What is the primary advantage of using CNNs in image recognition compared to traditional computer vision techniques?a.Improved feature engineeringb.Better performance with small datasetsc.Automatic feature learningd.Faster execution on CPU

Which statement is true?Group of answer choicesNeural nets are not great for image data because for images, you only need to learn a few parametres but neural nets can learn many.Small changes in images don’t cause massively different image vectors.Images are not very high dimension and thus, are not very painful to train.CNNs are great for images because they are translation invariant.

Question 4.Regarding CNN Deep Learning Architectures for Computer Vision, please discussand explain the following concepts.(a) The Universal Approximation Theorem states that a 2-layer network canrepresent any computational problem to an arbitrary degree of accuracy.Why then is there a need to look at deeper networks?(b) Why is there a need for non-linear activation functions between layers?(c) Why do we use a loss function for training instead of a more intuitivemetric such as accuracy?(d) With respect to image data augmentation, how does fastai presizing workand why is it advantageous?(e) What is fine tuning and how is it used? What are the advantages of finetuning over training from scratch?(10 marks)(Part B: Total 40 marks)

What are the advantages and disadvantages of using DNNs compared to other machine learning models?

What is one of the primary applications of Convolutional Neural Networks (CNN)?a.Speech recognitionb.Image recognitionc.Natural language processingd.Financial forecasting

Introduction of CNN for Image Recognition