I am sure everyone can attest to this saying. No matter what your task is, practice makes you better at it. In my Machine Learning journey, I have observed nothing different. In fact, I would go so far as to say that understanding a model itself, say, Logistic regression is less challenging than understanding where it should be applied as its application differs from dataset to dataset. Therefore, it is highly important that we practice the end-to-end Machine Learning process on different kinds of data and datasets. The more diverse datasets we use to build our models, the more we understand the model. This is also a great way to keep challenging yourself and explore some interesting data being collected around the world!

George Orwell's Animal Farm: A Fairy Story is a well-loved parable set on a farm in England, where rebellious animals stand in as critique for the corruption and downfall of the Communist Revolution in Russia. It is also a story that has often been made to serve different meanings for different groups of people. In 1946, Orwell received a letter (documented in the book George Orwell: A Life in Letters) from a colleague, Dwight Macdonald, who reported that anti-Stalinists in his circle "claimed that the parable of Animal Farm meant that revolution always ended badly for the underdog, 'hence to hell with it and hail the status quo.'" In his response, Orwell made sure to clarify his thoughts, writing: "If people think I am defending the status quo, that is, I think, because they have grown pessimistic and assume that there is no alternative except dictatorship or laissez-faire capitalism." He emphasized that if there was one lesson behind his parable, it was "you can't have a revolution unless you make it for yourself; there is no such thing as a benevolent dictatorship."

Convolutional Neural Networks This course is part of the Deep Learning Specialization Foundations of Convolutional Neural Networks. Learn to implement the foundational layers of CNNs (pooling, convolutions) and to stack them properly in a deep ... About this Course 1,001,748 recent views This course will teach you how to build convolutional neural networks and apply it to image data. Thanks to deep learning, computer vision is working far better than just two years ago, and this is enabling numerous exciting applications ranging from safe autonomous driving, to accurate face recognition, to automatic reading of radiology images. This is the fourth course of the Deep Learning Specialization.

When we pass any function to hidden layer, known as activation function.The task of activation function is to filter, normalize and non linearize the dataset and it also fires the input to perceptron of the next layer. In neural networks we update weights and biases in reference with error at output using Back propagation. Back Propagation would be possible only because of activation function. When we will be approaching deep learning problems most of the times we would deal with complex datasets. Non linear function gives clear understanding about complex datasets and can be differentiated multiple times, will give weights and baises for different- different layers and hence, will provide better learning opportunity for neural networks.

Officially created in collaboration with the Google Flutter team. What you'll learn Build beautiful, fast, and native-quality apps with Flutter … What you'll be learning This tutorial uses an older version of Symfony… but since it's a JavaScript tutorial, the concepts … Master Linear Algebra What you'll learn Fundamentals … Online guide on how to learn GAMS like a Pro.

Research shows that even undergraduate students lack these basic skills. Some are not accurate at judging the reliability of information sources or managing the quantity of material available. This is a particularly critical skill in the time of AI where fake news is so easy to produce and spread. Some do not have the skills to collaborate or work in teams, especially not through digital technologies. How can we then expect them to work with different types of AI?

I love quantum mechanics, something is fascinating about the perception of how QM explains the world. How different it is than the reality we can see and live in. Everything we call real is made of things that cannot be regarded as real. This quotation was a revelation to me when I was a student. Looking at the matter isn't the good way because all of the elements are just waves.

Python or R – which tool should you learn? If I got a penny each time I came across this question.. There is a widely held belief that mastering data science is about learning how to apply techniques in Python or R. Or any other tool. That tool has become the central point around which all other data science functions revolve. The assumption (or myth) is that being able to write code using existing libraries (numpy, scikit-learn, caret, etc.) should be enough to label yourself an expert.

In this paper, we report experimental results on assessing the impact of COVID-19 on college students by processing free-form texts generated by them. By free-form texts, we mean textual entries posted by college students (enrolled in a four year US college) via an app specifically designed to assess and improve their mental health. Using a dataset comprising of more than 9000 textual entries from 1451 students collected over four months (split between pre and post COVID-19), and established NLP techniques, a) we assess how topics of most interest to student change between pre and post COVID-19, and b) we assess the sentiments that students exhibit in each topic between pre and post COVID-19. Our analysis reveals that topics like Education became noticeably less important to students post COVID-19, while Health became much more trending. We also found that across all topics, negative sentiment among students post COVID-19 was much higher compared to pre-COVID-19. We expect our study to have an impact on policy-makers in higher education across several spectra, including college administrators, teachers, parents, and mental health counselors.

Leveraging social media data to understand people's lifestyle choices is an exciting domain to explore but requires a multiview formulation of the data. In this paper, we propose a joint embedding model based on the fusion of neural networks with attention mechanism by incorporating social and textual information of users to understand their activities and motivations. We use well-being related tweets from Twitter, focusing on 'Yoga'. We demonstrate our model on two downstream tasks: (i) finding user type such as either practitioner or promotional (promoting yoga studio/gym), other; (ii) finding user motivation i.e. health benefit, spirituality, love to tweet/retweet about yoga but do not practice yoga.