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Artificial Neural Networks: Understand The Basic Concepts [Size: 285 MB] ... Artificial Intelligence is becoming progressively more relevant in today’s world. The rise of Artificial intelligence has the potential to transform our future more than any other technology. By using the power of algorithms, you can develop applications which intelligently interact with the world around you, from building intelligent recommender systems to creating self-driving cars, robots and chatbots. Neural networks are a key element of artificial intelligence.

Neural networks are one of the most fascinating machine learning models and are used to solve wide range of problems in different areas of artificial intelligence and machine learning. Yet too few really understand how neural networks actually work. This course will take you on a fun and unhurried journey, starting from very simple ideas, and gradually building up an understanding of how neural networks work. The purpose of this course is to make neural networks accessible to as many students as possible.

In this course I’m going to explain the key aspects of neural networks and provide you with a foundation to get started with advanced topics. You will build a solid foundation knowledge of how a neural network learns from data, and the principles behind it. You will not only learn how to train neural networks, but will also explore generalization of these networks. Later we will delve into combining different neural network models and work with the real-world use cases. You’ll understand how to solve complex computational problems efficiently.

Practical Statistics For Data And Business Analysis [Size: 6.34 GB] ... This course material is prepared from highly experienced engineers worked in a leader companies like Microsoft , Facebook and Google. After hard working from five months ago we created +270 Lectures/Articles to cover everything related to practical statistics. In no time with simple and easy way you will learn and love statistics. We stress in this course to make it very spontaneous to make all students love statistics .

MATLAB Master Class: Go from Beginner to Expert in MATLAB [Size: 9.25 GB] ... MATLAB from beginner to advance level with Advanced Data Types and Applications from Data Science and Data Preprocessing: MATLAB (matrix laboratory) is one of the fundamental and leading programming language and is a must learn skill for anyone who want to develop a career in engineering, science or related fields. Excellent MATLAB programming skills is therefore a crucial factor in making or breaking your career. This course is designed from a perspective of a student who has no prior knowledge of MATLAB. The course starts from the very basic concepts and then built on top of those basic concepts and move towards more advanced topics such as visualization, exporting and importing of data, advance data types and data structures and advance programming constructs. To get the real feel of MATLAB in solving and analyzing real life problems, the course includes machine learning topics in data science and data preprocessing. The course is fun and exciting, but at the same time we dive deep into MATLAB to uncover its power of formulating and analyzing real life problems. The course is structured into four different Parts. Below is the detailed outline of this course.

August 08, 2019 , , , ,
Microsoft Office Suite 2016: Beginner to Pro [Size: 3.17 GB] .... Microsoft Office Suite 2016 for beginners and intermediates. This course covers wide range of topics such as Microsoft Word, Excel, Powerpoint, Access 2016 all in one place.

Excel: Master Microsoft Excel from Beginner to Advanced + Build a solid understanding on the Basics of Microsoft Excel + Learn the most common Excel functions used in the Office + Maintain large sets of Excel data in a list or table + Create dynamic reports by mastering one of the most popular tools, PivotTables. Word: You will learn how to take full advantage of Microsoft Word + Begin with the basics of creating Microsoft Word documents + Various techniques to create dynamic layouts + Preparing documents for printing and exporting + Format documents effectively using Microsoft Word Styles + Control page formatting and flow with sections and page breaks + Create and Manage Table Layouts + Work with Tab Stops to Align Content Properly + Perform Mail Merges to create Mailing Labels and Form Letters + Build and Deliver Word Forms + Manage Templates + Track and Accept/Reject Changes to a Document. PPT: Create a fully-animated and transition-filled business presentation + Rapidly improve your workflow and design skills + Minimize text quantity on presentations by using graphs and images + Work comfortably with PowerPoint and many of its advanced features + Become one of the top PowerPoint users in your team + Carrying out regular tasks faster than ever + Create sophisticated and well-organized PowerPoint presentations + Feel more confident when delivering presentations to superiors + Make an impression at work and achieve your professional goals. Access: Understand how Access is constructed and how to use the major objects within it. + Be confident in moving around within Access and be able to build effective database solutions for their unique data needs. + What you’ll learn + Understand the basics of Access tables, queries, forms and reports. + Know how to structure tables being imported from Excel. + Know how to create powerful queries and use them to create and modify tables. + Understand how reports work and how to base them on tables or queries. + Know how to create forms and subforms.

Artificial Intelligence Masterclass: Enter the new era of Hybrid AI Models optimized by Deep NeuroEvolution, with a complete toolkit of ML, DL & AI Models [Size: 6.10 GB] ... Are you keen on Artificial Intelligence? Do want to learn to build the most powerful AI model developed so far and even play against it? Sounds tempting right… Then Artificial Intelligence Masterclass course is the right choice for you. This ultimate AI toolbox is all you need to nail it down with ease. You will get 10 hours step by step guide and the full roadmap which will help you build your own Hybrid AI Model from scratch. In this course, we will teach you how to develop the most powerful Artificial intelligence model based on the most robust Hybrid Intelligent System. So far this model proves to be the best state of the art AI ever created beating its predecessors at all the AI competitions with incredibly high scores. This Hybrid Model is aptly named the Full World Model, and it combines all the state of the art models of the different AI branches, including Deep Learning, Deep Reinforcement Learning, Policy Gradient, and even, Deep NeuroEvolution. Learn how to combine the below models in order to achieve best performing artificial intelligence system: Fully-Connected Neural Networks + Convolutional Neural Networks + Recurrent Neural Networks + Variational AutoEncoders + Mixed Density Networks + Genetic Algorithms + Evolution Strategies + Covariance Matrix Adaptation Evolution Strategy (CMA-ES) + Parameter-Exploring Policy Gradients.

National Research University Higher School of Economics [Size: 1.26 GB] ... The goal of this course is to give learners basic understanding of modern neural networks and their applications in computer vision and natural language understanding. The course starts with a recap of linear models and discussion of stochastic optimization methods that are crucial for training deep neural networks. Learners will study all popular building blocks of neural networks including fully connected layers, convolutional and recurrent layers. Learners will use these building blocks to define complex modern architectures in TensorFlow and Keras frameworks. In the course project learner will implement deep neural network for the task of image captioning which solves the problem of giving a text description for an input image. The prerequisites for this course are: 1) Basic knowledge of Python. 2) Basic linear algebra and probability. Please note that this is an advanced course and we assume basic knowledge of machine learning. You should understand: 1) Linear regression: mean squared error, analytical solution. 2) Logistic regression: model, cross-entropy loss, class probability estimation. 3) Gradient descent for linear models. Derivatives of MSE and cross-entropy loss functions. 4) The problem of overfitting. 5) Regularization for linear models. Who is this class for: Developers, analysts and researchers who are faced with tasks involving complex structure understanding such as image, sound and text analysis.

The topics in this course come from an analysis of real requirements in data scientist job listings from the biggest tech employers. We'll cover the machine learning, AI, and data mining techniques real employers are looking for, including: Deep Learning / Neural Networks (MLP's, CNN's, RNN's) with TensorFlow and Keras + Sentiment analysis + Image recognition and classification + Regression analysis + K-Means Clustering + Principal Component Analysis + Train/Test and cross validation + Bayesian Methods + Decision Trees and Random Forests + Multivariate Regression + Multi-Level Models + Support Vector Machines + Reinforcement Learning + Collaborative Filtering + K-Nearest Neighbor + Bias/Variance Tradeoff + Ensemble Learning + Term Frequency / Inverse Document Frequency + Experimental Design and A/B Tests.

Machine Learning, Classification and Algorithms using MATLAB: Learn to Implement Classification Algorithms In One of the Most Power Tool used by Scientists and Engineer.

 This course is designed to cover one of the most interesting areas of machine learning called classification. I will take you step-by-step in this course and will first cover the basics of MATLAB. Following that we will look into the details of how to use different machine learning algorithms using MATLAB. Specifically, we will be looking at the MATLAB toolbox called statistic and machine learning toolbox. We will implement some of the most commonly used classification algorithms such as K-Nearest Neighbor, Naive Bayes, Discriminant Analysis, Decision Tress, Support Vector Machines, Error Correcting Output Codes and Ensembles. Following that we will be looking at how to cross validate these models and how to evaluate their performances. Intuition into the classification algorithms is also included so that a person with no mathematical background can still comprehend the essential ideas. The following are the course outlines. Segment 1: Grabbing and Importing Dataset + Segment 2: K-Nearest Neighbor + Segment 3: Naive Bayes + Segment 4: Decision Trees + Segment 5: Discriminant Analysis + Segment 6: Support Vector Machines + Segment 7: Error Correcting Output Codes + Segment 8: Classification with Ensembles + Segment 9: Validation Methods + Segment 10: Evaluating Performance.

October 05, 2018 , ,
Welcome to my BIGGEST, most complete, Microsoft Excel course ever!: Excel is one of the most important skills in today’s job market. Having a solid working knowledge of Excel can improve your job performance, help you qualify for raises and promotions, or even help you land that new job that you deserve.

If you need to know Excel, you’re in the right place: This course [Size: 2.34GB] walks you through Excel from the very basics of opening Excel all the way to advanced Excel skills used by the pros, like pivot tables and macros. My goal with this course is to give you the skills you need to get that raise, promotion, or new job you deserve. This course was created using Excel 2016, but it is good for versions 2013 and 2010 as well. It was created for use with a PC (personal computer). Mac (Macintosh) users can still benefit from this course, but the course doesn’t cover Mac-specific keyboard shortcuts and interface. We’ll start at the very beginning, with opening and saving a worksheet in Excel: I’ll show you what all the tools do and walk you through each Excel skill that you need to know. I’ll show you how to enter and format your data and how to create charts, tables and pivot tables so you can organize and analyze your data. I’ll teach you amazing keyboard shortcuts to make your work faster and more productive. You’ll learn how to record macros so you can do a whole series of actions with the just click of a button. We’ll keep going until you’re a pro at Excel. When you finish this course, you’ll know more than most people who use Excel at their job every day.

July 12, 2018 , ,
LaTeX: A document preparation system [Size: 284 MB] ... This course is open to anyone who wants to learn how to create a professional quality, typeset publication. In addition to improving the aesthetic quality of your work, LaTeX users benefit from automating many of the tedious processes involved in writing a professional publication. LaTeX allows you to manage references, figures, tables, footnotes, formatting, mathematical equations, algorithms, scientific proofs, and more in a programmatic fashion that provides benefits far exceeding that of word processing software. Need to format a paper for a specific venue? Many academic journals and conferences provide LaTeX Style files (.sty) for correctly typesetting your submission. With one line of code, you can modify your publication to match the style of many leading academic publishing outlets. + Not writing a paper? LaTeX can also be used for books, reports, technical and business documents, screenplays, resumes, letters ... anything you want to write! + The course uses tools that are freely available online for Mac (Linux) and Windows users. Templates are included for download to get you started.

Topics include: Section 1: Getting Started - Section 2: Creating a New Document (.tex) - Section 3: Lists, Tables and Graphics - Section 4: Bibliography (.bib and .bbl) - Section 5: Elements in Science and Mathematics - Section 6: Managing Packages and Style Files (.sty) - Section 7: Advanced Topics - Section 8: Next Steps.

July 10, 2018 , ,
AutoCAD Civil 3D Essential Training Course [Size: 1.03 GB] ... AutoCAD Civil 3D software is a design and documentation solution for civil engineering that supports building information modeling (BIM) workflows. By learning to use AutoCAD Civil 3D, you can improve project performance, maintain consistent data, follow standard processes, and respond faster to change. This course gets you up and running with AutoCAD Civil 3D. First, instructor Josh Modglin shows how to model a surface, lay out parcels, and design geometry, including the making of horizontal alignments and vertical profiles. Next, Josh demonstrates how to create corridors, cross sections, pipe networks, and pressure networks. Then, he covers working with feature lines and grading objects, and how to share your data. He wraps up by providing an overview of plan production tools.

Topics include: - Navigating the Civil 3D interface - Using point groups and description keys - Importing survey data - Managing figures - Creating and analyzing surfaces - Creating parcels - Working with alignments - Working with profiles and profile views - Working with assemblies and subassemblies - Creating Basic and Advanced Corridors - Using an Intersection Object - Making sample lines, cross sections, and section views - Creating a pipe network - Understanding pressure parts - Creating and editing feature lines - Creating and editing grading objects - Sharing and referencing data.

Machine Learning Classification Algorithms using MatLab [Size: 580 MB] ... This course is designed to cover one of the most interesting areas of machine learning called classification. I will take you step-by-step in this course and will first cover the basics of MATLAB. Following that we will look into the details of how to use different machine learning algorithms using MATLAB. Specifically, we will be looking at the MATLAB toolbox called statistic and machine learning toolbox. We will implement some of the most commonly used classification algorithms such as K-Nearest Neighbor, Naive Bayes, Discriminant Analysis, Decision Tress, Support Vector Machines, Error Correcting Output Codes and Ensembles. Following that we will be looking at how to cross validate these models and how to evaluate their performances. Intuition into the classification algorithms is also included so that a person with no mathematical background can still comprehend the essential ideas. The following are the course outlines.

Table of Contents: - Segment 1: Instructor and Course Introduction - Segment 2: MATLAB Crash Course - Segment 3: Grabbing and Importing Dataset - Segment 4: K-Nearest Neighbor - Segment 5: Naive Bayes - Segment 6: Decision Trees - Segment 7: Discriminant Analysis - Segment 8: Support Vector Machines - Segment 9: Error Correcting Output Codes - Segment 10: Classification with Ensembles - Segment 11: Validation Methods - Segment 12: Evaluating Performance. This course is really good for a beginner. It will help you to start from the ground up and move on to more complicated areas. You receive knowledge from a Ph.D. in Computer science (machine learning) with over 10 years of teaching and research experience.

Learn Neural Networks using Matlab Course [Size: 187 MB] ... MATLAB (matrix laboratory) is a multi-paradigm numerical computing environment and fourth-generation programming language developed by MathWorks. Although MATLAB is intended primarily for numerical computing, but by optional toolboxes, using the MuPAD symbolic engine, has access to symbolic computing capabilities too. One of these toolboxes is Neural Network toolbox. This toolbox is free, open source software for simulating models of brain and central nervous system, based on MATLAB computational platform. In these courses you will learn the general principles of Neural Network Toolbox designed in Matlab and you will be able to use this Toolbox efficiently as well.

The list of contents is: - Introduction: in this chapter the Neural Network Toolbox is Defined and introduced. An overview of neural network application is provided and the neural network training process for pattern recognition, function fitting and clustering data in demonstrated. - Neuron models:  A description of the neuron model is provided, including simple neurons, transfer functions, and vector inputs and single and multiple layers neurons are explained. The format of input data structures is very effective in the simulation results of both static and dynamic networks. So this effect is discussed in this chapter too. And finally the incremental and batch training rule is explained. - Perceptron networks: In this chapter the perceptron architecture is shown and it is explained how to create a perceptron in Neural network toolbox. The perceptron learning rule and its training algorithm is discussed and finally the network/Data manager GUI is explained. - Linear filters: in this chapter linear networks and linear system design function is discussed. The tapped delay lines and linear filters are discussed and at the end of the chapter LMS algorithm and linear classification algorithm used for linear filters are explained. - Backpropagation networks: The architecture, simulation, and several high-performance backpropagation training algorithms of backpropagation networks are discussed in this chapter. - Conclusion: in this chapter the memory and speed of different backpropagation training algorithms are illustrated. And at the end of the chapter all these algorithms are compared to help you select the best training algorithm for your problem in hand. - Matlab Software Installation: You are required to install the Matlab Software on your machine, so you can start executing the codes, and examples we work during the course.

