Invertis University Associate Professor Interview Questions and Answers

Deep in deep learning refers to the multiple layers of neural networks used to process complex data.

• Deep learning involves multiple layers of artificial neural networks

• These layers allow for the processing of complex data such as images, speech, and natural language

• The depth of the network allows for more accurate predictions and classifications

• Examples of deep learning applications include image recognition, speech re

No, the deepness of a model depends on the complexity of the problem and the number of abstract features it needs to learn.

• The number of layers and neurons can affect the performance and accuracy of the model, but it's not the only factor.

• A simple problem may require only a few layers and neurons, while a complex problem may need more.

• The deepness of a model can also be influenced by the type of layers used, such as co...read more

KL University established a Biotechnology platform to promote research and innovation in the field.

• The platform provides state-of-the-art facilities for research in biotechnology.

• It offers training and workshops to students and researchers to enhance their skills.

• The platform collaborates with industry partners to promote technology transfer and commercialization.

• Research projects in the platform include areas such as ...read more

Molecular biotechnology plays a crucial role in plant breeding by enabling the identification and manipulation of specific genes.

• Molecular markers are used to identify desirable traits in plants

• Genetic engineering techniques are used to introduce specific genes into plants

• CRISPR/Cas9 technology is used to edit genes in plants

• Molecular biotechnology helps in developing disease-resistant and high-yielding plant varieties

• ...read more

Different transformation methods used in RDT include normalization, standardization, log transformation, and power transformation.

• Normalization: scaling data to a range of 0 to 1

• Standardization: scaling data to have a mean of 0 and standard deviation of 1

• Log transformation: transforming data to its logarithmic form

• Power transformation: transforming data using a power function