TechGeeks

Intro to Reinforcement LearningPhoto by James Pond on Unsplash
“Success in creating AI would be the biggest event in human history. Unfortunately, it might also be the last, unless we learn how to avoid the risks.” — Stephen Hawking
Reinforcement Learning is a category of Machine learning which is again a subset of an Artificial Intelligence (AI). We all have experienced AI somehow in our day to day life. The implementation of AI has a vast range of applications in the domain of Education, Finance, Medical, Agriculture, Electronics, Transport, etc.
Now, moving on to machine learning which is a subset of AI. Machine learning is the field of study that gives the computers an ability to learn without being explicitly programmed.


Moving towards Reinforcement LearningThe idea of reinforcement learning is inspired by an idea about how the human brain works. So there are a lot of analogies between them. Reinforcement learning (RL) is trial and error based method in which an agent will perform a…

What is Pose Estimation?Pose Estimation is one of the recent applications of Deep Learning and Computer Vision. In pose estimation we try to predict the pose of a person, it means representing an orientation of a person in graphical format. While reading out the intro it might look a bit complex but actually, it is not. We implement the idea by considering each joint of our body as a coordinate for our graphical representation. The bones which connect two joints are shown as a simple line.To make a posture simple and easy to visualize we do not represent each and every bone of a human body. Instead, we represent Hands, Legs, Shoulder and Face of a person.

BackgroundPreviously, one of the famous application of computer vision was face detection and face recognition. We also used to apply computer vision for detecting whether a person is present in an image or not. If the presence is detected then we can highlight the area where it is present. So, after making some advancements and a bit…

Linear Regression is a Supervised Learning Algorithm. This is probably the first algorithm taught when you start learning ML. Now as a beginner you must be wondering "What is a Supervised Learning??". So machine learning can be divided mainly into 3 types: Supervised LearningUnsupervised LearningReinforcement LearningSupervised Learning is a technique in which we use a labelled dataset to train a model. In short, you can say there is a supervisor who will tell whether the predictions model is making is correct or not. This supervisor decides about based on the labels provided in the datasets. Labels are the correct values for a particular training example. We will deal later with the other two techniques of ML.
In supervised learning, we can classify problems into 2 different categories: Regression - problems in which we need to predict continuous value as an output.Classification - where we need to classify the training examples in a particular category. We plot all the trainin…