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Let’s have a look at these 7 signs to know whether you are a potential data scientist or not


Data Science has not just become the “Sexiest Job of the 21st Century” but one of the most exciting job roles. You get to make an impact at the company-wide level with the latest technology and algorithms.

How does a person know that he needs to pursue Data Science? What are the qualities of a Data Scientist that one must look for? He may be interested in coding, building new tech, is patient at debugging. For example, a mathematics aspirant knows he wants to become a mathematician due to his interest in mathematical concepts and a knack for solving problems. So what are the signs that define a data scientist and one must look within themselves to know if they are a potential data scientist or not?

The role of a data scientist is really crucial to the whole organization and the economy as a whole. But the problem is – there is a shortage of “Skilled” data scientists globally. The AI and ML Blackbelt+ program aims to make you an industry-ready certified data science professional with 14+ courses, 39+ real-life projects, and 1:1 mentorship sessions so that you are never off-track.

If you are new to the world of data science, I suggest you go through the data science roadmap –

Table of Contents

Love Number Crunching

You are always up for solving Puzzles

Enjoy solving unstructured problems

You are curious – always asking “Why?”

You have a knack for problem-solving

Enjoy deep research

Love telling Stories – Great at presenting

1. Love Number Crunching and Solving Puzzles

If you love crunching numbers and solving logical problems based on probability, statistics, puzzles then chances are that you have a natural tendency of becoming a data scientist or a business analyst. By “love”, I don’t mean calculating the bill split among your friends accurately, but a craze that reaches to next level.

For example, guessing the number of boys in India that are under 15 years. This is known as guess estimate and Data science interviewers love asking these questions. I have found the perfect article for you to understand problem-solving as data scientists –

2. Enjoy solving unstructured problems

Unstructured problems are everywhere around us. In an Edtech company, the management may be asking – how do we increase the revenue on our courses? Whereas in a social networking company, the question maybe – How do we increase the user retention on our app. Do you notice something wrong with these questions? Well, these are unstructured problems.

An important aspect of being a data scientist is the ability to form well-defined goals. A structured approach to the above question can be – To retain 20% more customers through the slide-down feature in the next 3 months. The goal should represent What? Why? How?

Next time, if you find yourself in the middle of a problem and you find yourself breaking down the problem into smaller goals, this might be a good starting sign of becoming a data scientist.

3. You are curious – always asking “Why?”

Do you find yourself questioning other people’s assumptions? Are you not able to end your day without asking “Why this? Why that? Why this over that?” You may be a natural fit to become a data scientist.

For example, in the above example, you may want to question the management and ask – Why do we want to retain customers? Can we retain only the paying customers? why focus on retention rather than user acquisition?

Some of the best data scientists would stop anyone and ask for a rationale if they are not clear – Why did you ask this question? What was your thought process? Why do you assume so? are just a few examples of these questions!

Do you find yourself in the middle of these signs? You love problem-solving but don’t love mathematics by heart? A lot of these qualities can be gained with practice. The AI and ML Blackbelt+ certified Data Science program aims to take you from zero-to-hero with its 14+ courses, 39+ real-life projects, and 1:1 mentorship sessions with experts!

4. You have a knack for problem-solving

The easiest sign that tells you are a potential data scientist is the addiction to solving problems. The fridge is not working? You try to identify the root cause of the problem. The sales of your company have decreased the quarter, you try to understand the root cause of the problem.

Data Scientist’s role is not just to apply machine learning algorithms to build an accurate model, it is to formulate a problem statement, form a hypothesis, data analysis, data model, and then finding the best results and communicating to the management. Each of these steps requires a knack for solving problems.

5. Enjoy Deep Research

A Data scientist needs to sit on a single problem statement for a long duration of time, going back and forth to the stakeholders to understand their requirements, trying out different hypotheses, mining data, different modeling techniques until the results have been achieved.

Are you someone who finds themselves not giving up until the problem has been solved? Studying and Googling and researching on a single problem. When was the last time you spent hours and hours immersed in solving a problem? Can you do that again and again?

6. Love telling Stories – Great at presenting

There is always a person in the room who tells amazing stories. No one can resist listening to him and he impresses everyone with his storytelling skills. Are you that person?

A Data scientist needs to be great at storytelling. What is the use of all the hard work, if he is not able to influence his stakeholders? Communicating with data and presenting stories backed by data is one of the most important elements in the life of a data scientist.

7. Love Experimentations

Do you find yourself experimenting at any time in the day? I don’t mean science experiments. A part of being a data scientist is to be curious.

What is the best mode of transportation to the office? If I find traffic at the X traffic signal, what is my estimated time of arrival? What if I travel halfway through the bus and then take the metro at the signal where I find traffic 90% of the time? These problems seem trivial but a large part of your life is going to be filled with experimentations.

If you love to experiment and play with curiosity in your daily life then being a data scientist is going to be a fun job for you!

End Notes

Being a data scientist is one of the most exciting roles and I love being part of this awesome community.

Were you able to relate to the points mentioned above? You are probably going to enjoy being a data scientist and do some amazing work in this field. The AI and ML Blackbelt+ program aims to nurture your talent as a data scientist and make you an industry-ready professional. You can check out the program here.

Which point were you able to relate to the most?


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Data Scientist Vs Data Analyst: Key Differences Explained

In the world of data-driven decisions, two prominent roles have emerged: data analysts and data scientists. These professionals play a crucial role in helping organizations harness the power of data, but their responsibilities and skill sets are quite different.

Data analysts focus on using data visualization and statistical analysis to understand data and identify patterns. They are usually required to have at least a bachelor’s degree in a relevant field like mathematics, statistics, computer science, or finance.

Broadly speaking, both professions involve extracting valuable insights from data; however, their approaches and skill sets do vary.

In this article, we will explore the differences between data scientists and data analysts and highlight the unique skills and responsibilities required for each role.

