Trending December 2023 # Comment: Analyst’s 2023 Imac Claim Raises More Questions Than It Answers # Suggested January 2024 # Top 20 Popular

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A new report today offers a claimed look at Apple’s 2023 iMac plans, namely to prioritize quality over quantity when it comes to the display. The report says that Apple is sticking to 27 inches for next year’s iMac, but will offer the same display tech used in the just-launched MacBook Pro models.

This contradicts expectations of a 32-inch iMac, which would seem the obvious next step for a couple of reasons …

First, the fact that a 27-inch display looks pretty cramped by today’s standards. I mean, it was a huge display when the first 27-inch iMac was introduced way back in 2009, but in 2023 it looks rather miserly. Indeed, 27-inch monitors are very commonly used in dual and triple monitor setups, rather than on their own.

I’ve admittedly gone to something of an extreme with my 49-inch monitor, but that’s effectively just a dual 27-inch setup without the distraction of center bezels.

Second, Apple chose 32 inches for its Pro Display XDR. It would have seemed logical to create the next-generation of iMacs to match this.

Today’s report says not.

This morning, display analyst Ross Young tweeted that he expects Apple to launch a 27-inch iMac in the first quarter of 2023, featuring a mini-LED 120Hz display, just like the displays seen in the MacBook Pros unveiled this week.

Rather than increase the screen size, says Ross, Apple will instead focus on a significant boost to the quality of the display – bringing the same features as seen in the 14- and 16-inch MacBook Pro models.

That certainly has to be considered a plausible claim. As we noted, the source has a good track record in this field.

Ross Young has a very reliable track record when it comes to predicting Apple’s supply chain commitments for display components. In fact, he was essentially the only person suggesting that the new MacBook Pros would feature 120Hz high refresh rate ahead of the Unleashed event, and he of course turned out to be correct.

It would also make sense for Apple to introduce the latest display tech first to the smaller displays of MacBook Pro models, and later to the larger iMac ones.

At the same time, though, there’s something odd about the idea. Although some pro users like the form factor of the iMac, and would want the new display tech, they are also going to want the M1 Pro and M1 Max chips. Effectively a larger-screened, desktop version of the MacBook Pro.

So is this new model an iMac Pro? Maybe, but does it make sense to have a 27-inch pro Mac alongside a 32-inch pro monitor?

On the other hand, if it’s a consumer machine, does it make sense to have both a 24-inch and 27-inch consumer iMac? One can argue that 14 inches and 16 inches are also close in size, but small differences in dimensions and weight can be important when it comes to a laptop. For a desktop, such a small difference would seem odd.

Apple could have an M1-powered 24-inch iMac and an M1 Pro/Max iMac Pro, but then the company just separated screen size and performance in the MacBook Pro: You can spec out the 14-inch model with the exact same options and maxima as the 16-inch one.

Or is Young right about the display specs but wrong about the intended product? Is this actually for a 27-inch monitor to finally replace the discontinued Apple Thunderbolt Display? This was in fact Young’s original guess before he changed his mind.

Right now, I have more questions than answers.

If I had to bet, I’m going to go with it being an iMac Pro. Yes, it’s odd to have a 27-inch pro iMac and a 32-inch pro monitor, but perhaps the market for the Pro Display XDR is so tiny that it’s effectively irrelevant in this context. So Apple launches a new iMac Pro with M1 Pro and M1 Max options, together with the latest pro display tech.

27 inches does still feel small, but I know some pros like to have an all-in-one setup with a decent-size screen that is still transportable for on-site use, so perhaps that’s a decent chunk of the market.

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50 Machine Learning Interview Questions And Answers (2023)

Here are Machine Learning interview questions and answers for fresher as well as experienced candidates to get their dream job.

1) What is Machine learning?

Machine learning is a branch of computer science which deals with system programming in order to automatically learn and improve with experience. For example: Robots are programed so that they can perform the task based on data they gather from sensors. It automatically learns programs from data.

2) Mention the difference between Data Mining and Machine learning?

Machine learning relates with the study, design and development of the algorithms that give computers the capability to learn without being explicitly programmed. While, data mining can be defined as the process in which the unstructured data tries to extract knowledge or unknown interesting patterns. During this process machine, learning algorithms are used.

