Computer Vision also referred as Vision is the recent cutting edge field within computer science that deals with enabling computers, devices or machines, in general, to see, understand, interpret or manipulate what is being seen. Computer Vision technology implements deep learning techniques and in few cases also employs Natural Language Processing techniques as a natural progression of steps to analyze extracted text from images.
To learn more, check out my publication with apress on this topic. The definition of Artificial Intelligence has evolved ever since its first reference in 1956 at the Dartmouth conference. From emulating how the human brain works to solving focused, complex problems, doing all that a human can do like see, hear, communicate, act, learn,
Encouraged by all the responses to my previous “Simplified” blog series on Reinforcement learning and Ensemble learning, I am writing this blog covering Deep learning basics in a step-by-step manner. The primary aim of this blog is to enforce mastering the neural networks and related deep learning techniques conceptually. With the help of a complex
We are seeing that there is an Enterprise-wide adoption of transfer learning techniques while using Machine learning and Deep learning. And here is the reason why… Traditionally, all the Machine learning algorithms assume learning to happen from scratch for every new learning problem. The assumption is that no previous learning will be leveraged. Transfer learning
Inline with my earlier blog on “Reinforcement learning Simplified” that I recieved excellent reception, I now have Ensemble learning basics and algorithms explained using analogies. Please don’t forget to share your inputs and valuable feedback. Ensemble, in general, means a group of things that are usually seen as a whole rather than in terms of
We are all overwhelmed by the programming /scripting language options we have for implementing AI/DL/ML. In this blog post, I am covering the landscape of available options and in specific giving away some beginners dope on Julia. There are several open source and commercial Machine learning frameworks and tools in the market that have evolved
This post is a modified excerpt from one of my publications on machine learning. Everyone is curious and the jargon doesn’t get off our backs. I attempted a comparison between the prevalent terminologies that exist today and how each of these are similar or dissimilar to machine learning. Please remember, I haven’t yet brought in
In this blog, I will be covering the below taxonomy of Reinforcement learning. It is easy to visualize and memorize the complete concept in a single mindmap. This is how I learn and hope you all find it easy to understand and apply.. Let’s recap different learning methods before we go deep into Reinforcement learning.
While I was attempting to learn how Bayesian, Regression analysis and Instance-based learning techniques under probablistic machine learning, I realized how deep the statistical techniques are and how they form a basis for the above supervised learning techniques. Below shown is a mind map of statistical and algebraic concepts that Regression analysis based algorithms employ.
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