At LearnOpenCV.com, we have adopted a mission of helping people take their first steps in the fields of Machine Learning, Computer Vision, Deep Learning, and AI. We have created a series of tutorials for absolute beginners to get started with Deep Learning using Keras and TensorFlow. Although you can find a lot of tutorials on the Keras and TensorFlow websites, we have tried to complement those with this series of tutorials which offer a gentle introduction to Deep Learning for absolute beginners.
Neural Networks : A 30,000-Foot View for Beginners
In this post, we will provide an overview of Neural Neworks for absolute beginners in the field of Machine Learning and Deep Learnig.
How to Train Neural Networks: For Beginners
In this post, we cover the essential elements required for training Neural Networks for an image classification problem.
Learn How to use Keras for Modeling Linear Regression
In this post, we will cover the fundamental components of a simple (linear) neural network to model linear regression.
Understanding Feedforward Neural Networks
In this article, we will learn about feedforward Neural Networks, also known as Deep feedforward Networks or Multi-layer Perceptrons. Check out the full tutorial
Image Classification using Feedforward Neural Network in Keras
In this post, we will learn how to implement a Feedforward Neural Network in Keras to classify handwritten digits.
Image Classification using Convolutional Neural Networks in Keras
In this tutorial, we will learn the basics of Convolutional Neural Networks ( CNNs ) and how to use them for an Image Classification task. Check out the full tutorial
Keras Tutorial : Using pre-trained Imagenet models
In this post we will learn how to use pre-trained models trained on large datasets like ILSVRC and how to use them for a different task than it was trained on. Check out the full tutorialKeras Tutorial: Transfer Learning using pre-trained models
In this tutorial, we will discuss how to use pre-trained Imagenet models as a Feature Extractor and train a new model for a different classification task. Check out the full tutorial
