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  1. Raptor

    Hola, un saludo a todo el foro

    Hola Hammer y bienvenido. Si tienes alguna duda eres libre de crear un tema en nuestros foros Saludos!
  2. View File 【English Subtitles】 Animation tutorial for digital painting Requirements Adobe Photoshop CS5(or newer version) Clip Studio Paint Drawing tablet Description How many times did you pick up your pen only to put it down again? How many times did you want to paint the character in your mind but do not know where to start? Let's solve this dilemma and make everything possible! Now hold your pen and follow us! This course is tailored for YOU. This course is gaining popularity not only overseas, but also in Japan. It includes many basic skills for digital painting, and videos, so that you can catch the key point of each lesson easily. We teach from the know-how of the software to the deep understanding of drawing skills, which enables every single learner to start from zero. We teach not only how to use Photoshop but also Clip Studio Paint. Through this course, you can lay a solid foundation for your future learning as well. No worries about the language barrier. We have specific English subtitles for all sentences of the videos. During the course, your questions will be translated to the teacher by teaching assistants and you will get feedback immediately. These are the reasons why this course is definitely the best choice for every beginner. So why don't you join us? Who is the target audience? Anyone who wants to learn digital painting even without any painting skills Submitter Raptor Submitted 17/09/18 Category Artes y Manualidades Idioma English Subtítulo English Nivel Principiante Peso del Curso 100 MB+  
  3. View File The Complete Machine Learning Course with Python Requirements Basic Python programming knowledge is necessary Good understanding of linear algebra Description The average salary of a Machine Learning Engineer in the US is $166,000! By the end of this course, you will have a Portfolio of 12 Machine Learning projects that will help you land your dream job or enable you to solve real life problems in your business, job or personal life with Machine Learning algorithms. Come learn Machine Learning with Python this exciting course with Anthony NG, a Senior Lecturer in Singapore who has followed Rob Percival’s “project based" teaching style to bring you this hands-on course. With over 18 hours of content and more than fifty 5 star rating, it's already the longest and best rated Machine Learning course on Udemy! Build Powerful Machine Learning Models to Solve Any Problem You'll go from beginner to extremely high-level and your instructor will build each algorithm with you step by step on screen. By the end of the course, you will have trained machine learning algorithms to classify flowers, predict house price, identify handwritings or digits, identify staff that is most likely to leave prematurely, detect cancer cells and much more! Inside the course, you'll learn how to: Set up a Python development environment correctly Gain complete machine learning tool sets to tackle most real world problems Understand the various regression, classification and other ml algorithms performance metrics such as R-squared, MSE, accuracy, confusion matrix, prevision, recall, etc. and when to use them. Combine multiple models with by bagging, boosting or stacking Make use to unsupervised Machine Learning (ML) algorithms such as Hierarchical clustering, k-means clustering etc. to understand your data Develop in Jupyter (IPython) notebook, Spyder and various IDE Communicate visually and effectively with Matplotlib and Seaborn Engineer new features to improve algorithm predictions Make use of train/test, K-fold and Stratified K-fold cross validation to select correct model and predict model perform with unseen data Use SVM for handwriting recognition, and classification problems in general Use decision trees to predict staff attrition Apply the association rule to retail shopping datasets And much much more! No Machine Learning required. Although having some basic Python experience would be helpful, no prior Python knowledge is necessary as all the codes will be provided and the instructor will be going through them line-by-line and you get friendly support in the Q&A area. Make This Investment in Yourself If you want to ride the machine learning wave and enjoy the salaries that data scientists make, then this is the course for you! Take this course and become a machine learning engineer! Who is the target audience? Anyone willing and interested to learn machine learning algorithm with Python Any one who has a deep interest in the practical application of machine learning to real world problems Anyone wishes to move beyond the basics and develop an understanding of the whole range of machine learning algorithms Any intermediate to advanced EXCEL users who is unable to work with large datasets Anyone interested to present their findings in a professional and convincing manner Anyone who wishes to start or transit into a career as a data scientist Anyone who wants to apply machine learning to their domain Submitter Raptor Submitted 17/09/18 Category Datos y Análisis Idioma English Subtítulo Español English Portugués Nivel Todo los niveles Peso del Curso 1 GB+  
  4. Raptor


