EduAllRounder

 Advanced Deep Learning Sequence Models 1.Which tool is NOT Suited for building ANN models Ans:Excel Neural Networks & Deep Learning 2. “Convolutional Neural Networks can perform various types of transformation (rotations or scaling) in an input”. Is the statement correct True or False? Ans: False Convolutional Neural Networks 3.CNN is mostly used when there is a/an? Ans:Unstructured data Sequence Models 4.How deep learning models are built on Keras Ans:by using sequential models Convolutional Neural Networks 5.Which of the following activation function can not be used in the output layer of an image classification model? Ans:ReLu Neural Networks & Deep Learning 6.ReLU activation function outputs zero when: Ans:Input is less than or equal to zero Sequence Models 7.What is generally the sequence followed when building a neural network architecture for semantic segmentation for image? Ans:Convolutional network on input and deconvolutional network on output 8. A tensor is simi...

 Advanced Machine Learning Engineering 1.Containers running on a single machine all share the same operating system kernel, so they start instantly and make more efficient use of RAM. Ans: True   Flask Application and FastAPI Introduction 2.WSGI stands for the? Ans:Web Server Gateway Interface   Azure Fundamentals 3.Which of the following service is used to manage resources in Azure? Ans:Azure Resource Manager         Azure Portal Version Control - Git 4. git push is the opposite of git clone, instead of downloading your code from GitHub, uploads your changes and code back to GitHub? Ans: True ML Lifecycle 5.Which of the following are ML methods? Ans:based on human supervision MLOps - MLFlow 6.MLOps enables supporting machine learning models and datasets to build these models as first-class citizens within CI/CD systems. Ans:True ML Lifecycle 7. Which of the following is a reasonable way to select the number of principal components "k"? Ans:Choose k to...

 Advanced Machine Learning Challenge 1.What are the steps for using a gradient descent algorithm? Calculate error between the actual value and the predicted value Reiterate until you find the best weights of network Pass an input through the network and get values from output layer Initialize random weight and bias Go to each neurons which contributes to the error and change its respective values to reduce the error Ans: 4, 3, 1, 5, 2 2. Binning is the process of transforming numerical variables into categorical counterparts. Ans: True 3. What is Decision Tree? Ans:Flow-Chart & Structure in which internal node represents test on an attribute, each branch represents outcome of test and each leaf node represents class label 4. Examples of Naive Bayes Algorithm is/are Ans: Spam filtration          Sentimental analysis           Classifying articles 5. Additive model for time series Y = . . . Ans:T+S+C+I  6. For Ridge Regressio...

1.If the value of 'n' is too large,Binomial Distribution ___________. ans: tends to Poisson Distribution 2.Two random variables are independent if below statements are true: ans: P(x|y) = P(x), for all values of X and Y.         P(x∩y) = P(x) * P(y), for all values of X and Y 3. How can yo define the relation between sample size and the standard error of the mean? ans: standard error decreases as the sample size increases. 4. Bernoulli random variables take (only) the values 1 and 0. ans: True 5. Find the value of k if f (x, y) = k(1− x)(1− y) for 0 < x, y < 1 is to be joint density function. ans: 4 6. If a P-value is small enough, then we say the results are statistically significant. ans: True 7. The expected value of a discrete random variable ‘x’ is given by ans: ∑ x P(x) 8. Select the correct statements about exponential distribution from the following: ans: Exponential distribution is bi-variate. Exponential distribution is only defined for non-negative v...

  Tools for Data Science Course Introduction Welcome to the course!  You've begun one of the most complete overviews on data science tooling that you’ll currently  find on the internet.  This doesn’t mean that we cover each and every tool, but later in the course we’ll  introduce a comprehensive list of tasks a data scientist needs to perform and give you  the top two or three open source and commercial tools available to complete them.  We also explain how the tools overlap in functionality, what their pros and cons are, and how these  tools can address the whole data science pipeline.  Let’s start with data.  Data is obviously central to data scientists.  In this course, we’ll show you how to manage, extract, transform, analyze, and visualize  data.  Now, you might be able to survive data science without programming skills if you use the  right set of tools.  However, we highly recommend getting familiar with p...