Let’s dive in.

While data scientists and data analysts both work with data, they have distinct roles and responsibilities.

Understanding the differences between these two roles is important for organizations seeking to build an effective data team. Also, it is crucial for those that would like a career in data to understand.

In this section, we will explore the key differences between data scientists and data analysts, including their educational backgrounds, technical skills, and the types of problems they are typically tasked with solving.

The table below gives a quick overview of the differences between the two roles:

Education/BackgroundData ScientistData AnalystDegreeBachelor’s degree in business, economics, statistics, or a related fieldBachelor’s degree in business, economics, statistics, or related fieldProgramming skillsProficient in languages such as Python, R, and SQLProficient in Excel, SQL, and basic scripting languagesMathematics skillsStrong mathematical skills, including linear algebra, calculus, and statisticsStrong statistical skills, including regression analysis and hypothesis testingWork experienceExperience with big data technologies, machine learning, and data visualizationExperience with statistical analysis, data modeling, and reporting

Joining a boot camp, using tutorials, or completing online courses or certificate programs may not cut it.

Data scientists should have a strong foundation in mathematics, statistics, and computer science, as well as hands-on experience with programming languages such as Python, R, and SQL.

Other data analyst skills include working with databases and having basic scripting language skills.

The job involves working on large data sets, developing predictive models, and extracting insights from data. Like data analysts, it also requires soft skills like communication and collaboration since you often need to work with different teams.

Data analysts: Very simply, a data analyst’s job involves analyzing and interpreting data to provide insights and recommendations to stakeholders.

You may be tasked with working with different data sources to identify trends and patterns that can inform business decisions.

Some specific responsibilities of data analysts can include:

Collecting, cleaning, and organizing data from various sources

Conducting statistical analysis to identify trends and patterns in data using software like Tableau

Creating reports and dashboards to visualize data and communicate insights to stakeholders

Identifying areas for process improvement and making data-driven recommendations to stakeholders

Developing and maintaining databases and data systems to support data analysis

Keeping up-to-date with the latest trends and developments in data analysis and visualization.

Now, things get a little more complex.

Data scientists: Being a data scientist involves analyzing complex data sets, developing predictive models, and extracting insights from data.

They work closely with stakeholders across different departments to provide insights and recommendations based on their data analysis.

Some specific responsibilities of data scientists include:

Conducting exploratory data analysis to identify patterns and trends in data

Developing predictive models using statistical and machine learning techniques

Building and testing machine learning models to improve predictive accuracy

Using problem-solving skills and business intelligence to come up with data-driven solutions to business problems

Communicating complex findings and recommendations to non-technical stakeholders

Collaborating with data engineers and software developers to build and deploy data-driven solutions

In the next two sections, we’ll take a look at the future job prospects and salary expectations for the two professions.

The job outlook for data scientists in 2023 is very promising as organizations across industries continue to collect and analyze increasing amounts of data.

According to the U.S. Bureau of Labor Statistics (BLS), employment of data scientists is projected to grow by 36% from 2023 to 2031, which is much faster than the average when compared to other occupations. Job opportunities in the field are driven by the increasing use of data and analytics to drive decision-making in organizations of all sizes.

According to Glassdoor, the national average salary for data scientists in the United States is around $103,000 per year. Many organizations also offer various additional forms of compensation for data scientists, such as bonuses, equity, and other benefits like medical insurance and paid time off.

Please note that compensation can vary widely depending on location, industry, and years of experience.

According to the BLS, employment of management analysts (which includes data analyst careers) is projected to grow by 11% from 2023 to 2030. Like data scientists, the job outlook for data analysts is very positive for the foreseeable future.

Compensation for data analysts may vary based on factors such as experience, industry, and location. Entry-level data analysts typically earn lower salaries, they can expect their pay to increase as their skills and expertise develop over time.

In terms of salary, the national average for data analyst positions in the United States is around $65,850 per year, according to Glassdoor.

The job prospects and compensation for both data scientists and data analysts are very promising, but how can you decide which career is right for you? We’re going to take a look at factors to consider in the next section.

Deciding which career path is right for you can feel daunting, but think of it as an exciting opportunity to explore this wonderful world of data!

The two fields may seem similar at first glance, and in a way, they are, but they require different skill sets and offer unique career paths.

With the right information and guidance, you can choose the path that is best suited for your skills, interests, and career goals.

In this section, we’ll provide some tips and insights to help you navigate this decision and choose the right path for you.

When considering a career in data science or data analysis, it’s important to think about your skills, interests, and career goals.

Here are some specific factors to consider:

Roles and responsibilities: Data scientists are often responsible for more strategic and complex initiatives, such as developing predictive models or creating machine learning algorithms. Data analyst roles focus more on day-to-day operations and providing insights to stakeholders.

Job outlook and salary: Both data scientists and data analysts have strong job prospects and competitive salaries, but the specific job outlook and salary can vary depending on the industry, location, and years of experience.

Ultimately, the right path for you will come down to your individual goals and aspirations.

Now one great thing about data skills is that they can be applied in most industries, let check them out.

The field of data science and data analytics is in high demand across a wide range of industries and company types.

Here are some examples of industries that both commonly employ data scientists and data analysts:

Finance and Banking: The finance and banking industry relies heavily on data analytics to identify trends, assess risk, and make informed business decisions. Business analysts are in high demand.

Healthcare: Healthcare organizations use data science and data analytics to improve patient outcomes, manage resources, and drive innovation in medical research.

E-commerce: E-commerce companies use data analytics to better understand their customer’s behavior, preferences, and purchasing habits in order to improve marketing and sales strategies.

Technology: Technology companies use data science and data analytics to develop new products and services, improve user experiences, come up with real-world solutions, and identify areas for innovation and growth.

There are employment opportunities across different company types, including startups, large corporations, consulting firms, and government agencies.

