Fruits Classification using deep learning.
Kashyap Raval (~kashyap32) |
Convolutional Neural Networks (ConvNets or CNNs) are a category of Neural Networks that have proven very effective in areas such as image recognition and classification. ConvNets have been successful in identifying faces, objects, and traffic signs apart from powering vision in robots and self-driving cars. In this workshop, we will see fruits classification using deep learning(CNN).
Deep Learning is a new area of Machine Learning research, which has been introduced with the objective of moving Machine Learning closer to one of its original goals: Artificial Intelligence.
- A ConvNet architecture is in the simplest case a list of Layers that transform the image volume into an output volume (e.g. holding the class scores)
- There are a few distinct types of Layers (e.g. CONV/FC/RELU/POOL are by far the most popular)
- Each Layer accepts an input 3D volume and transforms it to an output 3D volume through a differentiable function
- Each Layer may or may not have parameters (e.g. CONV/FC do, RELU/POOL don’t)
- Each Layer may or may not have additional hyperparameters (e.g. CONV/FC/POOL do, RELU doesn’t)
In this workshop, we will see fruits classification using deep learning(CNN).
- Introduction Of neural networks.
- An introduction of keras using tensorflow backend.
- CNN in keras.
- Fruits classifier.
The participants should have interest in ML/DL.
Basics of Linear Algebra
Python-lover Machine Learning\Deep learning enthusiast.My main work focused on ML / DL / NLP/ WEB. I am an also open source contributor.
Work : -
Intern as Python Developer at LetsNurture.
GDG (Google Developer Group) Ahmedabad - AI/ML in chatbot Alpha College of Engineering - Python