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You are part of a project that will use deep learning to try to identify what is in images - such as cars, ducks, mountains, sky, trees, etc. In this project, two things are important - the first one, is that the deep learning model trains quickly, with efficiency (because...
Cássia Sampaio
Object detection is a large field in computer vision, and one of the more important applications of computer vision "in the wild". On one end, it can be used to build autonomous systems that navigate agents through environments - be it robots performing tasks or self-driving cars, but...
David Landup
Tensors are multi-dimensional objects, and the essential data representation block of Deep Learning frameworks such as TensorFlow and PyTorch. A scalar has zero dimensions, a vector has one dimension, a matrix has two dimensions and tensors have three or more. In practice, we oftentimes refer to scalars and vectors and...
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
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