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How To Become A Successful Programmer?

How To Become A Successful Programmer? I have heard many novice programmers saying I want to get better at programming but there is hardly a slight improvement in their skills. I have observed that most of them say they want to get better but that is just a wish. They do not really mean it. They mere wish to improve their skills. They do not work for it. Your wish does not guarantee that you will become a successful programmer. Many other people who have developed an interest in computer programming do not know how to reach to a point where they will be called successful programmers. They either keep wandering in the middle of nowhere or just give up. The same response is for them too as it was for the wishers. Your interest does not guarantee that you will succeed. Programming is a field which requires intensive work to master. Along with improving your technical knowledge of programming, you need to work on your interest. You need to develop a habit of not giving up. You need to…
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AI and ML: Are they one and the same?

As children we believed in magic, imagined, and a fantasy where robots would one day follow our commands, undertaking our most meager tasks and even help with our homework at the push of a button! But sadly it always seemed that these beliefs, along with the idea of self-driven aero cars and jetpacks, belonged in a future beyond our imagination or in a Hollywood Sci-fi. Would we ever get to experience the future in our lifetime? But then it arrived! Artificial Intelligence, aka AI, made its debut in real life and became the buzz word of the 21st century, providing us with new ideas to explore and incredible possibilities. And just as we were getting used to AI we were introduced to Futuristic Learning, Deep Learning, and another term we often confuse with AI: Machine Learning (ML). Whew! Suddenly the future is well and truly here, and it’s hard to keep up with the advancement of these technologies, what each term means and how they relate to one another – particularly when it comes to A…

5 Key Challenges In Today’s Era of Big Data

Digital transformation will create trillions of dollars of value. While estimates vary, the World Economic Forum in 2016 estimated an increase in $100 trillion in global business and social value by 2030. Due to AI, PwC has estimated an increase of $15.7 trillion and McKinsey has estimated an increase of $13 trillion in annual global GDP by 2030. We are currently in the middle of an AI renaissance, driven by big data and breakthroughs in machine learning and deep learning. These breakthroughs offer opportunities and challenges to companies depending on the speed at which they adapt to these changes. Modern enterprises face 5 key challenges in today’s era of big data1. Handling a multiplicity of enterprise source systems The average Fortune 500 enterprise has a few hundred enterprise IT systems, all with their different data formats, mismatched references across data sources, and duplication 2. Incorporating and contextualising high frequency data The challenge gets significantly harder wi…

Why Do We Forget Most Of The Things We Read?

How To Code: How To Improve Logical Thinking For Programming

There are multiple techniques shared on the internet on how to improve logical thinking or # logic building tips for programmers or how to think like a programmer. But those techniques does not work. I mean they work but... the hard way. It is like someone is asking you to taste a delicious food without opening your mouth. Can you do that? Probably you can come up with a super creative way but that's not the way it works. There is one most important process to follow before applying those techniques. You can say, it is a prerequisite to fully utilize those logic building technique. And it is, understanding the flow of data inside your code. The code that you haven't written yet. Wait... What?
I am not kidding. You can understand your code that does not exists yet by using your imagination power. The power that makes us human, human. If you want something done and you cannot imagine it, you will never get it done. Here, I will tell you how programmers in the beginning develop …

Deep Learning: When To Stop Training Nueral Network?

Quick Recap An artificial neural network is a combination of artificial neurons which does some math and try to estimate a mathematical function. This estimation process is called training or fitting. Basic training mechanism The math involved in ANN is mostly MAC (Multiply-Accumulate) operations where the input is multiplied by weights and biases are added to the product. One of the activation functions is applied to the output and it is forwarded to the next layer and the same process continues until it reaches to the end layer. This process is called feed-forward.
After the end layer calculation, the output computed by the network is compared with the actual output. The difference between actual output and estimated output is calculated using a function called loss function. Common loss functions these days are Mean Squared Error, Mean Absolute Error, Root MSE, Cross-Entropy etc. The error calculated using loss function is propagated backward throughout the neural network in the fo…