Machine learning is the subfield of computer science, that provides computers the ability to automatically learn on their own and improve from their experiences without being explicitly programmed…
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Understand the significance of classification and regression in machine learning. Explore how these techniques drive AI decision-making and enable accurate predictions.
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Computer vision is a technique for assisting computer programmes in comprehending visual media. It identification software or image recognition machine-learning
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Image Classification Computer vision is a technique for aiding software in understanding visual media.
Data Curator Dashboard for ML Record Classification designed by Tim Webber. Connect with them on Dribbble; the global community for designers and creative professionals.
Image Classification Computer vision is a technique for aiding software in understanding visual media.
Understand the significance of classification and regression in machine learning. Explore how these techniques drive AI decision-making and enable accurate predictions.
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Understand the significance of classification and regression in machine learning. Explore how these techniques drive AI decision-making and enable accurate predictions.
Image Classification Computer vision is a technique for aiding software in understanding visual media.
Regression vs Classification in Machine Learning with Machine Learning, Machine Learning Tutorial, Machine Learning Introduction, What is Machine Learning, Data Machine Learning, Applications of Machine Learning, Machine Learning vs Artificial Intelligence etc.
In this last part, I demonstrate how to save and load the KNN Classification model. (The code linked below will work with 0.2.1 of ml5 and will be upda... Video
Computer vision is a technique for assisting computer programmes in comprehending visual media. It identification software or image recognition machine-learning
Classification algorithms are a type of ML algorithm that can be used to classify data into different categories. Learn about popular ones & their applications.
Classification techniques probably are the most fundamental in Machine Learning. The majority of all online ML/AI courses and curriculums start with this. In normal classification, we have a model…
This post is the outcome of my studies in Neural Networks and a sketch for application of the Backpropagation algorithm. It’s a binary classification task with N = 4 cases in a Neural Network with a single hidden layer. After the hidden layer and the output layer there are sigmoid activation functions. Different colors were… Read More »Matrix Multiplication in Neural Networks
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What is Confusion Matrix and Advanced Classification Metrics? After data preparation and model training, there is model evaluation phase which I mentioned in my earlier article Simple Picture of Machine Learning Modelling Process. Once model is developed, the next phase is to calculate the performance of the developed model using some evaluation metrics. In this article, you will just discover about confusion matrix though there are many classification metrics out there. Mainly, it focuses on below points: What is confusion matrix? Four outputs in confusion matrix Advanced classification metrics Table 1. Confusion matrix with advanced classification metrics Confusion Matrix is a tool to determine the performance of classifier. It contains information about actual and predicted classifications. The below table shows confusion matrix of two-class, spam and non-spam classifier. Table 2. Confusion matrix of email classification Let’s understand four outputs in confusion matrix. 1. True Positive (TP) is the number of correct predictions that an example is positive which means positive class correctly identified as positive. Example: Given class is spam and the classifier has been correctly predicted it as spam. 2. False Negative (FN) is the number of incorrect predictions that an example is negative which means positive class incorrectly identified as negative. Example: Given class is spam however, the classifier has been incorrectly predicted it as non-spam. 3. False positive (FP) is the number of incorrect predictions that an example is positive which means negative class incorrectly identified as positive. Example: Given class is non-spam however, the classifier has been incorrectly predicted it as spam. 4. True Negative (TN) is the number of correct predictions that an example is negative which means negative class correctly identified as negative. Example: Given class is spam and the classifier has been correctly predicted it as negative. Now, let’s see some advanced classification metrics based on confusion matrix. These metrics are mathematically expressed in Table 1 with example of email classification, shown in Table 2. Classification problem has spam and non-spam classes and dataset contains 100 examples, 65 are Spams and 35 are non-spams. Sensitivity is also referred as True Positive Rate or Recall. It is measure of positive examples labeled as positive by classifier. It should be higher. For instance, proportion of emails which are spam among all spam emails. Table 3. Sensitivity in confusion matrix Sensitivity = 45/(45+20) = 69.23% . The 69.23% spam emails are correctly classified and excluded from all non-spam emails. Specificity is also know as True Negative Rate. It is measure of negative examples labeled as negative by classifier. There should be high specificity. For instance, proportion of emails which are non-spam among all non-spam emails. Table 4. Specificity in confusion matrix specificity = 30/(30+5) = 85.71% . The 85.71% non-spam emails are accurately classified and excluded from all spam emails. Precision is ratio of total number of correctly classified positive examples and the total number of predicted positive examples. It shows correctness achieved in positive prediction. Table 5. Precision in confusion matrix Precision = 45/(45+5)= 90% The 90% of examples are classified as spam are actually spam. Accuracy is the proportion of the total number of predictions that are correct. Table 6. Accuracy in confusion matrix Accuracy = (45+30)/(45+20+5+30) = 75% The 75% of examples are correctly classified by the classifier. F1 score is a weighted average of the recall (sensitivity) and precision. F1 score might be good choice when you seek to balance between Precision and Recall. It helps to compute recall and precision in one equation so that the problem to distinguish the models with low recall and high precision or vice versa could be solved. Kindly follow my blog by email and stay tuned for more advanced post on regression measures. Thank you!
GPT-3 is a neural network trained by the OpenAI organization with more parameters than earlier generation models. The main difference between GPT-3 and GPT-2, is its size which is 175 billion parameters. It’s the largest language model that was trained on a large dataset. The model responds better to different types of input, such as … Continue reading Intent Classification & Paraphrasing examples using GPT-3
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Convolutional Neural Networks are a type of Deep Learning Algorithm. Learn how CNN works with complete architecture and example. Explore applications of CNN
