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. &q

Hey Readers! A recent development caught my eye in the field of E-commerce rivalries! Have a look at the following article clipping: Journalism Credits : Business Today Group Walmart-backed Flipkart has just issued a challenge to Amazon's Alexa and Google Assistant. The home-grown e-commerce giant today announced that it has acquired Bengaluru-based artificial intelligence (AI) startup Liv.ai, which has developed a platform that converts speech-to-text in nine regional languages apart from English. With this move, the e-tailer hopes to soon offer an end-to-end conversational shopping experience for its users. "Given the complexities in typing on vernacular keyboards, voice will become a preferred interface for new shoppers. One does understand that building a voice interface is complex, and is especially challenging in Indian context given multiple languages and accents," Flipkart CEO Kalyan Krishnamurthy said in a statement. "Ultimately, we want to give ou

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  conflicting , we can assume that our c

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 ran

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: "It's very easy for me to tel

Hey there! My dear readers.  Today this kernel review is going to be about the famous MNIST data-set, one of the most famous introductory datasets which we encounter along side Iris dataset and the titanic survival challenge data sets.  Since it is a competition kernel, I have decided not to make it public. (yet) Also,  if you want to try your hands at the challenge itself, then you can find the challenge page here :  Note: This kernel has been largely focused on network modelling rather than Exploratory Data Analysis because it's simple, classic stuff. Still, I will try my best to explain that stuff here. Exploratory Data Analysis First indication of a great dataset is the face that it gives all the mentioned labels equal rows in it and this one doesn't disappoint. This data set maintains a fair 4000+ entries per label which actually is a great statistic for a good dataset. And the next thing one needs to know is how does that data

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