3) What is ‘Overfitting’ in Machine learning?

In machine learning, when a statistical model describes random error or noise instead of underlying relationship ‘overfitting’ occurs. When a model is excessively complex, overfitting is normally observed, because of having too many parameters with respect to the number of training data types. The model exhibits poor performance which has been overfit.

4) Why overfitting happens?

The possibility of overfitting exists as the criteria used for training the model is not the same as the criteria used to judge the efficacy of a model.

5) How can you avoid overfitting?

By using a lot of data overfitting can be avoided, overfitting happens relatively as you have a small dataset, and you try to learn from it. But if you have a small database and you are forced to come with a model based on that. In such situation, you can use a technique known as cross validation. In this method the dataset splits into two section, testing and training datasets, the testing dataset will only test the model while, in training dataset, the datapoints will come up with the model.

In this technique, a model is usually given a dataset of a known data on which training (training data set) is run and a dataset of unknown data against which the model is tested. The idea of cross validation is to define a dataset to “test” the model in the training phase.

6) What is inductive machine learning?

The inductive machine learning involves the process of learning by examples, where a system, from a set of observed instances tries to induce a general rule.

7) What are the five popular algorithms of Machine Learning?

Decision Trees

Neural Networks (back propagation)

Probabilistic networks

Nearest Neighbor

Support vector machines

8) What are the different Algorithm techniques in Machine Learning?

The different types of techniques in Machine Learning are

Supervised Learning

Unsupervised Learning

Semi-supervised Learning

Reinforcement Learning

Transduction

Learning to Learn

9) What are the three stages to build the hypotheses or model in machine learning?

Model building

Model testing

Applying the model

10) What is the standard approach to supervised learning?

The standard approach to supervised learning is to split the set of example into the training set and the test.

11) What is ‘Training set’ and ‘Test set’?

In various areas of information science like machine learning, a set of data is used to discover the potentially predictive relationship known as ‘Training Set’. Training set is an examples given to the learner, while Test set is used to test the accuracy of the hypotheses generated by the learner, and it is the set of example held back from the learner. Training set are distinct from Test set.

12) List down various approaches for machine learning?

The different approaches in Machine Learning are

Concept Vs Classification Learning

Symbolic Vs Statistical Learning

Inductive Vs Analytical Learning

13) What is not Machine Learning?

Artificial Intelligence

Rule based inference

14) Explain what is the function of ‘Unsupervised Learning’?

Find clusters of the data

Find low-dimensional representations of the data

Find interesting directions in data

Interesting coordinates and correlations

Find novel observations/ database cleaning

15) Explain what is the function of ‘Supervised Learning’?

Classifications

Speech recognition

Regression

Predict time series

Annotate strings

16) What is algorithm independent machine learning?

Machine learning in where mathematical foundations is independent of any particular classifier or learning algorithm is referred as algorithm independent machine learning?

17) What is the difference between artificial learning and machine learning?

Designing and developing algorithms according to the behaviours based on empirical data are known as Machine Learning. While artificial intelligence in addition to machine learning, it also covers other aspects like knowledge representation, natural language processing, planning, robotics etc.

18) What is classifier in machine learning?

A classifier in a Machine Learning is a system that inputs a vector of discrete or continuous feature values and outputs a single discrete value, the class.

20) In what areas Pattern Recognition is used?

Pattern Recognition can be used in

Computer Vision

Speech Recognition

Data Mining

Statistics

Informal Retrieval

Bio-Informatics

21) What is Genetic Programming?

Genetic programming is one of the two techniques used in machine learning. The model is based on the testing and selecting the best choice among a set of results.

22) What is Inductive Logic Programming in Machine Learning?

Inductive Logic Programming (ILP) is a subfield of machine learning which uses logical programming representing background knowledge and examples.

23) What is Model Selection in Machine Learning?

The process of selecting models among different mathematical models, which are used to describe the same data set is known as Model Selection. Model selection is applied to the fields of statistics, machine learning and data mining.

24) What are the two methods used for the calibration in Supervised Learning?

