At CereLabs, we are building various image classification systems. While building any kind of classification system one is often challenged to test the trained models. One useful measure to test such models is accuracy, which is the proportion of true results and the total number of images examined. Accuracy thus communicates the essential message of how close one comes to the correct result. In the case of an image classification system, accuracy is how accurately the trained model is able to classify the test image dataset. If we are trying to classify the image of an apple, accuracy will be the measure of how accurately the classifier is able to detect the apple in an image. Consider the following confusion matrix. True Positive (TP) Actual image contains an apple, and is correctly classified as an apple False Negative (FN) Actual image contains an apple but is not classified as an apple False Positive (FP) Actual image does not contain an apple but is class
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Imagine a scenario - You are on a website, and on the bottom right you see a chat window. You realize that it is a virtual assistant, some kind of a chatbot. The look of that window tempts you to experiment. You have interacted with various chatbots previously, a kind of a hobby you have developed, and have seen how miserable the chatbots are. So you start your usual conversation with a Hello. The chatbot replies with “Hello. How can I help you?” . You think yourself as a grand experimenter. You have heard of the Turing Test, and you feel like testing this bot. You say “Hello” again, expecting a similar reply that you got earlier, but to your surprise you get “Hey you. I guess we have exchanged greetings. How can I help you?” You are not satisfied, you want to test it further. You say “Hi, what’s up?” . Will the chatbot get irritated, you ask yourself. The chatbot replies “So you want to play the hello game? I am all for it, as long as you don’t get tired.” You get sho
Cere Labs’ Project Fellowship Program will develop professionals as well as leaders in emerging world of Artificial Intelligence . The Project Fellowship Program gathers all artificial intelligence enthusiasts in real time working and learning environment. Through this program candidates will get hands on experience on Machine Learning , Deep Learning , Neural Networks , Data Mining and many other fields of Artificial Intelligence . Hands on experience, expert mentor ship and a strong network of AI (Artificial Intelligence) professionals will make your job more interesting. What is Project Fellowship Program ? Cere Labs is a privately held company and is working in AI (Artificial Intelligence) and it provides Jo bs in Machine Learning , Jobs in Deep Learning , Jobs in Neural Network , Jobs in Data Mining , Jobs in Artificial Intelligence through this fellowship program. This fellowship program will groom professionals and will make him/her ready to pursue a