These three articles will mostly take the form of step-by-step coding tutorials while explaining on a theoretical level many technical choices that have been made.
Motivated by the dissertation for my MSc in Robotics I decided to write a trilogy of articles. The aspiration behind this endeavor is to share the findings and the knowledge I have acquired through this magical journey. These three articles will mostly take the form of step-by-step coding tutorials while explaining on a theoretical level many technical choices that have been made.
Before we start implementing let’s talk about the field of Facial Expression Recognition (FER) a little bit:
FER is the scientific research area that deals with techniques and methods that try to identify/infer emotional states from facial expressions. In human communication, facial expressions play a crucial role in inferring emotions that could potentially help in understanding the intentions of others. According to different surveys [1, 2], _**_verbal components convey only one-third of interpersonal communication_, and _non-verbal components convey two-thirds_. The majority of messages related to attitudes and feelings lies in facial expressions. Hence, facial expressions have proven to play a vital role in the entire information exchange process. Expressions and emotions are indissolubly connected. Ekman and Friesen in  triggered the first wave of _Basic Emotion Theory_ inspired studies on emotional expression. They used still photographs of prototypical emotional facial expressions and documented some degree of universality in the recognition and production of a limited set of _“basic” emotions**_ (happiness 😀, surprise 😮, fear 😨, disgust 🤮, sadness 😭, and anger 😡)._
These *6 categories *are going to be used for our task as well, and *CK+48 * is the chosen dataset that will help us train and evaluate our model. The whole implementation took place on Google Colab using GPU acceleration. So, without further ado let’s dive into our task…
Most popular Data Science and Machine Learning courses — August 2020. This list was last updated in August 2020 — and will be updated regularly so as to keep it relevant
This article will simply explain the concept which will help you understand the difference between Machine Learning and Deep Learning.
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In Conversation With Dr Suman Sanyal, NIIT University,he shares his insights on how universities can contribute to this highly promising sector and what aspirants can do to build a successful data science career.
PyTorch for Deep Learning | Data Science | Machine Learning | Python. PyTorch is a library in Python which provides tools to build deep learning models. What python does for programming PyTorch does for deep learning. Python is a very flexible language for programming and just like python, the PyTorch library provides flexible tools for deep learning.