    Este foro de intercambio sera eliminado.
  5. Seria bueno que hagan una colección de los cursos publicados en EyBooks sobre Diseño, para que la persona interesada pueda saber como empezar esta carrera.
  6. View File Deep Learning A-Z™: Hands-On Artificial Neural Networks Requirements Just some high school mathematics level Description *** As seen on Kickstarter *** Artificial intelligence is growing exponentially. There is no doubt about that. Self-driving cars are clocking up millions of miles, IBM Watson is diagnosing patients better than armies of doctors and Google Deepmind's AlphaGo beat the World champion at Go - a game where intuition plays a key role. But the further AI advances, the more complex become the problems it needs to solve. And only Deep Learning can solve such complex problems and that's why it's at the heart of Artificial intelligence. --- Why Deep Learning A-Z? --- Here are five reasons we think Deep Learning A-Z™ really is different, and stands out from the crowd of other training programs out there: 1. ROBUST STRUCTURE The first and most important thing we focused on is giving the course a robust structure. Deep Learning is very broad and complex and to navigate this maze you need a clear and global vision of it. That's why we grouped the tutorials into two volumes, representing the two fundamental branches of Deep Learning: Supervised Deep Learning and Unsupervised Deep Learning. With each volume focusing on three distinct algorithms, we found that this is the best structure for mastering Deep Learning. 2. INTUITION TUTORIALS So many courses and books just bombard you with the theory, and math, and coding... But they forget to explain, perhaps, the most important part: why you are doing what you are doing. And that's how this course is so different. We focus on developing an intuitive *feel* for the concepts behind Deep Learning algorithms. With our intuition tutorials you will be confident that you understand all the techniques on an instinctive level. And once you proceed to the hands-on coding exercises you will see for yourself how much more meaningful your experience will be. This is a game-changer. 3. EXCITING PROJECTS Are you tired of courses based on over-used, outdated data sets? Yes? Well then you're in for a treat. Inside this class we will work on Real-World datasets, to solve Real-World business problems. (Definitely not the boring iris or digit classification datasets that we see in every course). In this course we will solve six real-world challenges: Artificial Neural Networks to solve a Customer Churn problem Convolutional Neural Networks for Image Recognition Recurrent Neural Networks to predict Stock Prices Self-Organizing Maps to investigate Fraud Boltzmann Machines to create a Recomender System Stacked Autoencoders* to take on the challenge for the Netflix $1 Million prize *Stacked Autoencoders is a brand new technique in Deep Learning which didn't even exist a couple of years ago. We haven't seen this method explained anywhere else in sufficient depth. 4. HANDS-ON CODING In Deep Learning A-Z™ we code together with you. Every practical tutorial starts with a blank page and we write up the code from scratch. This way you can follow along and understand exactly how the code comes together and what each line means. In addition, we will purposefully structure the code in such a way so that you can download it and apply it in your own projects. Moreover, we explain step-by-step where and how to modify the code to insert YOUR dataset, to tailor the algorithm to your needs, to get the output that you are after. This is a course which naturally extends into your career. 5. IN-COURSE SUPPORT Have you ever taken a course or read a book where you have questions but cannot reach the author? Well, this course is different. We are fully committed to making this the most disruptive and powerful Deep Learning course on the planet. With that comes a responsibility to constantly be there when you need our help. In fact, since we physically also need to eat and sleep we have put together a team of professional Data Scientists to help us out. Whenever you ask a question you will get a response from us within 48 hours maximum. No matter how complex your query, we will be there. The bottom line is we want you to succeed. --- The Tools --- Tensorflow and Pytorch are the two most popular open-source libraries for Deep Learning. In this course you will learn both! TensorFlow was developed by Google and is used in their speech recognition system, in the new google photos product, gmail, google search and much more. Companies using Tensorflow include AirBnb, Airbus, Ebay, Intel, Uber and dozens more. PyTorch is as just as powerful and is being developed by researchers at Nvidia and leading universities: Stanford, Oxford, ParisTech. Companies using PyTorch include Twitter, Saleforce and Facebook. So which is better and for what? Well, in this course you will have an opportunity to work with both and understand when Tensorflow is better and when