Understanding the diverse range of industries and company types that rely on data professionals is crucial for individuals looking to build successful careers in these fields.

It’s also important to note that both fields are evolving, and there are emerging trends that are worth considering.

In addition to industry types, consider emerging trends in data science and data analytics that are changing the landscape of the two fields.

Here are some current trends that are shaping the future of data science and data analytics:

Artificial intelligence and machine learning: AI and machine learning are increasingly being used in data science and data analytics to automate data processing, identify patterns, and make predictions. These technologies have the potential to revolutionize industries from healthcare to finance to marketing.

Cloud computing: Cloud computing has made it easier and more cost-effective to store, manage, and analyze large amounts of data. As cloud infrastructure and technology continue to improve, it’s expected that cloud-based data analytics and machine learning will become more widespread.

Data ethics and privacy: As more and more data is collected and analyzed, concerns about data ethics and privacy have come to the forefront. Data scientists and analysts are being called upon to ensure that data is being used ethically and responsibly and to implement measures to protect sensitive data.

Internet of things (IoT): The IoT refers to the network of interconnected devices and sensors that collect and share data. With the increasing adoption of IoT technology, there is a growing need for data scientists and analysts who can manage and analyze the vast amounts of data generated by these devices.

In the world of data, both data scientists and data analysts play important full-time roles in a business. While there are similarities between the two, they possess distinct differences in terms of responsibilities and required skills.

Data analysts primarily focus on working with structured data to solve tangible business problems using SQL, R, or Python programming languages, data visualization tools, and statistical analysis. They help organizations identify trends and derive insights from data.

On the other hand, data scientists are more involved in programming machines, optimizing systems, and creating frameworks and algorithms for collecting usable data. Their primary duties lie in collecting data and designing robust data-driven solutions.

While both job descriptions work within the realm of big data, identifying the right path depends on your interests, skills, and career goals. Whichever path you choose, both data scientists and data analysts are in-demand careers, making them an exciting and rewarding choices for those interested in working with data.

11 Superb Data Science Videos Every Data Scientist Must Watch


Presenting 11 data science videos that will enhance and expand your current skillset

We have categorized these videos into three fields – Natural Language Processing (NLP), Generative Models, and Reinforcement Learning

Learn how the concepts in these videos work and build your own data science project!


I love learning and understanding data science concepts through videos. I simply do not have the time to pour through books and pages of text to understand different ideas and topics. Instead, I get a much better overview of concepts via videos and then pick and choose the topics I want to learn more about.

The sheer quality and diversity of topics available on platforms like YouTube never ceases to amaze. I recently learned about the amazing XLNet framework for NLP from a video (which I have mentioned below for your consumption). This helped me grasp the concept so I could explore more about XLNet!

I strongly believe structure is very necessary when we’re learning any concept or topic. I follow that approach each time I write an article as well. That’s why I’ve categorized these videos into their respective domains, primarily Natural Language Processing (NLP), Generative Models and Reinforcement Learning.

So are you ready to dive in and explore the length and breadth of data science through these fascinating videos?

Without any further ado, here are 11 awesome Data Science Videos:

Natural Language Processing (NLP)

XLNet explained

How does Google Duplex work?

Google’s POEMPORTRAITS: Combining Art and AI

Generative Models

Dive into Variational Autoencoders!

Create Facial Animation from Audio

MuseNet Learned to Compose Mozart, Bon Jovi, and More

Reinforcement Learning

Teaching the Computer to drive

Learn how AlphaGo Zero works

Google DeepMind AI learns to walk

AI learns to play 2048


Adobe develops AI to detect Photoshopped Faces

XLNet Explained

XLNet is the hottest framework in NLP right now. You simply must be aware of what it is and how it works if you want to carve out a career in this field. I came across this video recently and wanted to share it with the community as soon as possible.

XLNet is the latest state-of-the-art NLP framework. It has outperformed Google’s BERT on 20 NLP tasks and achieved state-of-the-art results on 18 of them. That is very, very impressive.

Make sure you check out our article covering XLNet and it’s powerful ability here.

The below video provides a clear explanation of the original XLNet research paper. Note: You might need to know a few NLP concepts beforehand to truly grasp the inner workings of XLNet.

How does Google Duplex work?

Remember when Sundar Pichai went on stage and sent the whole world into a frenzy when he unveiled Google Duplex in his keynote at Google I/O 2023? I remember listening in complete awe to the super-realistic calls that the AI made.

It took a bit of time for the data science and NLP community to come up with an explanation as to how Google Duplex actually works. It’s pretty powerful and has the potential to change how we interact with machines.

So the million dollar question – did Google Duplex pass the Turing Test!? You decide after watching this video:

Google’s POEMPORTRAITS: Combining Art and AI

I am an artist and the prospect of combining any art form with Artificial Intelligence is extremely enticing. In a world where there is so much fear around AI, such applications are more than welcome.

Google’s POEMPORTRAITS AI has been trained on nineteenth-century poetry using NLP techniques. You can contribute and donate a word to generate your own POEMPORTRAIT. Check out how this awesome concept works:

Generative Models Dive into Variational Autoencoders!

Here’s one of our favorite reinforcement learning experts Xander Streenbrugge from his wonderful ArxivInsights channel.

Variational Autoencoders (VAEs) are powerful generative models with diverse applications. You can generate human faces or synthesize your own music or use VAEs for removing noise from images.

I like this video a lot. Xander begins with an introduction to basic autoencoders and then goes into VAEs and disentangled beta-VAEs. Quite technical, but explained beautifully and concisely, in typical Xander style.

Xander is coming back to DataHack Summit this year so you can hear from him and meet him in person!

Create Facial Animation from Audio

I was immediately drawn to the video when I read the title. This is Generative Models at their best! You can not only generate facial animation from audio but also generate different emotions for the same audio. And the facial expressions look incredibly natural.

