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Exploring Neural networks, the black box

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            In this blog, I'm going to share some of the insights I gained from the deeplearning.ai course from Coursera by Prof. Andrew Ng. This specialization consists of five separate courses: Neural networks, how to hyper tune the model, structuring machine learning projects, convolutional neural network, and sequence modeling. I am not going to blow by blow of the course. I aim to describe the neural network as a means of fundamental concepts and how they help to build a better model ( as far I know). In the end, I want to add a hack to explore the neural network architecture of any pre-built model. Ok, enough with the intro, let's get started,  Why deep learning is a hype in the '21s? In recent times "deep learning" creates a buzz in the AI industry. We have so many points to add to prove neural networks are performing well than other traditional methods. It's getting more hype because it faces every industrial problem without explicitly s...
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Why Statistics is piece of a data science pie?

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 As we all know, statistics is one of the industry knowledge one needs to be a data scientist. In this blog, I'm going to pen down some of the things about the same. I want to start with my favorite line from the book "Rich dad poor dad " which is a non-fictional book about personal finance, investing, business, etc. Although that is not related to statistics, I relate that point with statistics.  Here it is  "  Numbers are not the numbers, but what the numbers are telling you. It's just like words. It's not the words, but the story the words are telling you. " For me, the above words give the crystal clear explanation of what is statistics? As we all came across the phrase machine learning and deep learning are data-driven technologies. Because we know that data is the oil that runs those robust engines. The flow of any data science project will be, From the flow, we can say that ultimately the model depends upon the data. The data we download from K...
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