PyTorch is the way to go. Throughout the tutorials we compare the two and give you tips and ideas on which could work best in certain circumstances. The interesting thing is that both these libraries are barely over 1 year old. That's what we mean when we say that in this course we teach you the most cutting edge Deep Learning models and techniques. --- More Tools --- Theano is another open source deep learning library. It's very similar to Tensorflow in its functionality, but nevertheless we will still cover it. Keras is an incredible library to implement Deep Learning models. It acts as a wrapper for Theano and Tensorflow. Thanks to Keras we can create powerful and complex Deep Learning models with only a few lines of code. This is what will allow you to have a global vision of what you are creating. Everything you make will look so clear and structured thanks to this library, that you will really get the intuition and understanding of what you are doing. --- Even More Tools --- Scikit-learn the most practical Machine Learning library. We will mainly use it: to evaluate the performance of our models with the most relevant technique, k-Fold Cross Validation to improve our models with effective Parameter Tuning to preprocess our data, so that our models can learn in the best conditions And of course, we have to mention the usual suspects. This whole course is based on Python and in every single section you will be getting hours and hours of invaluable hands-on practical coding experience. Plus, throughout the course we will be using Numpy to do high computations and manipulate high dimensional arrays, Matplotlib to plot insightful charts and Pandas to import and manipulate datasets the most efficiently. --- Who Is This Course For? --- As you can see, there are lots of different tools in the space of Deep Learning and in this course we make sure to show you the most important and most progressive ones so that when you're done with Deep Learning A-Z™ your skills are on the cutting edge of today's technology. If you are just starting out into Deep Learning, then you will find this course extremely useful. Deep Learning A-Z™ is structured around special coding blueprint approaches meaning that you won't get bogged down in unnecessary programming or mathematical complexities and instead you will be applying Deep Learning techniques from very early on in the course. You will build your knowledge from the ground up and you will see how with every tutorial you are getting more and more confident. If you already have experience with Deep Learning, you will find this course refreshing, inspiring and very practical. Inside Deep Learning A-Z™ you will master some of the most cutting-edge Deep Learning algorithms and techniques (some of which didn't even exist a year ago) and through this course you will gain an immense amount of valuable hands-on experience with real-world business challenges. Plus, inside you will find inspiration to explore new Deep Learning skills and applications. --- Real-World Case Studies --- Mastering Deep Learning is not just about knowing the intuition and tools, it's also about being able to apply these models to real-world scenarios and derive actual measurable results for the business or project. That's why in this course we are introducing six exciting challenges: #1 Churn Modelling Problem In this part you will be solving a data analytics challenge for a bank. You will be given a dataset with a large sample of the bank's customers. To make this dataset, the bank gathered information such as customer id, credit score, gender, age, tenure, balance, if the customer is active, has a credit card, etc. During a period of 6 months, the bank observed if these customers left or stayed in the bank. Your goal is to make an Artificial Neural Network that can predict, based on geo-demographical and transactional information given above, if any individual customer will leave the bank or stay (customer churn). Besides, you are asked to rank all the customers of the bank, based on their probability of leaving. To do that, you will need to use the right Deep Learning model, one that is based on a probabilistic approach. If you succeed in this project, you will create significant added value to the bank. By applying your Deep Learning model the bank may significantly reduce customer churn. #2 Image Recognition In this part, you will create a Convolutional Neural Network that is able to detect various objects in images. We will implement this Deep Learning model to recognize a cat or a dog in a set of pictures. However, this model can be reused to detect anything else and we will show you how to do it - by simply changing the pictures in the input folder. For example, you will be able to train the same model on a set of brain images, to detect if they contain a tumor or not. But if you want to keep it fitted to cats and dogs, then you will literally be able to a take a picture of your cat or your dog, and your model will predict which pet you have. We even tested it