If you aren’t following Two Minute Papers, you’re missing out. They regularly churn out videos breaking down the latest developments in easy-to-understand fashion. It’s a gem of a channel.

MuseNet Learned to Compose Mozart, Bon Jovi, and More

OpenAI’s MuseNet is a deep neural network that generates musical compositions with different instruments and combines different styles. It uses the same general-purpose unsupervised technology as GPT-2 and the results are amazing.

Never heard of GPT-2? It’s an NLP framework on par with XLNet. Check out how MustNet works here:

Reinforcement Learning Teaching a Computer to Drive

Self-driving cars have always fascinated me. The sheer scale of an autonomous vehicle’s project is staggering. There are so many components, both on the hardware side as well as the data science side, that need to align for this project to work.

This is a perfect video for beginners to learn about Genetic Programming and Reinforcement Learning and how they are used to create powerful applications. Simon’s personality kept me hooked until the very end.

And I am definitely trying the project on my own.

Learn how Google DeepMind’s AlphaGo Zero works

Another great video by Xander. He explains Google DeepMind’s popular paper on AlphaGo Zero.

AlphaGo Zero is a new version of the original AlphaGo program that beat human champion Lee Sedol comprehensively. I recommend reading our article on Monte Carlo Tree Search, the algorithm behind AlphaGo before proceeding to learn about AlphaGo Zero.

AlphaGo Zero uses Reinforcement Learning to beat the world’s leading Go players without using data from human games.

“AlphaGo Zero surpassed the strength of AlphaGo Lee in three days by winning 100 games to 0, reached the level of AlphaGo Master in 21 days, and exceeded all the old versions in 40 days.”

Source: Wikipedia

Google DeepMind’s AI learns to Walk

This video is both hilarious and informative. Exactly the type of video I like when I’m learning new things! It was funny to watch the AI learn to walk. But at the same time, it left me marveling at the power of Reinforcement Learning.

The video discusses 3 papers to try and explain how the AI learned to walk and it is surprisingly simple to understand.

AI learns to play 2048

Have you ever played the 2048 game? It is super addictive once you get the hang of it. I used to easily finish games earlier but not anymore. Being a data science enthusiast, I am going to train my computer to play it with the help of this awesome video.

This is another example of the use of Genetic Programming and Evolutionary Algorithms.

BONUS: Adobe develops AI to detect Photoshopped Faces

Adobe is a market leader in image and video manipulation software. Other companies have tried, but not many have even gotten close to Adobe’s level.

Last month, Adobe announced its research efforts to detect manipulated images. High time someone did that! It will soon be impossible to tell real from fake given how quickly GANs have taken over the world.

Imagine Donald Trump challenging Kim Jong Un to a nuclear war and then claiming that it was a deepfake and shrugging off all responsibility! We need to avoid those situations turning into reality. This video shows how Adobe’s algorithm works and tried to combat fake images:

End Notes

I love knowing about the latest research in data science, machine learning and AI. But I find it hard to read papers. It takes a lot of time and effort – something not every data science professional has. I am sure many of you struggle with the same. Consuming videos is the ideal way to get an overview of these concepts.

You can then pick and choose where your interests lie and try to spin up a project or blog post on it. Trust me, it’s a wonderful way to learn and ingrain new data science concepts.


40 Questions To Test A Data Scientist On Deep Learning


Deep Learning has made many practical applications of machine learning possible. Deep Learning breaks down tasks in a way that makes all kinds of applications possible. This skilltest was conducted to test your knowledge of deep learning concepts.

A total of 853 people registered for this skill test. The test was designed to test the conceptual knowledge of deep learning. If you are one of those who missed out on this skill test, here are the questions and solutions. You missed on the real time test, but can read this article to find out how you could have answered correctly.

Here are the leaderboard ranking for all the participants.

Overall Scores

Below are the distribution scores, they will help you evaluate your performance.

You can access the final scores here. More than 270 people participated in the skill test and the highest score obtained was 38. Here are a few statistics about the distribution.

Mean Score: 15.05

Median Score: 18

Mode Score: 0

Useful Resources

A Complete Guide on Getting Started with Deep Learning in Python

The Evolution and Core Concepts of Deep Learning & Neural Networks

Practical Guide to implementing Neural Networks in Python (using Theano)

Fundamentals of Deep Learning – Starting with Artificial Neural Network

An Introduction to Implementing Neural Networks using TensorFlow

Fine-tuning a Keras model using Theano trained Neural Network & Introduction to Transfer Learning

6 Deep Learning Applications a beginner can build in minutes (using Python)

Questions & Answers

1) The difference between deep learning and machine learning algorithms is that there is no need of feature engineering in machine learning algorithms, whereas, it is recommended to do feature engineering first and then apply deep learning.



Solution: (B)

Deep learning itself does feature engineering whereas machine learning requires manual feature engineering.

2) Which of the following is a representation learning algorithm?

A) Neural network

B) Random Forest

C) k-Nearest neighbor

D) None of the above

Solution: (A)

Neural network converts data in such a form that it would be better to solve the desired problem. This is called representation learning.

3) Which of the following option is correct for the below-mentioned techniques?

AdaGrad uses first order differentiation

L-BFGS uses second order differentiation

AdaGrad uses second order differentiation

L-BFGS uses first order differentiation

A) 1 and 2

B) 3 and 4

C) 1 and 4

D) 2 and 3

Solution: (A)

Option A is correct.


4) Increase in size of a convolutional kernel would necessarily increase the performance of a convolutional neural network. 



Solution: (B)

Kernel size is a hyperparameter and therefore by changing it we can increase or decrease performance.


Question Context 

Now you want to use this model on different dataset which has images of only Ford Mustangs (aka car) and the task is to locate the car in an image.

5) Which of the following categories would be suitable for this type of problem?

