Online Courses Udemy - Python Data Science with Pandas: Master 12 Advanced Projects, Work with Pandas, SQL Databases, JSON, Web APIs & more to master your real-world Machine Learning & Finance Projects Bestseller Created by Alexander Hagmann English [Auto] Students also bought Machine Learning and AI: Support Vector Machines in Python Unsupervised Machine Learning Hidden Markov Models in Python Natural Language Processing with Deep Learning in Python Advanced AI: Deep Reinforcement Learning in Python Deep Learning: Advanced Computer Vision (GANs, SSD, More!) Cutting-Edge AI: Deep Reinforcement Learning in Python Preview this course GET COUPON CODE Description Welcome to the first advanced and project-based Pandas Data Science Course! This Course starts where many other courses end: You can write some Pandas code but you are still struggling with real-world Projects because Real-World Data is typically not provided in a single or a few text/excel files - more advanced Data Importing Techniques are required Real-World Data is large, unstructured, nested and unclean - more advanced Data Manipulation and Data Analysis/Visualization Techniques are required many easy-to-use Pandas methods work best with relatively small and clean Datasets - real-world Datasets require more General Code (incorporating other Libraries/Modules) No matter if you need excellent Pandas skills for Data Analysis, Machine Learning or Finance purposes, this is the right Course for you to get your skills to Expert Level! This Course covers the full Data Workflow A-Z: Import (complex and nested) Data from JSON files. Efficiently import and merge Data from many text/CSV files. Clean, handle and flatten nested and stringified Data in DataFrames.

Natural language processing (NLP), or NLP for short, is a branch of artificial intelligence that helps computers understand, interpret, and manipulate human language. It is broadly defined as the automatic manipulation of natural language, like speech and text, by software or technology. Natural language processing is a form of AI that is easy to understand and start using. It can also do a lot to help you in making better business decisions. In order to make your website worth your user's time, NLP can do help you a lot.

If you are a student or a professional looking for various open-source computer vision projects, then, this article is here to help you. The computer vision projects listed below are categorized in an experience-wise manner. All of these projects can be implemented using Python. Face and Eyes Detection is a project that takes in a video image frame as an input and outputs the location of the eyes and face (in x-y coordinates) in that image frame. The script is fairly easy to understand and uses Haar Cascades for detecting the face and the eyes if found in the image frame.

In recent times there has been a lot of interest in embedding deep learning models into hardware. Energy is of paramount importance when it comes to deep learning model deployment especially at the edge. There is a great blog post on why energy matters for [email protected] by Pete Warden on "Why the future of Machine Learning is Tiny". Energy optimizations for programs (or models) can only be done with a good understanding of the underlying computations. Over the last few years of working with deep learning folks -- hardware architects, micro-kernel coders, model developers, platform programmers, and interviewees (especially interviewees) I have discovered that people understand LSTMs from a qualitative perspective but not well from a quantitative position.

Graph Neural Networks (GNNs) have exploded onto the machine learning scene in recent years owing to their capability to model and learn from graph-structured data. Such an ability has strong implications in a wide variety of fields whose data is inherently relational, for which conventional neural networks do not perform well. Indeed, as recent reviews can attest, research in the area of GNNs has grown rapidly and has lead to the development of a variety of GNN algorithm variants as well as to the exploration of groundbreaking applications in chemistry, neurology, electronics, or communication networks, among others. At the current stage of research, however, the efficient processing of GNNs is still an open challenge for several reasons. Besides of their novelty, GNNs are hard to compute due to their dependence on the input graph, their combination of dense and very sparse operations, or the need to scale to huge graphs in some applications. In this context, this paper aims to make two main contributions. On the one hand, a review of the field of GNNs is presented from the perspective of computing. This includes a brief tutorial on the GNN fundamentals, an overview of the evolution of the field in the last decade, and a summary of operations carried out in the multiple phases of different GNN algorithm variants. On the other hand, an in-depth analysis of current software and hardware acceleration schemes is provided, from which a hardware-software, graph-aware, and communication-centric vision for GNN accelerators is distilled.

This paper exploits extended Bayesian networks for uncertainty reasoning on Petri nets, where firing of transitions is probabilistic. In particular, Bayesian networks are used as symbolic representations of probability distributions, modelling the observer's knowledge about the tokens in the net. The observer can study the net by monitoring successful and failed steps. An update mechanism for Bayesian nets is enabled by relaxing some of their restrictions, leading to modular Bayesian nets that can conveniently be represented and modified. As for every symbolic representation, the question is how to derive information - in this case marginal probability distributions - from a modular Bayesian net. We show how to do this by generalizing the known method of variable elimination. The approach is illustrated by examples about the spreading of diseases (SIR model) and information diffusion in social networks. We have implemented our approach and provide runtime results.

Free Coupon Discount - { C Language } Deep Learning From Ground Up, Build Artificial Intelligence Applications in C Created by Israel Gbati Preview this Udemy Course - GET COUPON CODE Welcome to the { C Language } Deep Learning From Ground Up course. We are going to embark on a very exciting journey together. We are going to learn how to build deep neural networks from scratch in c language. We shall begin by learning the basics of deep learning with practical code showing each of the basic building blocks that end up making a giant deep neural network all the way to building fully functions deep learning models using c language only. By the end of this course you will be able to build neural networks from scratch without libraries, you will be able to understand the fundamentals of deep learning from a c language perspective and you will also be able to build your own deep learning library in c.

All Arm trademarks featured in this course are registered or unregistered trademarks of Arm Limited (or its subsidiaries) in the US or elsewhere. Welcome to the Deep Learning From Ground Up on ARM Processors course. We are going to embark on a very exciting journey together. We are going to learn how to build deep neural networks from scratch on our microcontrollers. We shall begin by learning the basics of deep learning with practical code showing each of the basic building blocks that end up making a giant deep neural network.

Machine learning has finally come of age. With H2O software, you can perform machine learning and data analysis using a simple open source framework that's easy to use, has a wide range of OS and language support, and scales for big data. This hands-on guide teaches you how to use H20 with only minimal math and theory behind the learning algorithms. Hot & New What you'll learn This course covers the main aspects of the H2O package for data science in Python. If you take this course, you can do away with taking other courses or buying books on Python-based data science as you will have the keys to a very powerful Python supported data science framework.

For the last year and a half I have been using Watson Studio and DSX Local as my development environments for exploring machine learning and implementing models. Inspired by Siraj Raval's video on Azure Machine Learning I decided to take the plunge and check out Microsoft's ML environment. This posting covers my first impressions, the good and the bad, and contrasts Azure ML with Watson Studio / DSX. Getting a first taste of Azure ML from a standing start is relatively easy -- here's a quick overview of the preparatory steps: As Siraj notes in his video, Microsoft touts a hybrid (on prem cloud) approach. Before getting into the experience of using Azure ML, I'd like to contrast my experience of "hybrid" Microsoft vs. IBM: Full disclosure: I am an IBM employee, but I can see pros and cons to both approaches.