This article will help you understand the transition of AI from classical machine learning to deep learning, starting from the basics of machine learning with its major - supervised and unsupervised learning to different regularization and optimization techniques.
In this article, we will discuss regularization and optimization techniques that are used by programmers to build a more robust and generalized neural network. We will study the most effective regularization techniques like L1, L2, Early Stopping, and Drop out which help for model generalization.
Introduction to Artificial Neural Networks for Beginners. Understanding the concepts of Neural Networks.
Demystify Employee Leaving with Machine Learning. Creation and Evaluation of Handful of Machine Learning Models for Leave Prediction. I will share recent work in the human resource domain to bring some predictive power to any firm struggling to retain their employees.
Optimizers are algorithms or methods used to change the attributes of the neural network such as weights and learning rates in order to reduce the losses.
Machine Learning: Similarities With Human Decision Making. Machine Learning as we know have evolved immensely in the last few decades.
In this article, we will discuss how to identify fake from real ones. It includes breaking down videos into a frame, detecting the faces from real and fake videos, crop the faces, and analyzing it. Deep-fake Detection Using OpenCV and MTCNN
One afternoon, in the middle of my holidays the thought of using machine learning to predict football results in the premier leagues came to my mind.
The Ultimate Beginner’s Guide to TensorFlow: In this tutorial, we will cover TensorFlow in enough depth so that you can train machine learning models from scratch!
Artificial neural networks seen to be useful in many applications in recent times like prediction, classification, recognition,translation…
There has been hype about artificial intelligence, machine learning, and neural networks for quite a while now. This will not be a math-heavy introduction because I just want to build the idea here.
A practical example in a hard-to-classify dataset. The need to reduce the complexity of a model can arise from multiple factors, often to reduce the computational requirements.
Basic fundamentals of CNN. CNN’s are a special type of ANN which accepts images as inputs. Below is the representation of a basic neuron of an ANN which takes as input X vector.
Machine learning is a subset of artificial intelligence that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
To build a machine learning algorithm, usually you’d define an architecture (e.g. Logistic regression, Support Vector Machine, Neural Network) and train it to learn parameters.
Learning Rates and Best Practices for Deep Learning. Explore best practices for creating deep learning models in Keras and finding the optimal learning rate.
A continuation of an earlier article. Perceptrons take inputs , scale/multiple them with weights , sum them up and then pass them through an activation function to obtain a result.
While the vast majority of developments in AI technology have centered around practical solutions such as self-driving cars and facial recognition, there's a growing number of artists using AI systems to develop new ideas for artistic projects and generate entirely unique pieces of work.
Rule-based systems and machine learning models are widely utilized to make conclusions from data. Both of these approaches have advantages and disadvantages. Several corporations are implementing and exploring tasks related to artificial intelligence to automate business processes, upgrade product improvement and to enhance market experiences. This blog provides some of the crucial points that should be considered before doing investment in any of the techniques.
I recently learned about logistic regression and feed forward neural networks and how either of them can be used for classification.