A) Fine tune only the last couple of layers and change the last layer (classification layer) to regression layer

B) Freeze all the layers except the last, re-train the last layer

C) Re-train the model for the new dataset

D) None of these

Solution: (A)


6) Suppose you have 5 convolutional kernel of size 7 x 7 with zero padding and stride 1 in the first layer of a convolutional neural network. You pass an input of dimension 224 x 224 x 3 through this layer. What are the dimensions of the data which the next layer will receive? 

A) 217 x 217 x 3

B) 217 x 217 x 8

C) 218 x 218 x 5

D) 220 x 220 x 7

Solution: (C)


7) Suppose we have a neural network with ReLU activation function. Let’s say, we replace ReLu activations by linear activations.

Would this new neural network be able to approximate an XNOR function? 

Note: The neural network was able to approximate XNOR function with activation function ReLu.

A) Yes

B) No

Solution: (B)

If ReLU activation is replaced by linear activation, the neural network loses its power to approximate non-linear function.


8) Suppose we have a 5-layer neural network which takes 3 hours to train on a GPU with 4GB VRAM. At test time, it takes 2 seconds for single data point. 

Now we change the architecture such that we add dropout after 2nd and 4th layer with rates 0.2 and 0.3 respectively.

What would be the testing time for this new architecture?

A) Less than 2 secs

B) Exactly 2 secs

C) Greater than 2 secs

D) Can’t Say

Solution: (B)

The changes is architecture when we add dropout only changes in the training, and not at test time.


9) Which of the following options can be used to reduce overfitting in deep learning models?

Add more data

Use data augmentation 

Use architecture that generalizes well

Add regularization

Reduce architectural complexity

A) 1, 2, 3

B) 1, 4, 5

C) 1, 3, 4, 5

D) All of these

Solution: (D)

All of the above techniques can be used to reduce overfitting.


10) Perplexity is a commonly used evaluation technique when applying deep learning for NLP tasks. Which of the following statement is correct?

A) Higher the perplexity the better

B) Lower the perplexity the better

Solution: (B)


11) Suppose an input to Max-Pooling layer is given above. The pooling size of neurons in the layer is (3, 3).

What would be the output of this Pooling layer?

A) 3

B) 5

C) 5.5

D) 7

Solution: (D)

Max pooling works as follows, it first takes the input using the pooling size we defined, and gives out the highest activated input.


12) Suppose there is a neural network with the below configuration. 

If we remove the ReLU layers, we can still use this neural network to model non-linear functions.



Solution: (B)


13) Deep learning can be applied to which of the following NLP tasks?

A) Machine translation

B) Sentiment analysis

C) Question Answering system

D) All of the above

Solution: (D)

Deep learning can be applied to all of the above-mentioned NLP tasks.


14) Scenario 1: You are given data of the map of Arcadia city, with aerial photographs of the city and its outskirts. The task is to segment the areas into industrial land, farmland and natural landmarks like river, mountains, etc.

Deep learning can be applied to Scenario 1 but not Scenario 2.



Solution: (B)

Scenario 1 is on Euclidean data and scenario 2 is on Graphical data. Deep learning can be applied to both types of data.


15) Which of the following is a data augmentation technique used in image recognition tasks?

Horizontal flipping

Random cropping

Random scaling

Color jittering

Random translation

Random shearing

A) 1, 2, 4

B) 2, 3, 4, 5, 6

C) 1, 3, 5, 6

D) All of these

Solution: (D)


16) Given an n-character word, we want to predict which character would be the n+1th character in the sequence. For example, our input is “predictio” (which is a 9 character word) and we have to predict what would be the 10th character.

Which neural network architecture would be suitable to complete this task?

A) Fully-Connected Neural Network

B) Convolutional Neural Network

C) Recurrent Neural Network

D) Restricted Boltzmann Machine

Solution: (C)

Recurrent neural network works best for sequential data. Therefore, it would be best for the task.


17) What is generally the sequence followed when building a neural network architecture for semantic segmentation for image?

A) Convolutional network on input and deconvolutional network on output

B) Deconvolutional network on input and convolutional network on output

Solution: (A)


18) Sigmoid was the most commonly used activation function in neural network, until an issue was identified. The issue is that when the gradients are too large in positive or negative direction, the resulting gradients coming out of the activation function get squashed. This is called saturation of the neuron.

That is why ReLU function was proposed, which kept the gradients same as before in the positive direction.

A ReLU unit in neural network never gets saturated.



Solution: (B)

ReLU can get saturated too. This can be on the negative side of x-axis.


19) What is the relationship between dropout rate and regularization?

Note: we have defined dropout rate as the probability of keeping a neuron active?

A) Higher the dropout rate, higher is the regularization

B) Higher the dropout rate, lower is the regularization

Solution: (B)

Higher dropout rate says that more neurons are active. So there would be less regularization.


20) What is the technical difference between vanilla backpropagation algorithm and backpropagation through time (BPTT) algorithm?

A) Unlike backprop, in BPTT we sum up gradients for corresponding weight for each time step

B) Unlike backprop, in BPTT we subtract gradients for corresponding weight for each time step

Solution: (A)

BPTT is used in context of recurrent neural networks. It works by summing up gradients for each time step


21) Exploding gradient problem is an issue in training deep networks where the gradient getS so large that the loss goes to an infinitely high value and then explodes.

What is the probable approach when dealing with “Exploding Gradient” problem in RNNs?

A) Use modified architectures like LSTM and GRUs

B) Gradient clipping

C) Dropout

D) None of these

Solution: (B)

To deal with exploding gradient problem, it’s best to threshold the gradient values at a specific point. This is called gradient clipping.


22) There are many types of gradient descent algorithms. Two of the most notable ones are l-BFGS and SGD. l-BFGS is a second order gradient descent technique whereas SGD is a first order gradient descent technique.

In which of the following scenarios would you prefer l-BFGS over SGD?

