Being a Data Scientist, there are situations when we feel that our findings can be better explained with the help of a working prototype. A prototype can be very helpful in giving a birds-eye view of the solution to stakeholders. But due to limited or no knowledge of web development, we either end up looking for some help and if this doesn’t wo...
A “Loss” and “Metric” are the 2 things which we look at the end of each epoch1 to check the performance of a model. These are very closely related but are not exactly the same. Let’s look at their significance. An Epoch is an event when our model has looked at each of the data points in the training set once. ↩
It doesn’t matter what software and libraries we learn because it only takes a few days to switch from one software or library to another. Whatever tools we use the concept remains the same. These libraries will be updated at a much faster pace in the coming time as more people will be getting into this field, with different skillsets and idea...
In my earlier blog, we looked at the similarities and differences between our machine learning or deep learning model and regular computer program. If you have not gone through it, I would request you to go through it by clicking here.
Have you ever thought about why it is always advised to use 224 in the image transformation function while you deal with any computer vision problem in deep learning? What will happen if you increase or decrease this size? If these are a few of the questions that ever came to your mind then you have been landed in the correct spot.
Data Science, Machine Learning, Deep Learning and Artificial Intelligence are a few of the buzzwords in the current times. Let’s see how machine learning and deep learning models different from normal computer programs. Machine learning model in general terms is more like regular programming which gets the computer to complete a specific task....
As a standard practice, we hold 20% of the data points to be used for model validation, this data is referred to as a validation set. The other 80% of the data is used for training the model.
ResNet is one of the standard architectures which is widely used in computer vision problems. It is both fast and accurate for many datasets and problems.
In the field of deep learning, we have the ability to use the capabilities of a model which was designed to do an entirely different set of tasks as we want to do it now. These original models are termed as pretrained models.
