The Rise of Artificial Intelligence

Hey Readers! Welcome to yet another post where I play with a self designed neural network. This CNN would be tackling a variant of classical MNIST known as Fashion MNIST dataset  . Before we start exploring what is the approach for this dataset, let's first checkout what this dataset really is. Fashion-MNIST is a dataset of Zalando's article images—consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes. Zalando intends Fashion-MNIST to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms. It shares the same image size and structure of training and testing splits. The original MNIST dataset contains a lot of handwritten digits. Members of the AI/ML/Data Science community love this dataset and use it as a benchmark to validate their algorithms. In fact, MNIST is often the first dataset researchers try. ...

Hello Readers! Welcome to yet another value prediction work! Today, we will be looking at the in-demand dataset , namely Genetic Variant Classifications . We will look at this dataset and go for it's primary objective, that is classification of  the two lab reports and determining whether they both conflict or not. The Kernel you may want to look at for more information : Conflicting result classifications As usual, we will be looking at the dataset with the aim of EDA , Feature Engineering and Predictions Exploratory Data Analysis One would like to see what are the Chromosomes vs Class distribution of this data. For that, you can simply use :  As you can observe in the graph given below, the dataset happens to be heavily biased towards the  non- conflicting  genes and that too with the  CHROM == 2  standing out as the clear bias winner. Since the incidents where the genes are recorded to be  conflict...

Hey readers! Today, allow me to present you yet another dataset analysis of a rather gluttony topic, namely Avocado price analysis. This Data set  represents the historical data on avocado prices and sales volume in multiple US markets. Our prime objectives will be to visualize the dataset, pre-process it and ultimately test multiple sklearn classifiers to checkout which one gives us the best confidence and accuracy for our Avocado's Organic assurance! Note : I'd like to extend the kernel contribution to Shivam Negi . All this code belongs to him. Data Visualization This script must procure the following jointplot  While a similar joint plot can be drawn for conluding the linearly exponent relations between extra large bags and the small ones. Pre Processing The following script has been used for pre processing the input data. Model Definition and Comparisons We will be looking mostly at three different models, namely ra...

Hey guys! I recently came across one excellent poetry algorithm named Deep-Speare.  It was developed by   A four-person team from the School of Information and the Graduate School of Education designed a computer algorithm   which was able to successfully fool people  trying to distinguish between human- and bot-written verses nearly 50 per cent of the time.  However, experts could still easily identify machine-generated poetry, and AI may have a long way to go before it can outdo Shakespeare, researchers said.  Computer scientists at University of Melbourne in Australia and University of Toronto in Canada designed an algorithm that writes poetry following the rules of rhyme and metre.  In some ways, the computer's verses were better than Shakespeare's. The rhymes and metre in the machine-generated poetry were more precise than in human-written poems. Test Run Feedback  The following excerpt is courtesy Economic Times: ...

Hey Readers! Good news for all the "cheap fair power" computer fans, as a result of a major collaboration effort between TensorFlow and Raspberry Pi foundation, one can now install tensorflow precompiled binaries using Python's pip package system !  When TensorFlow was first launched in 2015, they wanted it to be an “ open source machine learning framework for everyone ”. To do that, they needed to run on as many of the platforms that people are using as possible. They have long supported Linux, MacOS, Windows, iOS, and Android, but despite the heroic efforts of many contributors, running TensorFlow on a Raspberry Pi has involved a lot of work. If one is using Rasbian9 they can simply use these 2 commands to install tensorflow on their machine! According to an excerpt from TensorFlow's medium article page :  " We’re excited about this because the Raspberry Pi is used by many innovative developers, and is also widely used in education to ...

Data Science and Artificial Intelligence fans, this might be a good day for you. Google DeepMind Cofounder gives a teenage AI fan  pieces of advice, and I think you should know that too! Some artificial intelligence specialists at organizations like Google and Facebook are currently acquiring more cash than venture financiers at Goldman Sachs and J.P. Morgan.  These specialists additionally have the benefit of working in a field of technology that is ready to majorly affect the world we live in.  Be that as it may, for some individuals, it's not clear how to approach landing a job in AI. This week, 17-year-old Londoner Aron Chase asked Shane Legg — the chief scientist and cofounder of DeepMind, an AI lab acquired by DeepMind for a reported £400 million — for five pieces of advice for an AI enthusiast like himself. " Hey Shane I’m currently 17 from London England and am very passionate about AI, also learning about in-depth human needs. What would be the 5 piec...

Hey Readers,  As Python has gained a lot of traction in the recent years in Data Science industry. I wanted to outline some of its most useful libraries for data scientists and engineers, based on recent experience. NumPy When beginning to manage the scientific undertaking in Python, one unavoidably desires help to Python's SciPy Stack, which is an accumulation of programming particularly intended for scientific processing in Python (don't mistake for SciPy library, which is a piece of this stack, and the network around this stack). Along these lines we need to begin with a glance at it. Be that as it may, the stack is quite huge, there is in excess of twelve of libraries in it, and we need to put a point of convergence on the center bundles (especially the most fundamental ones).  The most major bundle, around which the scientific computation stack is constructed, is NumPy (remains for Numerical Python). It gives a plenitude of valuable highlights for tas...

Hey Readers, today I bring forth an exciting news for you all aspiring data scientists and machine learners! Something new happened in Microsoft Research Blog :  The Microsoft Research Outreach team has worked extensively with the external research community to enable adoption of cloud-based research infrastructure over the past few years. Through this process, we experienced the ubiquity of Jim Gray’s fourth paradigm of discovery based on data-intensive science – that is, almost all research projects have a data component to them. This data deluge also demonstrated a clear need for curated and meaningful datasets in the research community, not only in computer science but also in interdisciplinary and domain sciences. Today we are excited to launch  Microsoft Research Open Data  – a new data repository in the cloud dedicated to facilitating collaboration across the global research community. Microsoft Research Open Data, in a single, convenient, cloud-hosted ...

Hi readers ,  There are heaps of good reasons why researchers are so focused on model designs, however it means that there are not very many assets accessible to control individuals who are centered around deploying machine learning underway. To address that, An ongoing talk at the gathering was on "the preposterous adequacy of preparing information", and I need to develop that a bit in this blog entry, clarifying why information is so imperative alongside some commonsense tips on enhancing it. As a feature of my investigation I work intimately with a great deal of researchers and item groups, and my faith in the intensity of information changes originates from the gigantic additions I've seen them accomplish when they focus on that side of their model building. The greatest boundary to utilizing deep learning in many applications is getting sufficiently high accuracy in reality, and enhancing the preparation set is the quickest route I've seen to accuracy upgr...