The two methods used for predicting good probabilities in Supervised Learning are

Platt Calibration

Isotonic Regression

These methods are designed for binary classification, and it is not trivial.

25) Which method is frequently used to prevent overfitting?

When there is sufficient data ‘Isotonic Regression’ is used to prevent an overfitting issue.

26) What is the difference between heuristic for rule learning and heuristics for decision trees?

The difference is that the heuristics for decision trees evaluate the average quality of a number of disjointed sets while rule learners only evaluate the quality of the set of instances that is covered with the candidate rule.

27) What is Perceptron in Machine Learning?

In Machine Learning, Perceptron is a supervised learning algorithm for binary classifiers where a binary classifier is a deciding function of whether an input represents a vector or a number.

28) Explain the two components of Bayesian logic program?

Bayesian logic program consists of two components. The first component is a logical one ; it consists of a set of Bayesian Clauses, which captures the qualitative structure of the domain. The second component is a quantitative one, it encodes the quantitative information about the domain.

29) What are Bayesian Networks (BN)?

Bayesian Network is used to represent the graphical model for probability relationship among a set of variables.

30) Why instance based learning algorithm sometimes referred as Lazy learning algorithm?

Instance based learning algorithm is also referred as Lazy learning algorithm as they delay the induction or generalization process until classification is performed.

31) What are the two classification methods that SVM ( Support Vector Machine) can handle?

Combining binary classifiers

Modifying binary to incorporate multiclass learning

32) What is ensemble learning?

To solve a particular computational program, multiple models such as classifiers or experts are strategically generated and combined. This process is known as ensemble learning.

33) Why ensemble learning is used?

Ensemble learning is used to improve the classification, prediction, function approximation etc of a model.

34) When to use ensemble learning?

35) What are the two paradigms of ensemble methods?

The two paradigms of ensemble methods are

Sequential ensemble methods

Parallel ensemble methods

36) What is the general principle of an ensemble method and what is bagging and boosting in ensemble method?

The general principle of an ensemble method is to combine the predictions of several models built with a given learning algorithm in order to improve robustness over a single model. Bagging is a method in ensemble for improving unstable estimation or classification schemes. While boosting method are used sequentially to reduce the bias of the combined model. Boosting and Bagging both can reduce errors by reducing the variance term.

37) What is bias-variance decomposition of classification error in ensemble method?

The expected error of a learning algorithm can be decomposed into bias and variance. A bias term measures how closely the average classifier produced by the learning algorithm matches the target function. The variance term measures how much the learning algorithm’s prediction fluctuates for different training sets.

38) What is an Incremental Learning algorithm in ensemble?

Incremental learning method is the ability of an algorithm to learn from new data that may be available after classifier has already been generated from already available dataset.

39) What is PCA, KPCA and ICA used for?

PCA (Principal Components Analysis), KPCA ( Kernel based Principal Component Analysis) and ICA ( Independent Component Analysis) are important feature extraction techniques used for dimensionality reduction.

40) What is dimension reduction in Machine Learning?

In Machine Learning and statistics, dimension reduction is the process of reducing the number of random variables under considerations and can be divided into feature selection and feature extraction.

41) What are support vector machines?

Support vector machines are supervised learning algorithms used for classification and regression analysis.

42) What are the components of relational evaluation techniques?

The important components of relational evaluation techniques are

Data Acquisition

Ground Truth Acquisition

Cross Validation Technique

Query Type

Scoring Metric

Significance Test

43) What are the different methods for Sequential Supervised Learning?

The different methods to solve Sequential Supervised Learning problems are

Sliding-window methods

Recurrent sliding windows

Hidden Markow models

Maximum entropy Markow models

Conditional random fields

Graph transformer networks

44) What are the areas in robotics and information processing where sequential prediction problem arises?

The areas in robotics and information processing where sequential prediction problem arises are

Imitation Learning

Structured prediction

Model based reinforcement learning

45) What is batch statistical learning?

Statistical learning techniques allow learning a function or predictor from a set of observed data that can make predictions about unseen or future data. These techniques provide guarantees on the performance of the learned predictor on the future unseen data based on a statistical assumption on the data generating process.

46) What is PAC Learning?

