Original Source Here Binomial Distribution! It is the collection of Bernoulli trials for the same event, i.e., it contains more than 1 Bernoulli event for the same scenario for which the Bernoulli trial is calculated. Way to represent Binomial Distribution! It can be represented using two things: The number of Bernoulli trials. Probability of an event in each trial. Binomial Distribution representation! [Image by Author] In the image above, “n” corresponds to the number of Bernoulli trials, & “p” corresponds to the probability of the event in each trial. Probability Function to calculate Binomial Distribution! When there is a requirement to calculate the likelihood of the occurrence of some event a specific number of times out of a fixed number of times, the formula listed below is used. Probability Function for Binomial Distribution! [Image by Author] In the above equation: “n” is the total number of trials of an event. “s” corresponds to the number of ti...

Original Source Here A Deepfake Queen’s Speech is just the start How Deepfakes and AI could start to become more common in the media we consume. Channel 4’s Deepfake Queen’s Speech might be the start of a new way of making media (image courtesy of Channel 4 ). As a brit, on Christmas Day I was quite happy to hear the call out for diversity and the nod to all those who have struggled through this year — especially those on the front line in key worker roles and in the NHS. As a data scientist, I was very intereste d in seeing Channel 4’s alternative Queen’s Speech. As I’m sure many of you have, I’ve followed the GAN/Deepfake progress for a while now. This, however, was the first time we’d seen it on such a scale, on the TV. It was well done, the imagery was of good enough quality at the start to highlight the power of these tools but an obvious call to trust and fake information sources — something especially important in the wake of COVID-19 vaccinations. What’s next...

https://cdn-images-1.medium.com/max/2600/0*7Dr334zRBzim6JOL Original Source Here Understand TensorFlow Basic with Python Basic terminologies used in TensorFlow Photo by Markus Spiske on Unsplash Why everyone wants to learn TensorFlow in deep learning, as we deep dive into the machine learning projects, we use the “sklearn” library, and when we talk about deep neural networks, TensorFlow comes into the picture. Anaconda distribution is perfect for data science and machine learning with the pre-installed library packages, but we have to install TensorFlow explicitly because it does not come in anaconda distribution. To download the TensorFlow write the following command in the anaconda prompt and press enter. #command to install TensorFlow pip install tensorflow Anaconda prompt view. A photo by Author TensorFlow is installing packages in the working environment. A photo Author TensorFlow is a library for deep computational processes in many applications and is widely ...

https://cdn-images-1.medium.com/max/2600/0*4ePMx0lLwFLwP791 Original Source Here NumPy: Zero to Hero with Python A handbook of NumPy basic operations with python Photo by James Harrison on Unsplash NumPy is a python library package used for multidimensional array computing. NumPy process is faster than list because of its homogeneous type that is packed densely in memory and performed vectorization. Vectorization means the computation done with parallel hardware process to make it fast. NumPy is the main purpose so, it is used in various fields like statistics, algebra, matrix operations, etc. The types of arrays we work with are shown below: Types of arrays. A photo by Author Introduction and Basic of array A class named array is used in NumPy for more functionality with its attributes. The different attributes in this functionality are dimensions, shape, size, type, etc. Let’s take the example to know about these attributes. #First need to import the library to u...

https://cdn-images-1.medium.com/max/2600/0*ea_xEs1nTxJaZ36w Original Source Here Machine Learning is not just classifying whether an image contains a dog or not. Neither is it just for predicting house predictions of Boston. When one gets into how many applications there really are of this vast industry, one usually gets stunned! Back to t he point, it’s not compulsory that a model can predict just one probability when given a scenario, for example, what if you want to check if an image contains a dog and a cat? You see? This is where we have multi-class classification. Multi-class Classification Multi – class classification is those tasks where examples are assigned exactly one of more than two classes . Binary Classification : Classification tasks with two classes . Multi – class Classification : Classification tasks with more than two classes .[1] Softmax Regression We have seen many examples of how to classify between two classes, i.e. Binary Classification. Now, w...

https://cdn-images-1.medium.com/max/2600/0*ea_xEs1nTxJaZ36w Original Source Here Machine Learning is not just classifying whether an image contains a dog or not. Neither is it just for predicting house predictions of Boston. When one gets into how many applications there really are of this vast industry, one usually gets stunned! Back to t he point, it’s not compulsory that a model can predict just one probability when given a scenario, for example, what if you want to check if an image contains a dog and a cat? You see? This is where we have multi-class classification. Multi-class Classification Multi – class classification is those tasks where examples are assigned exactly one of more than two classes . Binary Classification : Classification tasks with two classes . Multi – class Classification : Classification tasks with more than two classes .[1] Softmax Regression We have seen many examples of how to classify between two classes, i.e. Binary Classification. Now, w...

https://cdn-images-1.medium.com/max/1280/0*aF4PnQo_XAXnS8xv Original Source Here Vehicle Perception Technology Perception is key to understanding the world. This section connects sensing and perception in automated driving systems. Based on its function, vehicle perception technology can be divided into two major categories: Localization — locating the position of the vehicle on maps Recognition — object detection, target tracking, and motion estimation AVs usually need to perform simultaneous localization and mapping (SLAM) to operate in a dynamic environment. Localization is considered part of the perception module here, while planning and control are part of the decision module. Vehicle perception technology can be categorized based on its sensing medium: Perception via radio waves (GPS, radar) Perception via sound waves (ultrasonic sensors) Perception via light (LiDAR, Camera) Most AV Companies rely on HD Maps for navigation. Given this map, an AV coul...