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TensorFlow Datasets, also known as tfds is is a library that serves as a wrapper to a wide selection of datasets, with proprietary functions to load, split and prepare datasets for Machine and Deep Learning, primarily with TensorFlow. Note: While the TensorFlow Datasets library is used to get data, it's...
David Landup
Deep Learning models are very versatile and powerful - they're routinely outperforming humans in narrow tasks, and their generalization power is increasing at a rapid rate. New models are being released and benchmarked against community-accepted datasets frequently, and keeping up with all of them is getting harder. Most of these...
Keras is a high-level API, typically used with the TensorFlow library, and has lowered the barrier to entry for many and democratized the creation of Deep Learning models and systems. When just starting out, a high-level API that abstracts most of the inner-workings helps people get the hang of the...
Scikit-Learn is one of the most widely-used Machine Learning library in Python. It's optimized and efficient - and its high-level API is simple and easy to use. Scikit-Learn has a plethora of convenience tools and methods that make preprocessing, evaluating and other painstaking processes as easy as calling a single...
Let me preface the potentially provocative title with: It's true, nobody wants overfitting end models, just like nobody wants underfitting end models. Overfit models perform great on training data, but can't generalize well to new instances. What you end up with is a model that's approaching a fully hard-coded model...
This guide is an in-depth introduction to an unsupervised dimensionality reduction technique called Random Projections. A Random Projection can be used to reduce the complexity and size of data, making the data easier to process and visualize. It is also a preprocessing technique for input preparation to a classifier or...
Mehreen Saeed
In this guide, we'll be taking a look at an unsupervised learning model, known as a Self-Organizing Map (SOM), as well as its implementation in Python. We'll be using an RGB Color example to train the SOM and demonstrate its performance and typical usage. Self-Organizing Maps: A General Introduction A...
In this guide, we'll dive into a dimensionality reduction, data embedding and data visualization technique known as Multidimensional Scaling (MDS). We'll be utilizing Scikit-Learn to perform Multidimensional Scaling, as it has a wonderfully simple and powerful API. Throughout the guide, we'll be using the Olivetti faces dataset from AT&...
Preprocessing data is an often overlooked key step in Machine Learning. In fact - it's as important as the shiny model you want to fit with it. Garbage in - garbage out. You can have the best model crafted for any sort of problem - if you feed it garbage,...
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