PAC (Probably Approximately Correct) learning is a learning framework that has been introduced to analyze learning algorithms and their statistical efficiency.

47) What are the different categories you can categorized the sequence learning process?

Sequence prediction

Sequence generation

Sequence recognition

Sequential decision

48) What is sequence learning?

Sequence learning is a method of teaching and learning in a logical manner.

49) What are two techniques of Machine Learning?

The two techniques of Machine Learning are

Genetic Programming

Inductive Learning

50) Give a popular application of machine learning that you see on day to day basis?

The recommendation engine implemented by major ecommerce websites uses Machine Learning.

These interview questions will also help in your viva(orals)

Comment: Apple Pay Is Available At More Places Than Ever, But Key Opportunities Remain

Two years since first launching in the US, Apple Pay is an option at lots of payment terminals all across the country, but you still can’t assume contactless payments like Apple Pay will be supported. It’s still necessary to carry your debit or credit card (or cash) just in case.

So we’ve looked at the biggest categories of businesses that still aren’t ready for contactless payments in the US and reached out to a few of the biggest holdouts to ask why they’re still resisting. We’ve also looked at some of the categories that people still want supported to see how things are progressing.

When Apple Pay launched in October 2014, there were about eight bank partners and 220,000 terminals ready in the US. Now Apple Pay is supported by hundreds of banks and in February over 2 million businesses were accepting contactless payments. Apple Pay has also expanded to 10 additional markets outside of the United States with more coming soon.

Two years ago, I could use Apple Pay at my local McDonalds, Walgreens, Subway, and a local drug store. Apple Pay was easier to use in large cities with more stores than small towns with a handful of businesses. Now I can use Apple Pay at plenty of local businesses thanks to the adoption of contactless payment terminals.

Now places where I regularly shop like Best Buy, Khols, Nike, American Eagle, Starbucks, Ulta for my wife, one of the two nearby movie theaters, Chick-Fil-A and plenty more officially support Apple Pay, so my usage has increased. Walgreens even lets you use their loyalty card with Apple Pay; the cashiers are usually grateful that they don’t have to scan yours or ask you to type in your phone number.

A local ice cream shop in my town that uses Square recently upgraded to the new contactless reader which enables Apple Pay support, and my local grocery store (a two store chain called Jerry Lees) knows about and accepts Apple Pay.

I wrote about the awkwardness of Apple Pay sometimes failing two years ago; when the cashier at my local grocery store actually suggested I pay with my watch that stigma was broken for me. Some people are still impressed which is fun, but for a lot of people it’s just the new normal which is even better.

I jokingly tweeted a few weeks ago that Apple Pay has officially gone mainstream when I noticed the place where I get my haircut taping a sign about not accepting Apple Pay on their terminal. Clearly enough people asked and expected to use Apple Pay that a sign was necessary.

Apple Pay is also available at almost all places with the contactless payment logo even if there isn’t specifically signage for Apple Pay. The EMV contactless symbol looks like four waves, a small square, a hand, and an oval. When you see this, it means you can probably use Apple Pay (although a few places like CVS manually disable Apple Pay).

You can also find Apple Pay in iPhone, iPad, and Apple Watch apps and even on the web from iPhone, iPad, and Mac on a growing number of websites.

Apple Pay is in a lot more stores today than two years ago, but you still can’t totally replace your debit or credit card with it. Apple regularly works with banks and credit unions across the country to add Apple Pay support and now nearly all banks are supported. But readers often tell us that they want to hear about new businesses accepting Apple Pay too.

Flipping that switch is a much bigger task for businesses — it involves replacing payment terminals for one — but some businesses have contactless-ready terminals and block Apple Pay or have chip reader terminals that don’t work with contactless payments.

I posed this question on Twitter yesterday: What are the top three places where you’d want to see Apple Pay accepted if you could flip a switch? The responses were very helpful in understanding where the biggest opportunities for Apple Pay adoption exist.

