Deep learning is a subset of Artificial Intelligence, which is an area that relies on learning and improving on its own by examining computer algorithms. While machine learning uses simpler concepts, these models work with artificial neural networks, designed to mimic the way humans think and learn.
Deep learning – complex neural networks – is designed to mimic the workings of the human brain so that computers can be trained to deal with ill-defined abstractions and problems.
An average five-year-old kid can easily find the difference between the face of his teacher and that of the crossing guard. In contrast, the computer has to do a lot of work to understand Who’s Who. Neural networks and deep learning are often used similarly in the tasks of object recognition, speech recognition, and various other computer vision tasks.
To understand the process followed by a deep learning model, we first need to know what is a neural network, because a deep learning model is all about neural networks. So what are Neural Networks? A neural network is also a kind of machine learning model which is inspired by the neurons in the human brain. It mainly consists of three or more layers. An input layer, one hidden layer or even more hidden layers, and an output layer.
While training these deep neural networks model, the Data is injected through the input layer. Then the data is changed in the hidden layer and the output layers based on the weights applied to those nodes. Generally, a Neural network is surrounded by thousands of millions of nodes which are densely interconnected with each other.
#by aman kharwal #deep learning
For this week’s data science career interview, we got in touch with Dr Suman Sanyal, Associate Professor of Computer Science and Engineering at NIIT University. In this interview, Dr Sanyal shares his insights on how universities can contribute to this highly promising sector and what aspirants can do to build a successful data science career.
With industry-linkage, technology and research-driven seamless education, NIIT University has been recognised for addressing the growing demand for data science experts worldwide with its industry-ready courses. The university has recently introduced B.Tech in Data Science course, which aims to deploy data sets models to solve real-world problems. The programme provides industry-academic synergy for the students to establish careers in data science, artificial intelligence and machine learning.
“Students with skills that are aligned to new-age technology will be of huge value. The industry today wants young, ambitious students who have the know-how on how to get things done,” Sanyal said.
#careers # #data science aspirant #data science career #data science career intervie #data science education #data science education marke #data science jobs #niit university data science
There are a lot of great online resources and websites on data science and machine learning that one can leverage in order to learn something new or maybe work on an existing skill now. The Age of Internet as they say, has made it extremely easy to access information on the go.
One of the hardest things to do in technology is disrupt yourself
- Matt Mullenweg
I have been approached on LinkedIn in through messages and connection requests regarding the same question countless times. And it is always something like this,
“ What exactly should I do to learn Data Science and Machine Learning….which courses to do…… and basically how to start? “.
The purpose of this article is to answer this question and along with it to give the readers a list of the most popular courses in this field currently.
Below you will find online courses which will help you accelerate your growth in the field of data science. But know that watching videos is just going to give you a seat on the ML council, it is not going to grant you the rank of an ML master if you know what I mean, for that you will have to work on practical real-world problems and get your hands dirty with data.
Photo by Author from SWU
What I can say though is that these courses will take you to that level from where you will be capable enough to figure out what you should do next. It is just a matter of starting out now!
And that is when you will find this table helpful. These course teach you the basics all the way to the advanced topics, and it is advisable that you take your time with each and everything and understand at least the basic concepts properly before you jump in on the code — something I am sure you would want to do from the word go!
#machine-learning #artificial-intelligence #deep-learning #data-science #data #data science
With recruiters listing a myriad of “preferred skills” in their job postings, learning Data Science can get quite overwhelming at times. Dividing the journey up into five chapters can provide a clearer picture of what lies ahead.
#machine-learning #learn-data-science #data-science-training #python-for-data-science #data-science-courses
When you’re working on Deep Learning algorithms you almost always require a large volume of data to train your model on. It’s inevitable since Deep Learning algorithms like RNN’s or GRU’s are data-hungry, and in order to converge they need their fair share of data. But what if there’s not enough data? This is not a rare situation to happen, in fact, when working in research you probably have to deal with it on a daily basis, or even if you are working on a new area/product where there’s not, yet, a lot of data available. How can you deal with this? Am I able to apply Machine Learning? Can I leverage the latest advances in Deep Learning?
There are two possible paths that we can choose from, and I’ll be covering them in the next sections — the Data Path and the Model Path.
It may sound a bit obvious, but sometimes we miss the power of this kind of solution to solve small data problems — I’m talking about Data Augmentation. The idea behind Data Augmentation is — Points nearby a certain point (in hyperspace) represent a similar behavior. For eg: An image of a dog with increased contrast or brightness is still an image of a dog.
To utilize Data augmentation for a tabular dataset there are numerous methods available like:
Let’s talk about SMOTE in more detail. To simplify we can define the concept behind SMOTE as “Birds of the same feather flock together”, which reduced in data terms means that data-points that are close to each other in the hyperspace represent similar behavior so they can be approximated as new data-points for the dataset.
This technique works best when a few samples are synthesized from a bigger set. But, once you go past a certain threshold of approximating the synthetic samples, the likelihood of the samples starts to diverge. So you’ve to keep this in mind while implementing this.
But, once you go past a certain threshold of approximating the synthetic samples, the likelihood of the samples starts to diverge.
Let’s also talk about **Data Augmentation **in images. Looking at the image below, they all represent a parrot but they are all different since the demographics of the image like contrast, brightness, etc., changed, going from a single image to 12 images increasing your data volume 12x.
Off-course that we can also apply rotations, mirroring, and crop in order to augment the available images datasets. For this purpose, there are several libraries available such as OpenCV, PyTorch, and TensorFlow. Another one that is also quite interesting is albumentation, which you can see in action over this Colab notebook.
Generative models have proven a super-useful understanding of the data distribution, so much that today these models are the prime tool for Data Augmentation tasks.
#computer-science #machine-learning #deep-learning #data-science #data #deep learning
If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.
If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.
In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition