Note: If you prefer to read with a white background and black font, you can see this article in GitHub here. Las time I check SVG images rendered just fine. It is no exaggeration to say that NumPy is at the core of the entire scientific computing Python ecosystem, both as a standalone package for numerical computation and as the engine behind most data science packages. In this document, I review NumPy main components and functionality, with attention to the needs of Data Science and Machine Learning practitioners, and people who aspire to become a data professional. My only assumption is that you have basic familiarity with Python, things like variables, lists, tuples, and loops. Advance Python concepts like Object Oriented Programming are not touched at all. Content-wise, I'll say that 95% is based on NumPy v1.18 manual, in particular: The rest 5% comes from a couple of random articles on the Internet and Stack Overflow. I resort to those sources mostly to clarify concepts and functionality that wasn't clear for me from NumPy documentation. My own experience was the base to organize the tutorial, explain concepts, create practical examples, create images, etc. "Why are you using the documentation as the main source of content, instead of the many great tutorials online?" Because it is the most up-to-date, complete, and reliable source about NumPy (and about any library for that matter). "Why then I should read this if everything comes from the documentation?" Well, you don't need to read this, you are right. Actually, I encourage you to read the documentation and learn from there. What I can offer is my own: (1) organization of contents, (2) selection of contents, (3) explanations and framing of concepts, (4) images, (5) practical examples, (6) and general perspective. This tutorial is part of a larger project I am working on, which is an introduction to Python and its libraries for scientific computing, data science, and machine learning that you can find here. As a final note, if you are NumPy expert, advanced user, or developer, you may find some inaccuracies or lack of depth in some of my explanations. Two things: (1) feel free to suggest a better explanation or something that I may add to make things clearer, (2) I prioritize conciseness and accessibility over the accuracy, so the lack of accuracy or depth sometimes it is intentional from my part. If you have any questions or suggestion feel free to reach me out to at pcaceres@wisc.edu Here is my Twitter, LinkedIn, and personal site. Scientific and numerical computing often requires processing massive datasets with complex algorithms. If you are a scientist or data professional, you want a programming language than can process data FAST. The closer a programming language is to machine instructions (binary), the faster it runs.

While her peers reveled in an unprecedented virtual school year, the self-described "technology enthusiast," Harita Suresh, 13, was bored. She decided on an online course and settled on IBM Skills Network's "AI chatbots without programming." She lacked experience with artificial intelligence, but was eager to learn through the self-paced course. Harita is more than a little familiar with tech, "I have been interested in technology since I was 5," she said. "My first coding challenge was the Lightbot Hour of Code. I was fascinated that the code I wrote could control the actions of the characters on screen. Since then, I pursued coding on multiple platforms like code.org, The more I learned about tech, the more I wanted to know. In fifth grade, I took a Python programming course offered by Georgia Tech."

Technology is the tool that is empowering the human race with its astonishing innovations. Almost every object that we come across is based on the foundation that has a significant share in digitizing our lives. Determining which is better data science or artificial intelligence is undermining one over another. However, both the technologies have their contribution to revolutionizing not only the business operations but also the lifestyle. This article aims to cover the technologies that have become a major sector of investment in the IT industry.

Online Courses Udemy Artificial Intelligence 2018: Build the Most Powerful AI, Learn, build and implement the most powerful AI model at home. Created by Hadelin de Ponteves, Kirill Eremenko, SuperDataScience Team English [Auto-generated], Indonesian [Auto-generated], 3 more Students also bought PyTorch for Deep Learning and Computer Vision Deep Learning and NLP A-Z: How to create a ChatBot Deep Learning and Computer Vision A-Z: OpenCV, SSD & GANs Machine Learning Practical: 6 Real-World Applications The Complete Neural Networks Bootcamp: Theory, Applications Preview this course - GET COUPON CODE Description Two months ago we discovered that a very new kind of AI was invented. The kind of AI which is based on a genius idea and that you can build from scratch and without the need for any framework. We checked that out, we built it, and... the results are absolutely insane! This game-changing AI called Augmented Random Search, ARS for short.

The Complete Supervised Machine Learning Models in Python 4.6 (46 ratings) Course Ratings are calculated from individual students' ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately. In this course, you are going to learn all types of Supervised Machine Learning Models implemented in Python. The Math behind every model is very important. Without it, you can never become a Good Data Scientist. That is the reason, I have covered the Math behind every model in the intuition part of each Model.

Google and Alphabet CEO Sundar Pichai has announced a Google for India Digitisation Fund through which the company will invest Rs 75,000 crore, or approximately $10 billion, over the next five to seven years to drive digital transformation in the country. "We'll do this through a mix of equity investments, partnership investments, and operational, infrastructure and ecosystem investments. This is a reflection of our confidence in the future of India and its digital economy," Pichai said during the Google for India virtual conference. The fund will focus on four areas that are important to India's digitisation. These include enabling affordable access to the internet and to information for every Indian in their own language and building new products and services that are deeply relevant to India's unique needs including consumer tech, education, health and agriculture.

We introduce the eigentask framework for lifelong learning. An eigentask is a pairing of a skill that solves a set of related tasks, paired with a generative model that can sample from the skill's input space. The framework extends generative replay approaches, which have mainly been used to avoid catastrophic forgetting, to also address other lifelong learning goals such as forward knowledge transfer. We propose a wake-sleep cycle of alternating task learning and knowledge consolidation for learning in our framework, and instantiate it for lifelong supervised learning and lifelong RL. We achieve improved performance over the state-of-the-art in supervised continual learning, and show evidence of forward knowledge transfer in a lifelong RL application in the game Starcraft2.

Policy gradient methods have shown success in learning control policies for high-dimensional dynamical systems. Their biggest downside is the amount of exploration they require before yielding high-performing policies. In a lifelong learning setting, in which an agent is faced with multiple consecutive tasks over its lifetime, reusing information from previously seen tasks can substantially accelerate the learning of new tasks. We provide a novel method for lifelong policy gradient learning that trains lifelong function approximators directly via policy gradients, allowing the agent to benefit from accumulated knowledge throughout the entire training process. We show empirically that our algorithm learns faster and converges to better policies than single-task and lifelong learning baselines, and completely avoids catastrophic forgetting on a variety of challenging domains.

The ability to cooperate through language is a defining feature of humans. As the perceptual, motory and planning capabilities of deep artificial networks increase, researchers are studying whether they also can develop a shared language to interact. From a scientific perspective, understanding the conditions under which language evolves in communities of deep agents and its emergent features can shed light on human language evolution. From an applied perspective, endowing deep networks with the ability to solve problems interactively by communicating with each other and with us should make them more flexible and useful in everyday life.

Clustering is a foundational problem in machine learning with numerous applications. As machine learning increases in ubiquity as a backend for automated systems, concerns about fairness arise. Much of the current literature on fairness deals with discrimination against protected classes in supervised learning (group fairness). We define a different notion of fair clustering wherein the probability that two points (or a community of points) become separated is bounded by an increasing function of their pairwise distance (or community diameter). We capture the situation where data points represent people who gain some benefit from being clustered together. Unfairness arises when certain points are deterministically separated, either arbitrarily or by someone who intends to harm them as in the case of gerrymandering election districts. In response, we formally define two new types of fairness in the clustering setting, pairwise fairness and community preservation. To explore the practicality of our fairness goals, we devise an approach for extending existing $k$-center algorithms to satisfy these fairness constraints. Analysis of this approach proves that reasonable approximations can be achieved while maintaining fairness. In experiments, we compare the effectiveness of our approach to classical $k$-center algorithms/heuristics and explore the tradeoff between optimal clustering and fairness.