A Boltzmann machine is a network of symmetrically connected, neuron-like units that make stochastic decisions about whether to be on or off. Boltzmann machines have a simple learning algorithm that allows them to discover interesting features that represent complex regularities in the training data. The learning algorithm is very slow in networks with many layers of feature detectors, but it is fast in "restricted Boltzmann machines" that have a single layer of feature detectors. Many hidden layers can be learned efficiently by composing restricted Boltzmann machines, using the feature activations of one as the training data for the next. Boltzmann machines are used to solve two quite different computational problems. For a search problem, the weights on the connections are fixed and are used to represent a cost function. The stochastic dynamics of a Boltzmann machine then allow it to sample binary state vectors that have low values of the cost function. For a learning problem, the Boltzmann machine is shown a set of binary data vectors and it must learn to generate these vectors with high probability. To do this, it must find weights on the connections so that, relative to other possible binary vectors, the data vectors have low values of the cost function. To solve a learning problem, Boltzmann machines make many small updates to their weights, and each update requires them to solve many different search problems.

A recurrent neural network (RNN) is a type of advanced artificial neural network (ANN) that involves directed cycles in memory. One aspect of recurrent neural networks is the ability to build on earlier types of networks with fixed-size input vectors and output vectors. The use of recurrent neural networks are often related to deep learning and the use of sequences to evolve models that simulate the neural activity in the human brain. In terms of practical application, RNNs have been an active area of focus for many professionals for uses like image processing, language processing, and even models that add characters to text one at a time. By playing around with these text generation models, scientists have been able to produce samples that look a lot like different kinds of human writing – for example, modern investment op-eds, or classical Shakespeare plays. The RNN has been able to generate text results that demonstrate the ability to learn English from scratch, or from very limited programming inputs. Many examples of using RNNs produce text that is not grammatically correct. The idea is that a large number of these experiments and systems need additional supports to really become useful – but they do demonstrate amazing artificial intelligence power to model the human generation of language.

Convolutional networks (CNNs) are deep artificial neural networks that can be used to classify images (name what they see), cluster them by similarity (photo search), and perform object recognition within scenes. They are algorithms that can identify faces, individuals, street signs, eggplants, platypuses and many other aspects of visual data. Convolutional networks perform optical character recognition (OCR) to digitize text and make natural-language processing possible on analog and hand-written documents, where the images are symbols to be transcribed. CNNs can also be applied to sound when it is represented visually as a spectrogram. More recently, convolutional networks have been applied directly to text analytics as well as graph data with graph convolutional networks. The efficacy of convolutional nets (ConvNets or CNNs) in image recognition is one of the main reasons why the world has woken up to the efficacy of deep learning. They are powering major advances in machine vision, which has obvious applications for self-driving cars, robotics, drones, security, medical diagnoses, and treatments for the visually impaired.

Neural networks are a set of algorithms, modeled loosely after the human brain, that are designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeling or clustering raw input. The patterns they recognize are numerical, contained in vectors, into which all real-world data, be it images, sound, text or time series, must be translated. Neural networks help us cluster and classify. You can think of them as a clustering and classification layer on top of data you store and manage. They help to group unlabeled data according to similarities among the example inputs, and they classify data when they have a labeled dataset to train on. (To be more precise, neural networks extract features that are fed to other algorithms for clustering and classification; so you can think of deep neural networks as components of larger machine-learning applications involving algorithms for reinforcement learning, classification and regression.)

GUIs (also known as graphical user interfaces or UIs) provide point-and-click control of software applications, eliminating the need to learn a language or type commands in order to run the application. MATLAB apps are self-contained MATLAB programs with GUI front ends that automate a task or calculation. The GUI typically contains controls such as menus, toolbars, buttons, and sliders. Many MATLAB products, such as Curve Fitting Toolbox, Signal Processing Toolbox, and Control System Toolbox include apps with custom user interfaces. You can also create your own custom apps, including their corresponding UIs, for others to use.

Particle swarm optimization (PSO) is a population-based stochastic approach for solving continuous and discrete optimization problems. In particle swarm optimization, simple software agents, called particles, move in the search space of an optimization problem. The position of a particle represents a candidate solution to the optimization problem at hand. Each particle searches for better positions in the search space by changing its velocity according to rules originally inspired by behavioral models of bird flocking. Particle swarm optimization belongs to the class of swarm intelligence techniques that are used to solve optimization problems.

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