Data is sparse

Number of parameters of neural network are small

A) Both 1 and 2

B) Only 1

C) Only 2

D) None of these

Solution: (A)

l-BFGS works best for both of the scenarios.


23) Which of the following is not a direct prediction technique for NLP tasks?

A) Recurrent Neural Network

B) Skip-gram model


D) Convolutional neural network

Solution: (C)


24) Which of the following would be the best for a non-continuous objective during optimization in deep neural net?



C) AdaGrad

D) Subgradient method

Solution: (D)

Other optimization algorithms might fail on non-continuous objectives, but sub-gradient method would not.


25) Which of the following is correct?

Dropout randomly masks the input weights to a neuron

Dropconnect randomly masks both input and output weights to a neuron

A) 1 is True and 2 is False

B) 1 is False and 2 is True

C) Both 1 and 2 are True

D) Both 1 and 2 are False

Solution: (D)

In dropout, neurons are dropped; whereas in dropconnect; connections are dropped. So both input and output weights will be rendered in useless, i.e. both will be dropped for a neuron. Whereas in dropconnect, only one of them should be dropped


26) While training a neural network for image recognition task, we plot the graph of training error and validation error for debugging.

What is the best place in the graph for early stopping?

A) A

B) B

C) C

D) D

Solution: (C)

You would “early stop” where the model is most generalized. Therefore option C is correct.


27) Research is going on to solve image inpainting problems using computer vision with deep learning. For this, which loss function would be appropriate for computing the pixel-wise region to be inpainted?

Image inpainting is one of those problems which requires human expertise for solving it. It is particularly useful to repair damaged photos or videos. Below is an example of input and output of an image inpainting example.

A) Euclidean loss

B) Negative-log Likelihood loss

C) Any of the above

Solution: (C)

Both A and B can be used as a loss function for image inpainting problem.

A) Sum of squared error with respect to inputs

B) Sum of squared error with respect to weights

C) Sum of squared error with respect to outputs

D) None of the above

Solution: (C)

29) Mini-Batch sizes when defining a neural network are preferred to be multiple of 2’s such as 256 or 512. What is the reason behind it?

A) Gradient descent optimizes best when you use an even number

B) Parallelization of neural network is best when the memory is used optimally

C) Losses are erratic when you don’t use an even number

D) None of these

Solution: (B)


30) Xavier initialization is most commonly used to initialize the weights of a neural network. Below is given the formula for initialization.

If weights at the start are small, then signals reaching the end will be too tiny.

If weights at the start are too large, signals reaching the end will be too large.

Weights from Xavier’s init are drawn from the Gaussian distribution.

Xavier’s init helps reduce vanishing gradient problem.

Xavier’s init is used to help the input signals reach deep into the network. Which of the following statements are true?

A) 1, 2, 4

B) 2, 3, 4

C) 1, 3, 4

D) 1, 2, 3

E) 1, 2, 3, 4

Solution: (D)

All of the above statements are true.


31) As the length of sentence increases, it becomes harder for a neural translation machine to perform as sentence meaning is represented by a fixed dimensional vector. To solve this, which of the following could we do?

A) Use recursive units instead of recurrent

B)Use attention mechanism

C) Use character level translation

D) None of these

Solution: (B)

32) A recurrent neural network can be unfolded into a full-connected neural network with infinite length.



Solution: (A)

Recurrent neuron can be thought of as a neuron sequence of infinite length of time steps.


33) Which of the following is a bottleneck for deep learning algorithm?

A) Data related to the problem

B) CPU to GPU communication

C) GPU memory

D) All of the above

Solution: (D)

Along with having the knowledge of how to apply deep learning algorithms, you should also know the implementation details. Therefore you should know that all the above mentioned problems are a bottleneck for deep learning algorithm.


34) Dropout is a regularization technique used especially in the context of deep learning. It works as following, in one iteration we first randomly choose neurons in the layers and masks them. Then this network is trained and optimized in the same iteration. In the next iteration, another set of randomly chosen neurons are selected and masked and the training continues.

A) Affine layer

B) Convolutional layer

C) RNN layer

D) None of these

Solution: (C)

Dropout does not work well with recurrent layer. You would have to modify dropout technique a bit to get good results.


35) Suppose your task is to predict the next few notes of song when you are given the preceding segment of the song.

For example:

The input given to you is an image depicting the music symbols as given below,

Your required output is an image of succeeding symbols.

Which architecture of neural network would be better suited to solve the problem?

A) End-to-End fully connected neural network

B) Convolutional neural network followed by recurrent units

C) Neural Turing Machine

D) None of these

Solution: (B)

CNN work best on image recognition problems, whereas RNN works best on sequence prediction. Here you would have to use best of both worlds!


36) When deriving a memory cell in memory networks, we choose to read values as vector values instead of scalars. Which type of addressing would this entail?

A) Content-based addressing

B) Location-based addressing

Solution: (A)

A) Affine layer

B) Strided convolutional layer

C) Fractional strided convolutional layer

D) ReLU layer

Solution: (C)

Option C is correct. Go through this link.


Question Context 38-40

GRU is a special type of Recurrent Neural Networks proposed to overcome the difficulties of classical RNNs. This is the paper in which they were proposed: “On the Properties of Neural Machine Translation: Encoder–Decoder Approaches. Read the full paper here. 

38) Which of the following statements is true with respect to GRU?

Units with short-term dependencies have reset gate very active.

Units with long-term dependencies have update gate very active

A) Only 1

B) Only 2

C) None of them

D) Both 1 and 2

Solution: (D)


39) If calculation of reset gate in GRU unit is close to 0, which of the following would occur?

A) Previous hidden state would be ignored

B) Previous hidden state would be not be ignored

Solution: (A)


40) If calculation of update gate in GRU unit is close to 1, which of the following would occur? 