The first four were the most popular responses by far with the other results getting a mention or two:

Regional grocery store chains

Target

Walmart

Gas stations at the pump

Local shops

Laundry machines

Bus fare

Restaurants (split billing, tips, kiosks on tablets, all sit down restaurants)

Doctor’s offices

CVS

In-N-Out Burger

Parking meters

ATMs

Vending machines

At least one person responded to say that they’re still waiting for their US bank to support Apple Pay, and people in Spain and Germany said they just want support in their countries. Several people in Canada responded to say the question didn’t really apply to them because Apple Pay is generally accepted everything.

I agree with all of the responses above plus the people who said generally anywhere cash is accepted or where credit card readers are available. Solutions like Square’s contactless card reader (and competitors) help make that possible for almost any business. I would also add drive-thrus to the list: plenty of Apple Pay fast food chains don’t properly support drive-thru windows.

Let’s look at a few items on the list starting with gas stations. Chevron has said it is planning to accept Apple Pay at the pump but I can report that this hasn’t extended to my neck of the woods yet. Texaco under Chevron is also listed as an Apple Pay partner, but my suspicion is that some of these terminals are found inside and not at the pump. Paying for gas with a card is more convenient for filling up since you don’t know how much to pay ahead of time, and going inside is only really necessary with cash.

The only way I’ve been able to Apple Pay for gas in my area is with the Exxon Mobil Speedpass+ app. It’s a bit of a hack: you Apple Pay in the app then fuel up rather than wave your iPhone or Apple Watch by a terminal but it works. It’s just not worth driving out of your way for if you don’t live near a station.

Sit down restaurants can accept Apple Pay, which is way more ideal than handing a stranger your debit or credit card to walk away with and swipe. But this is currently limited to select OpenTable restaurants in certain regions.

It’s possible for vending machines and ATMs to work with Apple Pay. Chase and Bank of America already have Apple Pay ATMs that let you withdraw cash with your iPhone or Apple Watch rather than your debit card. (I’ve never seen one personally except on Twitter.) We heard about Apple Pay vending machines and parking spots two years ago but it’s clear replacing hardware takes a while. I’ve seen Coke vending machines with contactless payments and their loyalty card works with Apple Pay.

Doctor’s offices are also a great suggestion. Medical billing for me usually involves passing back a credit card over the counter, but I’m sure there are some offices that accept Apple Pay. I’d love an office that accepts Apple Pay and shares medical records for importing into the Health app.

CVS has the contactless payment hardware in place but goes out-of-the-way to not accept Apple Pay. This was initially because of CurrentC, a competing mobile payment service that fizzled out before launch, and now has more to do with CVS Pay. 9to5Mac asked CVS about the current state of Apple Pay at their stores and received this statement:

We currently do not offer Apple Pay. Today we announced the rollout of our CVS Pay solution chainwide and right now we focused on creating the best customer shopping experience and building loyalty within our own Mobile App. With CVS Pay, our customers get more than just payment functionality: they get a convenient and integrated experience that simplifies the entire checkout experience, from picking up their prescription to earning ExtraCare deals. We will continue to evaluate all potential Mobile Payment solutions that would be able to provide a great shopping experience for our customers.

Walmart doesn’t actively block Apple Pay but similarly provides their own mobile payment service that requires an app, Walmart Pay. When asked about supporting Apple Pay in stores, Walmart replied that they are open to a solution that could work similar to Exxon Mobile Speedpass+ but not straight forward Apple Pay:

We made a strategic decision to design Walmart Pay to work with almost any smartphone and accept almost any payment type – even allowing for the integration of other mobile wallets in the future. We are currently evaluating several mobile wallets we could potentially integrate into Walmart Pay in the future.

The thing for me is this: my local grocery store may have higher prices than Walmart (I’m not comparing but this is probably true), but I’ll go to my grocery store over Walmart whenever possible just to use Apple Pay. The same is true for Walgreens or Rite-Aid over CVS.

Hopefully when we revisit this topic in another year, the list continues to shrink even more.

I’ll wrap up with a few resources for businesses that don’t accept Apple Pay yet but would consider it. Square Contactless is an affordable card reader that works with Apple Pay, Android Pay, and chip cards and financing options are available to make deploying easier. I did a quick hands-on demo earlier this year to try it in action. I’ve since seen it used in at least two local businesses.

Apple also has resources for businesses and Apple Pay:

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