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Why I Love Keras and Why You Should Too
I started working with Deep Learning (DL) in the 2016 – 2017 time frame when the framework ecosystem was much more diverse and fragmented than it is today. Theano was the gold standard at the time, Tensorflow had just been released, and DeepLearning4j was still being... Read more
Keras Metrics: Everything You Need To Know
Keras metrics are functions that are used to evaluate the performance of your deep learning model. Choosing a good metric for your problem is usually a difficult task. you need to understand which metrics are already available in Keras and tf.keras and how to use them, in many... Read more
10 Notable Frameworks for NLP
Natural Language Processing hit its big stride back in 2017 with the introduction of Transformer Architecture from Google. State of the art approaches helped bridge the gap between humans and machines and helped us build bots capable of using human language undetected. It’s an exciting time.... Read more
5 Deep Learning Frameworks to Consider for 2020
Enough of flirting with deep learning and deep learning frameworks; it’s time to glide across the room and say, “Hello.” Call it an advanced subfield of machine learning or future to enhanced vision in the field of technology, deep learning won’t stop now!  Imbibed in the... Read more
Major Updates to the Most Popular Data Science Frameworks in 2019
This time last year we brought you a detailed report of all the important updates for popular data science (machine learning and deep learning) frameworks throughout 2018. The developers of these frameworks continue to innovate at an accelerated rate. Data scientists demand more powerful tools in... Read more
Top 7 Machine Learning Frameworks for 2020
Machine learning is a nightmare without some kind of structure. You can’t build everything from scratch, especially if you’re in a business setting. Even if you want to (and if you do, comment here and tell us about it!), you don’t have time in most cases.... Read more
Deep Learning Frameworks You Need to Know in 2020
Deep learning networks have a mind-boggling ability to learn, so training these models requires massive computing power and intense amounts of data. You’ll need a framework to make that development easier. Deep learning requires massive processing power and lots of data. Because it uses unstructured, often non-text... Read more
Using Keras and TensorFlow in R
Keras and Tensorflow are two very powerful packages that are normally accessed via python. Since the packages were developed for python they may have the illusion of being out of reach for R users. However, this is not the case as the Keras and Tensorflow packages... Read more
Deep Learning in R with Keras
The primary professional hat I wear is as a data science consultant working with machine learning in a variety of problem domains. Due to my academic past in computer science and applied statistics, my development environment of choice today is typically R. Lately however, Python is... Read more
Want to Train Computer Vision Models 100x Faster? Meet MissingLink.ai
To stay competitive, large and small organizations are turning to deep learning and AI for faster innovation. They strive to create value, improve customer experience and ship faster to differentiate themselves from the competition, so they place tremendous pressure and demands on their data science teams... Read more