A) Forgets the information for future time steps

B) Copies the information through many time steps

Solution: (B)


End Notes

If you missed out on this competition, make sure you complete in the ones coming up shortly. We are giving cash prizes worth $10,000+ during the month of April 2023.

If you have any questions or doubts feel free to post them below.

Check out all the upcoming skilltests here.


10 Signs You Were Born To Be A Digital Marketer

Choosing a career is one of – perhaps the­ – most important decisions you will make. Some people are drawn to one pursuit or another, while others take the time to cycle through careers before they settle on the perfect fit. How do you know which path is right for you?

Here are ten signs you were born to be in digital marketing:

You Know Communication Skills Rule All

As a digital marketer, every second of your day will be occupied with creating, communicating, synthesizing, organizing, and digesting information. From phrasing a sensitive email tactfully to presenting a new campaign proposal to brainstorming copy to reading about updates in your field, every single day is a see-saw of content input and output. The average office worker receives

The average office worker receives 121 emails daily. The average active web user sees an average of 490,000 words per day, more words than The Lord of the Rings trilogy. The average person spends more than 50% of their time online looking at content, be it news sites, social media, or email. One would expect that these figures are even higher for digital marketers who conduct almost all of their business online.

The ability to communicate your message clearly, succinctly, and engagingly reigns above all else. On the flip side, your ability to draw out the main message from material that is imprecise, long-winded, and dull will serve you well. Communication skills operate in both respects.

You can take courses to improve both writing skills and reading comprehension, but it ultimately boils down to practice and influence. Write often, read always. Devour articles, books, and yes, even tweets, by writers who inspire you.

You Have a Creative Spark Waiting to Be Unleashed

Digital marketing is a creative field, and few things in life can compare with the feeling when you zero in on that perfect tagline after hours of tedious back-and-forth – or when you’re struck with an unprovoked lightning bolt of inspiration at 4 am. You know you were born to be in digital marketing if you have an unquenchable desire to share your creativity with the world and see your work bring recognition (and sales) to your clients.

Creativity is often inborn, but it can certainly be nurture. In a survey of CEOs, creativity was highlighted as the #1 most desired skill. It may not seem it, but think of creativity like a muscle that needs to be trained, and creativity in your free time can reap benefits in your professional life. Play an instrument, sketch artwork, attend plays, and listen to comedy routines. Surround yourself with creative expression and tend to your inner creativity.

You Can Change Your Voice

You can fake it ‘til you make it, or you can be proactive in embracing different voices and roles. Sign up for industry newsletters, read niche websites, and create Twitter lists with influential voices in each sector. You want to be on top of it when DropBox is hacked or a postal strike affects gift delivery, not find out weeks after the fact. Immerse yourself in each world and never assume you’ve learned enough.

You Have an Insatiable Hunger for Knowledge

You need to be constantly updating your knowledge about digital marketing, and about your clients and their industries. There’s always a new feature, algorithm update, or hack. You need to have a desire to seek out, read, and digest news, studies, year-end reports, and case studies about digital marketing.

Follow influencers and digital marketing experts and learn from their insights about developments, best practices, and digital marketing resources. At least 64% of marketers use social media for 6+ hours each week; 41% are active on social media for 11+ hours weekly. Supplement your communication skills and creativity with unending scholarship, learning about SEO, web design, graphic design, PR, sales, and the vast number of other fields related to digital marketing.

There is no end to the resources available to aspiring digital marketers, and most of them are free. Take Lynda courses. Get Google AdWords certified. Watch YouTube videos from the world’s leading digital marketers. Attend workshops and seminars in your city. The only limit is your willingness to invest in your future.

You Thrive in an Ever-Evolving Environment

Fingers crossed this never happens, but you might have to handle a PR crisis or chase down a client with an overdue account. You may be smooth sailing, and a new Google algorithm update throws everything into chaos. According to an Adobe survey, 76% of respondents believe marketing has changed more in the past two years than in the previous 50.

Even though you work in digital, you also need to integrate traditional marketing techniques. These ever-evolving environments don’t scare those who were born to be in digital marketing – these scenarios excite them. You get to wear many hats, and impress no matter your role.

You Can Hack It On Your Own…And Play Well With Others

You were born to be in digital marketing if you can work on your own, and succeed in doing so. You need to be extremely self-motivated, organized, and independent. You need to be productive even when no one is there to guide you, and troubleshoot your way out of every dead-end. This is especially true if you set out on your own as a digital marketing consultant.

There are a number of productivity and organization tools you can use to stay on the ball, whether you use Google Calendar, Evernote, Swipes, and more.

At the same time, you need to work well in a team environment. You need to be able to collaborate, delegate, take orders, give orders, and excel whether on your own or as a small cog in a big marketing machine. You need to form a productive and healthy partnership with every client as you work toward shared goals.

You Can Cede the Spotlight, or Step Into it With Poise

As a digital marketing manager, you do work on behalf of your clients. Your best work will often be published and promoted under the names of other people and entities. You were born to be in digital marketing if you are comfortable working in the background and can take pride in your hard work and accomplishments even if you don’t get public recognition. At the same time, you may be asked to take a public role at times, like presenting at a conference or leading a webinar. You are prepared for these instances and approach them with confidence and poise.

You can take courses on public speaking and watch tutorials on how to look more confident and prepared on camera. When you believe in what you’re saying and have a strong understanding of the subject material, it’s a lot easier to buy what you’re selling.

You Embrace Data

Digital marketing is a mix of art and science. You believe in measuring and analyzing the data and making tweaks to improve performance. You aren’t scared of what the numbers say; you welcome them with open arms because they draw a picture of where the results have been and where they need to go.

You learn the terminology and the meaning behind it. You were born to be in digital marketing if terms like CPC, CTR, and ROI feel like home. As much as 42% of B2B marketers point to a lack of quality data as an obstacle in lead generation; you’re eager to change that.

