-
 Machine Learning in Agriculture: Applications and Techniques
Machine Learning has emerged together with big data technologies and high-performance computing to create new opportunities to unravel, quantify, and understand data intensive processes in agricultural operational environments.
-
A Complete Exploratory Data Analysis and Visualization for Text Data: Combine Visualization and NLP to Generate Insights
Visually representing the content of a text document is one of the most important tasks in the field of text mining as a Data Scientist or NLP specialist. However, there are some gaps between visualizing unstructured (text) data and structured data.
-
Data Science vs. Decision Science
Data science and decision science are related but still separate fields, so at some points, it might be hard to compare them directly. We attempted to show our vision of the commonalities, differences, and specific features of data science and decision science.
-
Which Deep Learning Framework is Growing Fastest?
In September 2018, I compared all the major deep learning frameworks in terms of demand, usage, and popularity. TensorFlow was the champion of deep learning frameworks and PyTorch was the youngest framework. How has the landscape changed?
-
Graduating in GANs: Going From Understanding Generative Adversarial Networks to Running Your Own
Read how generative adversarial networks (GANs) research and evaluation has developed then implement your own GAN to generate handwritten digits.
-
Was it Worth Studying a Data Science Masters?
As I started to apply for Data Science roles it quickly became apparent that I was lacking two key skills: applying Machine Learning and coding
-
How To Go Into Data Science: Ultimate Q&A for Aspiring Data Scientists with Serious Guides
To learn ALL the skills sets in data science is next to impossible as the scope is way too wide. There’ll always be some skills (technical/non-technical) that data scientists don’t know or haven’t learned as different businesses require different skill sets.
-
Pedestrian Detection in Aerial Images Using RetinaNet
Object Detection in Aerial Images is a challenging and interesting problem. By using Keras to train a RetinaNet model for object detection in aerial images, we can use it to extract valuable information.
-
Deploy your PyTorch model to Production
This tutorial aims to teach you how to deploy your recently trained model in PyTorch as an API using Python.
-
How to Train a Keras Model 20x Faster with a TPU for Free
This post shows how to train an LSTM Model using Keras and Google CoLaboratory with TPUs to exponentially reduce training time compared to a GPU on your local machine.
|
Machine Learning in Agriculture: Applications and Techniques
How To Go Into Data Science: Ultimate Q&A for Aspiring Data Scientists with Serious Guides