What is TensorFlow?
TensorFlow is an open-source Machine Learning library. It is a fast, flexible, and low-level toolkit for doing complex algorithms and offers users customizability to build experimental learning architectures and to work on them to produce desired outputs.
Mention the difference between statistical AI and Classical AI ?
Statistical AI is more concerned with “inductive” thought like given a set of pattern, induce the trend etc. While, classical AI, on the other hand, is more concerned with “ deductive” thought given as a set of constraints, deduce a conclusion etc.
What’s regularization?
When you have underfitting or overfitting issues in a statistical model, you can use the regularization technique to resolve it. Regularization techniques like LASSO help penalize some model parameters if they are likely to lead to overfitting.
If the interviewer follows up with a question about other methods that can be used to avoid overfitting, you can mention cross-validation techniques such as k-folds cross-validation.
Another approach is to keep the model simple by taking into account fewer variables and parameters. Doing this helps remove some of the noise in the training data.
What is artificial intelligence?
AI can be described as an area of computer science that simulates human intelligence in machines. It’s about smart algorithms making decisions based on the available data.
Whether it’s Amazon’s Alexa or a self-driving car, the goal is to mimic human intelligence at lightning speed (and with a reduced rate of error).
What are intermediate tensors? Do sessions have lifetime?
The intermediate tensors are tensors that are neither inputs nor outputs of the Session.run() call, but are in the path leading from the inputs to the outputs; they will be freed at or before the end of the call.
Sessions can own resources, few classes like tf.Variable, tf.QueueBase, and tf.ReaderBase, and they use a significant amount of memory. These resources (and the associated memory) are released when the session is closed, by calling tf.Session.close.
When an algorithm is considered completed?
An algorithm is said completed when it terminates with a solution when one exists.
Explain the different algorithms used for hyperparameter optimization.
Grid Search
Grid search trains the network for every combination by using the two set of hyperparameters, learning rate and the number of layers. Then evaluates the model by using Cross Validation techniques.
Random Search
It randomly samples the search space and evaluates sets from a particular probability distribution. For example, instead of checking all 10,000 samples, randomly selected 100 parameters can be checked.
Bayesian Optimization
This includes fine-tuning the hyperparameters by enabling automated model tuning. The model used for approximating the objective function is called surrogate model (Gaussian Process). Bayesian Optimization uses Gaussian Process (GP) function to get posterior functions to make predictions based on prior functions.
What are the different types of keys in a relational database?
There are a variety of keys in a relational database, including:
Alternate keys are candidate keys that exclude all primary keys.
Artificial keys are created by assigning a unique number to each occurrence or record when there aren’t any compound or standalone keys.
Compound keys are made by combining multiple elements to develop a unique identifier for a construct when there isn’t a single data element that uniquely identifies occurrences within a construct. Also known as a composite key or a concatenated key, compound keys consist of two or more attributes.
Foreign keys are groups of fields in a database record that point to a key field or a group of fields that create a key of another database record that’s usually in a different table. Often, foreign keys in one table refer to primary keys in another. As the referenced data can be linked together quite quickly, it can be critical to database normalization.
Natural keys are data elements that are stored within constructs and utilized as primary keys.
Primary keys are values that can be used to identify unique rows in a table and the attributes associated with them. For example, these can take the form of a Social Security number that’s related to a specific person. In a relational model of data, the primary key is the candidate key. It’s also the primary method used to identify a tuple in each possible relation.
Super keys are defined in the relational model as a set of attributes of a relation variable. It holds that all relations assigned to that variable don’t have any distinct tuples. They also don’t have the same values for the attributes in the set. Super keys also are defined as a set of attributes of a relational variable upon which all of the functionality depends.
While creating Bayesian Network what is the consequence between a node and its predecessors?
While creating Bayesian Network, the consequence between a node and its predecessors is that a node can be conditionally independent of its predecessors.
Name a few Machine Learning algorithms you know.
- Logistic regression
- Linear regression
- Decision trees
- Support vector machines
- Naive Bayes, and so on