out on Hadelin’s dog! #3 Stock Price Prediction In this part, you will create one of the most powerful Deep Learning models. We will even go as far as saying that you will create the Deep Learning model closest to “Artificial Intelligence”. Why is that? Because this model will have long-term memory, just like us, humans. The branch of Deep Learning which facilitates this is Recurrent Neural Networks. Classic RNNs have short memory, and were neither popular nor powerful for this exact reason. But a recent major improvement in Recurrent Neural Networks gave rise to the popularity of LSTMs (Long Short Term Memory RNNs) which has completely changed the playing field. We are extremely excited to include these cutting-edge deep learning methods in our course! In this part you will learn how to implement this ultra-powerful model, and we will take the challenge to use it to predict the real Google stock price. A similar challenge has already been faced by researchers at Stanford University and we will aim to do at least as good as them. #4 Fraud Detection According to a recent report published by Markets & Markets the Fraud Detection and Prevention Market is going to be worth $33.19 Billion USD by 2021. This is a huge industry and the demand for advanced Deep Learning skills is only going to grow. That’s why we have included this case study in the course. This is the first part of Volume 2 - Unsupervised Deep Learning Models. The business challenge here is about detecting fraud in credit card applications. You will be creating a Deep Learning model for a bank and you are given a dataset that contains information on customers applying for an advanced credit card. This is the data that customers provided when filling the application form. Your task is to detect potential fraud within these applications. That means that by the end of the challenge, you will literally come up with an explicit list of customers who potentially cheated on their applications. #5 & 6 Recommender Systems From Amazon product suggestions to Netflix movie recommendations - good recommender systems are very valuable in today's World. And specialists who can create them are some of the top-paid Data Scientists on the planet. We will work on a dataset that has exactly the same features as the Netflix dataset: plenty of movies, thousands of users, who have rated the movies they watched. The ratings go from 1 to 5, exactly like in the Netflix dataset, which makes the Recommender System more complex to build than if the ratings were simply “Liked” or “Not Liked”. Your final Recommender System will be able to predict the ratings of the movies the customers didn’t watch. Accordingly, by ranking the predictions from 5 down to 1, your Deep Learning model will be able to recommend which movies each user should watch. Creating such a powerful Recommender System is quite a challenge so we will give ourselves two shots. Meaning we will build it with two different Deep Learning models. Our first model will be Deep Belief Networks, complex Boltzmann Machines that will be covered in Part 5. Then our second model will be with the powerful AutoEncoders, my personal favorites. You will appreciate the contrast between their simplicity, and what they are capable of. And you will even be able to apply it to yourself or your friends. The list of movies will be explicit so you will simply need to rate the movies you already watched, input your ratings in the dataset, execute your model and voila! The Recommender System will tell you exactly which movies you would love one night you if are out of ideas of what to watch on Netflix! --- Summary --- In conclusion, this is an exciting training program filled with intuition tutorials, practical exercises and real-World case studies. We are super enthusiastic about Deep Learning and hope to see you inside the class! Kirill & Hadelin Who is the target audience? Anyone interested in Deep Learning Students who have at least high school knowledge in math and who want to start learning Deep Learning Any intermediate level people who know the basics of Machine Learning or Deep Learning, including the classical algorithms like linear regression or logistic regression and more advanced topics like Artificial Neural Networks, but who want to learn more about it and explore all the different fields of Deep Learning Anyone who is not that comfortable with coding but who is interested in Deep Learning and wants to apply it easily on datasets Any students in college who want to start a career in Data Science Any data analysts who want to level up in Deep Learning Any people who are not satisfied with their job and who want to become a Data Scientist Any people who want to create added value to their business by using powerful Deep Learning tools Any business owners who want to understand how to leverage the Exponential technology of Deep Learning in their business Any Entrepreneur who wants to create disruption in an industry using the most cutting edge Deep Learning algorithms Submitter Raptor Submitted 17/09/18 Category Datos y Análisis Idioma English Subtítulo Español English Portugués Japanese Italian Turkish Nivel Todo los niveles Peso del Curso 1 GB+  
