type of problem is very different to being able to use it effectively with open source APIs on real datasets. Perhaps the most effective way of demonstrating skill as a deep learning practitioner is by developing models. A practitioner can practice on standard publicly available machine […]

September 29, 2020 Machine Learning

How to Develop Competence With Deep Learning for Computer Vision

Last Updated on July 5, 2019 Computer vision is perhaps one area that has been most impacted by developments in deep learning. It can be difficult to both develop and to demonstrate competence with deep learning for problems in the field of computer vision. It is not clear how to get started, what the most important techniques are, and the types of problems and projects that can best highlight the value that deep learning can bring to the field. On […]

September 29, 2020 Machine Learning

9 Applications of Deep Learning for Computer Vision

Last Updated on July 5, 2019 The field of computer vision is shifting from statistical methods to deep learning neural network methods. There are still many challenging problems to solve in computer vision. Nevertheless, deep learning methods are achieving state-of-the-art results on some specific problems. It is not just the performance of deep learning models on benchmark problems that is most interesting; it is the fact that a single model can learn meaning from images and perform vision tasks, obviating […]

September 29, 2020 Machine Learning

8 Books for Getting Started With Computer Vision

Last Updated on June 12, 2020 Computer vision is a subfield of artificial intelligence concerned with understanding the content of digital images, such as photographs and videos. Deep learning has made impressive inroads on challenging computer vision tasks and makes the promise of further advances. Before diving into the application of deep learning techniques to computer vision, it may be helpful to develop a foundation in computer vision more broadly. This may include the foundational and classical techniques, theory, and […]

September 29, 2020 Machine Learning

DeepLearning.AI Convolutional Neural Networks Course (Review)

Last Updated on July 5, 2019 Andrew Ng is famous for his Stanford machine learning course provided on Coursera. In 2017, he released a five-part course on deep learning also on Coursera titled “Deep Learning Specialization” that included one module on deep learning for computer vision titled “Convolutional Neural Networks.” This course provides an excellent introduction to deep learning methods for computer vision applications for those practitioners that are already familiar with the basics of deep learning. It does not […]

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