You Never Give Up in the Face of Adversity

You’re going to encounter obstacles in the course of your career as a digital marketer. You will have disappointing campaigns, obstinate clients, creativity slumps, and days/months/years where things don’t go your way. There will always be too little time, too much competition, too little budget, too little data.

You were born to be a digital marketer if you focus on solutions and learning from mistakes, not wallowing in self-pity and doubt. If you take a proactive approach to preventing miscommunications in the first place, even better.

Finally…You Love It

If you flat out love digital marketing, this is a surefire sign that you’ve chosen the right path. From that first meeting with a prospect through the discovery, creative, deployment, and review phases of a digital marketing campaign, you love what you do and can’t imagine enjoying or excelling at any other career path as much. Choosing a career is a difficult decision process, so don’t ignore or mistake the signs that you were born to be a digital marketer.

Even if you weren’t born with a soother in one hand and a copy of Gary Vaynerchuk’s latest marketing book in the other, you can still embrace and hone these qualities to become an exceptional digital marketing professional.

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Ayecon For Ios 7: A Must Have Theme, Even If You Don’t Like Skeuomorphism

If you’ve been jailbreaking for any decent amount of time, you’ve surely heard of the legendary Ayecon theme. This is a theme that set the standard for detail when Retina displays were still a relatively new feat in the mobile device industry.

A lot has changed since Ayecon first came riding over that hill on its white horse, reinvigorating the stagnant theming space in the process (I know some people will debate Ayecon’s design, but they simply can’t deny its influence.)

Flat is in, skeuomorphism is hated (and dead). So where does Ayecon fit in with this wave of change? Will Surenix, its designer, abandon the style that made Ayecon a success in the name of staying with the trend? Or, will he instead, stick to what made Ayecon work? Check inside for the full scoop along with the detailed video walkthrough.

The answer to that question is obvious if you’ve seen the screenshots accompanying this post. Of course, Ayecon won’t abandon its skeuomorphic style in favor of iOS’ new direction; it bucks the trend and does so proudly.

If you hate iOS 7’s icons then you’ll feel right at Home with Ayecon for iOS 7. It brings back the familiarity that was a staple of iOS for six successful generations. Ayecon is still one of the, if not the most detailed themes to ever hit Cydia. Super high resolution displays are the norm these days, so the effects are not as eye-popping as they once were, but it still looks awesome.

The insane level of detail is still present in iOS 7

The insane level of detail is still present in iOS 7

You’ll notice the meticulous detail on every single one of the stock iOS app icons. Even popular third party applications, jailbreak or otherwise, get the Ayecon treatment. You’ll notice apps like Instagram, iFile, Dropbox, and many others, all benefit from Ayecon’s insane level of detail.

Ayecon features:

Auto-app mask for all App Store and Cydia apps

100+ re-designed icons

Gorgeous new (classic) dock

9 beautiful high-resolution wallpapers

Status bar icons

SMS bubble enhancement

PSD file (to create your own icons using ayecon’s style)

For apps that haven’t yet been themed, they’ll still fit in well with the overall style. This is because Ayecon applies an auto app mask to every single app icon on your screen. The mask lends a three dimensional effect to the app icons and gets rid of the flat style that permeates the whole of iOS 7.

Skeuomorphic haters apply here

It may seem a little sacrilegious to promote using Ayecon without using the app icons that the theme is known for, but skeuomorphism is a very polarizing subject. I mentioned this to Surenix, and even he agreed; there’s still a lot to benefit from even if you don’t want to use Ayecon’s app icons.

The dock

The best argument for using Ayecon, outside of the tweak’s namesake feature, is for its dock. The Ayecon dock is more akin to the dock present in pre-iOS 7 devices. It brings the sense of having more breathing room to play with. It’s much like the effect that happens when you tear down a wall in the house t open up the living space; it makes the iPhone 5s and other 4″ screen devices seem taller.

Stock iOS 7 dock on the left, Ayecon’s dock on the right

The status bar

Stock iOS 7 dock on the left, Ayecon’s dock on the right

If you hate the new dots that represent the signal strength in iOS 7, then you’ll appreciate the status bar that comes with Ayecon. Surenix’s theme brings the cleaner looking status bar present on pre-iOS 7 devices. This feature, just like the dock, can be independently enabled or disabled via WinterBoard.

Stock iOS 7 status bar on the left, Ayceon on the right


Nine new wallpapers courtesy of Ayecon

Messages bubbles

Nine new wallpapers courtesy of Ayecon

The is one of the more subtle features of Ayecon’s theme, and it too can be enabled independently in WinterBoard’s settings. The message bubbles with Ayecon have a more squared off look, but it’s hard to tell the difference without comparing each side-by-side.

Stock iOS 7 on the left, Ayecon on the right


Stock iOS 7 on the left, Ayecon on the right

WinterBoard, being just recently updated to work with iOS 7 and the arm64 devices like the iPhone 5s, still has its fair share of bugs and fixes in store. Saurik acknowledges as much on WinterBoards’s Cydia page. With that said, the only issue that I encountered while testing out Ayecon was a simple caching problem. If you notice that some of the features don’t show up when you have Ayecon installed and enabled, try the following:

Step 1: Open iFile, and go to /private/var/mobile/Library/Caches/

Step 2: Delete all of the files located in this directory

Step 3: Respring your device

Doing this should clear up any caching issues that occur when enabled or disabling certain features of the theme.


I always make it a rule to be up front with you guys, so I’m going to be honest about how I plan on using Ayecon. As you know, I’m not a big fan of skeuomorphism. I used to like it, but ever since iOS 7, I prefer the cleaner look. This doesn’t mean that I won’t use Ayecon on my daily driver, I’ll just use it without enabling the SpringBoard option in WinterBoard. This means that I can enjoy the other benefits of Ayecon, namely the dock, without having to give up the flat icon style prevalent throughout the whole of iOS 7.

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