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Deep Learning: When To Stop Training Nueral Network?

Quick Recap An artificial neural network is a combination of artificial neurons which does some math and try to estimate a mathematical function. This estimation process is called training or fitting. Basic training mechanism The math involved in ANN is mostly MAC (Multiply-Accumulate) operations where the input is multiplied by weights and biases are added to the product. One of the activation functions is applied to the output and it is forwarded to the next layer and the same process continues until it reaches to the end layer. This process is called feed-forward.
After the end layer calculation, the output computed by the network is compared with the actual output. The difference between actual output and estimated output is calculated using a function called loss function. Common loss functions these days are Mean Squared Error, Mean Absolute Error, Root MSE, Cross-Entropy etc. The error calculated using loss function is propagated backward throughout the neural network in the fo…

Build Your First Nueral Network: Basic Image Classification Using Keras

Image classification is one of the most important problem to solve in machine learning. It can provide vital solutions to a variety of computer vision problems, such as face recognition, character recognition, object avoidance in autonomous vehicles and many others. Convolutional Neural Network (CNN), since its inception has been used for image classification and other computer vision problems. It is called convolutional neural network because of convolutional layer. Keras is a high level library which provides an easy way to get started with machine learning and neural networks. It will be used here to implement CNN to classify handwritten digits of MNIST dataset.
Image Classification is  a process to determine which of the given classes an input image belongs to. CNNs represent a huge breakthrough in image classification. In most cases, CNN outperforms other image classification methods and provides near to human-level accuracy. CNN models do not simply spit the class name the inp…

Supervised Learning vs Unsupervised Learning vs Reinforcement Learning

Supervised Learning vs Unsupervised Learning vs Reinforcement Learning Machine learning models are useful when there is huge amount of data available, there are patterns in data and there is no algorithm other than machine learning to process that data. If any of these three conditions are not satisfied, machine learning models are most likely to under-perform. Machine learning algorithms find patterns in data and try to learn from it as much as it can. Based on the type of data available and the approach used for learning, machine learning algorithms are classified in three broad categories. Supervised learningUnsupervised learningReinforcement learning An abstract definition of above terms would be that in supervised learning, labeled data is fed to ML algorithms while in unsupervised learning, unlabeled data is provided. There is a another learning approach which lies between supervised and unsupervised learning, semi-supervised learning. Semi supervised learning algorithms are giv…

AI Vs Machine Learning Vs Deep Learning

AI Vs Machine Learning Vs Deep Learning Artificial intelligence, deep learning and machine learning are often confused with each other. These terms are used interchangeably but do they do not refer to the same thing. These terms are closely related to each other which makes it difficult for beginners to spot differences among them. The reason I think of this puzzle is that AI is classified in many ways. It is divided into subfields with respect to the tasks AI is used for such as computer vision, natural language processing, forecasting and prediction, with respect to the type of approach used for learning and the type of data used. Subfields of Artificial Intelligence have much in common which makes it difficult for beginners to clearly differentiate among these areas. Different approaches of AI can process similar data to perform similar tasks. For example Deep learning and SVM both could be used for object detection task. Both have pros and cons. In some cases Machine Learning is …

Unsupervised Singing Voice Conversion: Facebook's AI Can Convert Your Voice Into Any Other Singer's Voice

You might have heard of the miracles artificial intelligence is doing these days. From movie recommendation on Netflix to Sophia, the robot, from intelligent humanoids in video games to super intelligent Google Assistant, artificial intelligence is dominating most aspects of our lives. It has an impact on our present and future in either way.
Continuing the pursuit to a technology intensive future, Facebook in collaboration with Tel Aviv University published their new research last week on arxiv.org (Unsupervised Singing Voice Conversion). Researchers at Facebook have trained an intelligent machine which can instantly convert audio of one singer to voice of another singer. You check the sample results here. All that is needed is to record a song in own "beautiful" voice and feed the file to the machine. AI will never disappoint you. The results will astound you.
Summary of the work According to authors of the work,
Our approach could lead, for example, to the ability to fre…