Software Engineering for Machine Learning are techniques and guidelines for building ML applications that do not concern the core ML problem — e.g. the development of new algorithms — but rather the surrounding activities like data ingestion, coding, testing, versioning, deployment, quality control, and team collaboration. Good software engineering practices enhance development, deployment and maintenance of production level applications using machine learning components. SE-ML provides a curated list of articles covering the intersection of these two disciplines. Read More
#machine-learningTag Archives: Machine Learning
Machine Learning Evolution — The Story of Perceptron
How it all started to where we are now
A perceptron is a convenient artificial model of a biological neuron. It is a single-layer neural network algorithm used for supervised learning. It consists of input values, weights and bias, and an activation function.
Artificial intelligence (AI) is the new electricity. It has become the talk of the town. Fancy words like machine learning (ML) and deep learning (DL) are now a mandatory part of every product or solution offered by the corporate world to galvanize their clients and end-users. … ML is here to stay for a while and if you are a developer looking to upskill your portfolio, I suggest you start learning. Read More
Backpropagation made easy
Backpropagation is so basic in machine learning yet seems so daunting. But actually, it is easier than it seems.
t doesn’t take a math genius to learn Machine Learning (ML). Basically, all you need is college first-year level calculus, linear algebra, and probability theory, and you are good to go. But behind the seemingly-benign first impression of ML, there are a lot of mathematical theories related to ML. For many people, the first real obstacle in learning ML is back-propagation (BP). It is the method we use to deduce the gradient of parameters in a neural network (NN). It is a necessary step in the Gradient Descent algorithm to train a model. Read More
Tasks, stability, architecture, and compute:Training more effective learned optimizers,and using them to train themselves
Much as replacing hand-designed features with learned functions has revolutionized how we solve perceptual tasks, we believe learned algorithms will transform how we train models. In this work we focus on general-purpose learned optimizers capable of training a wide variety of problems with no user-specified hyperparameters. We introduce a new, neural network parameterized, hierarchical optimizer with access to additional features such as validation loss to enable automatic regularization. Most learned optimizers have been trained on only a single task, or a small number of tasks. We train our optimizers on thousands of tasks, making use of orders of magnitude more compute, resulting in optimizers that generalize better to unseen tasks. The learned optimizers not only perform well, but learn behaviors that are distinct from existing first order optimizers. For instance, they generate update steps that have implicit regularization and adapt as the problem hyperparameters (e.g. batch size) or architecture (e.g. neural network width) change. Finally,these learned optimizers show evidence of being useful for out of distribution tasks such as training themselves from scratch. Read More
Causal Machine Learning Represents Next Evolution of AI
The Covid era is a proving ground. For example, alternative data providers have been espousing the wonders of their unique datasets, but if you can’t prove that your datasets are valuable in these volatile markets, the oxygen in the room is going to escape real quick.
Now stretching into its ninth month here in the US, the pandemic has also turned up the heat on machine-learning models that have historically relied on correlations between different types of datasets. Some very interesting work underway by IBM and Refinitiv could help brace these models for the future. Read More
Machine learning and Doppler vibrometer monitor household appliances
A way of monitoring household appliances by using machine learning to analyse vibrations on a wall or ceiling has been developed by researchers in the US. Their system could be used to create centralized smart home systems without the need for individual sensors in each object. What is more, the technology could help track energy use, identify electrical faults and even remind people to empty the dishwasher. Read More
How to Select the Right Machine Learning Algorithm
Seven key factors to consider when implementing an algorithm
or any given machine learning problem, numerous algorithms can be applied and multiple models can be generated. … Having a wealth of options is good, but deciding on which model to implement in production is crucial. …Here is the list of factors to consider when implementing an algorithm:
- Interpretability
- The number of data points and features
- Data format
- Linearity of data
- Training time
- Prediction time
- Memory requirements
#machine-learning
Machine Learning & Image to Audio Captioning
A brief literature review of how machine learning is used to translate images directly into speech. Read More
Will Machine Learning Supercharge Online Disinformation?
Once heralded as vehicles for promoting democratic values abroad, social media platforms now serve as vectors for homegrown and foreign disinformation. By dictating the information consumed by hundreds of millions of Americans, the machine learning (ML) algorithms employed by these platforms are an integral part of the spread of disinformation. Moreover, by improving and automating the generation and targeting of disinformation, emerging ML capabilities have the potential to significantly enhance the effectiveness of disinformation campaigns.
This brief summarizes the results of a recent analysis that critically evaluates how ML tools could affect the creation, spread, and effectiveness of disinformation. Read More
How to Build a Machine Learning Model
A Visual Guide to Learning Data Science
Learning data science may seem intimidating but it doesn’t have to be that way. Let’s make learning data science fun and easy. So the challenge is how do we exactly make learning data science both fun and easy?
Cartoons are fun and since “a picture is worth a thousand words”, so why not make a cartoon about data science? With that goal in mind, I’ve set out to doodle on my iPad the elements that are required for building a machine learning model. Read More
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