  7. View File Machine Learning A-Z™: Hands-On Python & R In Data Science Requirements Just some high school mathematics level. Description Interested in the field of Machine Learning? Then this course is for you! This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms and coding libraries in a simple way. We will walk you step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science. This course is fun and exciting, but at the same time we dive deep into Machine Learning. It is structured the following way: Part 1 - Data Preprocessing Part 2 - Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression Part 3 - Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification Part 4 - Clustering: K-Means, Hierarchical Clustering Part 5 - Association Rule Learning: Apriori, Eclat Part 6 - Reinforcement Learning: Upper Confidence Bound, Thompson Sampling Part 7 - Natural Language Processing: Bag-of-words model and algorithms for NLP Part 8 - Deep Learning: Artificial Neural Networks, Convolutional Neural Networks Part 9 - Dimensionality Reduction: PCA, LDA, Kernel PCA Part 10 - Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost Moreover, the course is packed with practical exercises which are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models. And as a bonus, this course includes both Python and R code templates which you can download and use on your own projects. Who is the target audience? Anyone interested in Machine Learning. Students who have at least high school knowledge in math and who want to start learning Machine Learning. Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning. Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets. Any students in college who want to start a career in Data Science. Any data analysts who want to level up in Machine Learning. Any people who are not satisfied with their job and who want to become a Data Scientist. Any people who want to create added value to their business by using powerful Machine Learning tools. Submitter Raptor Submitted 16/09/18 Category Datos y Análisis Idioma English Subtítulo Español English Portugués Japanese Italian Turkish Nivel Todo los niveles Peso del Curso 5 GB+  
  8. View File Python for Data Science and Machine Learning Bootcamp Requirements Some programming experience Admin permissions to download files Description Are you ready to start your path to becoming a Data Scientist! This comprehensive course will be your guide to learning how to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms! Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world's most interesting problems! This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Data Science! This comprehensive course is comparable to other Data Science bootcamps that usually cost thousands of dollars, but now you can learn all that information at a fraction of the cost! With over 100 HD video lectures and detailed code notebooks for every lecture this is one of the most comprehensive course for data science and machine learning on Udemy! We'll teach you how to program with Python, how to create amazing data visualizations, and how to use Machine Learning with Python! Here a just a few of the topics we will be learning: Programming with Python NumPy with Python Using pandas Data Frames to solve complex tasks Use pandas to handle Excel Files Web scraping with python Connect Python to SQL Use matplotlib and seaborn for data visualizations Use plotly for interactive visualizations Machine Learning with SciKit Learn, including: Linear Regression K Nearest Neighbors K Means Clustering Decision Trees Random Forests Natural Language Processing Neural Nets and Deep Learning Support Vector Machines and much, much more! Enroll in the course and become a data scientist today! Who is the target audience? This course is meant for people with at least some programming experience Submitter Raptor Submitted 16/09/18 Category Datos y Análisis Idioma English Subtítulo Español English Portugués Japanese Italian Turkish Nivel Todo los niveles Peso del Curso 5 GB+  
  9. Raptor

    Aprenda a usar el Buscador de EyBooks

    Al utilizar las comillas es forzar la buscada y que el resultado sea totalmente especifico.
  10. Raptor

    Aprenda a usar el Buscador de EyBooks

    Debes utilizar más de 5 caracteres para que te muestre resultados.
  11. Raptor

    Curso Single Page Application con JavaScript

    @Start La calidad de vídeo es muy baja.
  12. Yo he podido descargar, estas seguro de lo que dices? Necesitare algunas capturas.
  13. Raptor

    Aprenda a usar el Buscador de EyBooks

    Recomiendo no utilizar simbolos
  14. En efecto, udemy-dl tiene mejores opciones, en lo personal me gusta el hecho que descarga todo los subtitulos disponibles, ya sea francés, ingles, portugués, etc. Todo esto lo descarga al tiro.

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