Transfer learning from scratch is being used by many big tech companies across the world which can create numerous job opportunities
Transfer learning is one of the fast-growing fields of information technology. It is a set of techniques that allows machines to anticipate outcomes from a layered set of inputs. Transfer learning from scratch is being used by many big tech companies across the world which can create numerous job opportunities in the sector. If you are one of the readers wanting to start a career in transfer learning? Then you need to know about the transfer learning interview questions that are asked in the machine learning interviews. Let’s know more about it in this article.
Why do we need Transfer Learning when Machine Learning is present?
Machine learning is a framework that takes past data to identify the relationships among the features. The machines can predict the new data with the help of mathematical relationships by getting dynamic, accurate, and stable models. On the other hand, transfer learning is a segment of machine learning that uses complex algorithms based on the biological neural network to mimic the human brain and take decisions and actions like a human.
What does the Activation Function do in a Neural Network?
The activation function is the most important factor in a neural network which decides whether or not a neuron will be activated or not and transferred to the next layer. This simply means that it will decide whether the neuron’s input to the network is relevant or not in the process of prediction. This is one of the top transfer learning questions asked during a job interview.
How would you simulate the approach AlphaGo took to beat Lee Sedol at Go?
AlphaGo beating Lee Sedol, the best human player at Go, in a best-of-five series was a truly seminal event in the history of machine learning and transfer learning. The Nature paper clearly describes how this was accomplished with “Monte-Carlo tree search with deep neural networks that have been trained by supervised learning, from human expert games, and by reinforcement learning from games of self-play.”
Explain learning rate in the context of neural network models. What happens if the learning rate is too high or too low?
Learning rate is a number that ranges from 0 to 1. It is one of the most important tunable hyperparameters in neural network training models. The learning rate determines how quickly or slowly a neural network model adapts to a given situation and learns. A higher learning rate value indicates that the model only needs a few training epochs and produces rapid changes, whereas a lower learning rate indicates that the model may take a long time to converge or may never converge and become stuck on a poor solution. As a result, it is recommended that a good learning rate value be established by trial and error rather than using a learning rate that is too low or too high.
Explain about Gradient Descent?
Gradient Descent is an optimal algorithm to cut down the cost function or an error. The main aim is to find the local-global minima of a function. This determines the direction the model should take to reduce error. This is one of the top transfer learning questions asked during a job interview.
What is the difference between Single-Layer and Multi-Layer Perceptron?
In a neural network, a perceptron is the processing unit that performs computations to extract features. It is the building block of the Artificial Neural Network (ANN). A single-layer perceptron is the simplest neural network without any hidden layer. It is a linear classifier with only one neuron and can perform binary classification. It can only classify the linear separable classes having output as binary.
What is your Understanding of Backpropagation?
In machine learning, backpropagation is a widely used algorithm for training feedforward neural networks. Generalizations of backpropagation exist for other artificial neural networks, and functions in general. These classes of algorithms are all referred to generically, as “backpropagation”.
Explain what a deep neural network is.
An artificial neural network (ANN) having numerous layers between the input and output layers is known as a deep neural network (DNN). Deep neural networks are neural networks that use deep architectures. The term “deep” refers to functions that have a higher number of layers and units in a single layer. It is possible to create more accurate models by adding more and larger layers to capture higher levels of patterns. The below image depicts a deep neural network.
What Is Data Normalization, and Why Do We Need It?
The process of standardizing and reforming data is called “Data Normalization.” It’s a pre-processing step to eliminate data redundancy. Often, data comes in, and you get the same information in different formats. In these cases, you should rescale values to fit into a particular range, achieving better convergence.
Can we have the same bias for all neurons of a hidden layer?
Essentially, you can have a different bias value at each layer or each neuron as well. However, it is best if we have a bias matrix for all the neurons in the hidden layers as well. A point to note is that both these strategies would give you very different results.
Source: analyticsinsight.net
