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Deep Learning: Instructional Materials


From perceptrons to deep learning

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Have you ever wondered if it's possible to learn all there is to know about machine learning and deep learning from a book? Machine Learning--A Journey to Deep Learning, with Exercises and Answers is designed to give the self-taught student a solid foundation in machine learning with step-by-step solutions to the formative exercises and many concrete examples. By going through this text, readers should become able to apply and understand machine learning algorithms as well as create new ones. The statistical approach leads to the definition of regularization out of the example of regression. Building on regression, we develop the theory of perceptrons and logistic regression.


TensorFlow 2.0: A Complete Guide on the Brand New TensorFlow

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Free Coupon Discount - TensorFlow 2.0: A Complete Guide on the Brand New TensorFlow, Build Amazing Applications of Deep Learning and Artificial Intelligence in TensorFlow 2.0 4.2 (673 ratings) Created by Hadelin de Ponteves, Kirill Eremenko, SuperDataScience Team, Luka Anicin  English [Auto-generated] Preview this Udemy Course - GET COUPON CODE 100% Off Udemy Coupon . Free Udemy Courses . Online Classes


Artificial Neural Networks for Business Managers in R Studio

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You're looking for a complete Artificial Neural Network (ANN) course that teaches you everything you need to create a Neural Network model in R, right? You've found the right Neural Networks course! Identify the business problem which can be solved using Neural network Models. Have a clear understanding of Advanced Neural network concepts such as Gradient Descent, forward and Backward Propagation etc. Create Neural network models in R using Keras and Tensorflow libraries and analyze their results. How this course will help you?


TensorFlow 2.0 Practical

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Artificial Intelligence (AI) revolution is here and TensorFlow 2.0 is finally here to make it happen much faster! TensorFlow 2.0 is Google's most powerful, recently released open source platform to build and deploy AI models in practice. AI technology is experiencing exponential growth and is being widely adopted in the Healthcare, defense, banking, gaming, transportation and robotics industries. The purpose of this course is to provide students with practical knowledge of building, training, testing and deploying Artificial Neural Networks and Deep Learning models using TensorFlow 2.0 and Google Colab. The course provides students with practical hands-on experience in training Artificial Neural Networks and Convolutional Neural Networks using real-world dataset using TensorFlow 2.0 and Google Colab.


Deep Learning: Convolutional Neural Networks in Python

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For Data Science, Machine Learning, and AI Created by Lazy Programmer Inc. English [Auto], Italian [Auto], Preview this Udemy Course GET COUPON CODE Description *** NOW IN TENSORFLOW 2 and PYTHON 3 *** Learn about one of the most powerful Deep Learning architectures yet! The Convolutional Neural Network (CNN) has been used to obtain state-of-the-art results in computer vision tasks such as object detection, image segmentation, and generating photo-realistic images of people and things that don't exist in the real world! This course will teach you the fundamentals of convolution and why it's useful for deep learning and even NLP (natural language processing). You will learn about modern techniques such as data augmentation and batch normalization, and build modern architectures such as VGG yourself. This course will teach you: The basics of machine learning and neurons (just a review to get you warmed up!) Neural networks for classification and regression (just a review to get you warmed up!) How to model image data in code How to model text data for NLP (including preprocessing steps for text) How to build an CNN using Tensorflow 2 How to use batch normalization and dropout regularization in Tensorflow 2 How to do image classification in Tensorflow 2 How to do data preprocessing for your own custom image dataset How to use Embeddings in Tensorflow 2 for NLP How to build a Text Classification CNN for NLP (examples: spam detection, sentiment analysis, parts-of-speech tagging, named entity recognition) All of the materials required for this course can be downloaded and installed for FREE.


29 Best Data Analytics Certification Online Courses & Tutorials

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Do you want to upgrade your skills with Best Data Analytics Certification Online to stand out in the industry? Here is a list of Best Data Analytics Courses Online, Training, Tutorials, and Classes to assist you to become a top Data Analyst. Now Big data, Data Science, Machine Learning, Deep Learning, Artificial Intelligence (AI), Analytics, Python, R, r-stats are the most trending and highly demanding subjects in every sector for almost every industry. Learn business analytics to get hands-on knowledge of big data analytics, data visualization, data management, and data mining as an analytics professional. The majority of the business professionals are upgrading their skills with Best Data Analytics Training to standout in their industry.


A deep learning technique to solve Rubik's cube and other problems step-by-step

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Colin G. Johnson, an associate professor at the University of Nottingham, recently developed a deep-learning technique that can learn a so-called "fitness function" from a set of sample solutions to a problem. This technique, presented in a paper published in Wiley's Expert Systems journal, was initially trained to solve the Rubik's cube, the popular 3-D combination puzzle invented by Hungarian sculptor Ernő Rubik. "The aim of our paper was to use machine learning to learn to solve the Rubik's cube," Colin G. Johnson, one of the researchers who carried out the study, told TechXplore. "Rubik's cube is a very complex puzzle, but any of the vast number of combinations is at most 20 steps from a solution. So the approach we take here is to try and solve the problem by learning to do each of those steps individually."


15 new deep learning courses for top instructors from udemy

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This course will guide you through how to use Google's latest TensorFlow 2 framework to create artificial neural networks for deep learning! This course aims to give you an easy to understand guide to the complexities of Google's TensorFlow 2 framework in a way that is easy to understand. We'll focus on understanding the latest updates to TensorFlow and leveraging the Keras API (TensorFlow 2.0's official API) to quickly and easily build models. In this course we will build models to forecast future price homes, classify medical images, predict future sales data, generate complete new text artificially and much more! This course is designed to balance theory and practical implementation, with complete jupyter notebook guides of code and easy to reference slides and notes.


Newbie's Deep Learning Tutorial: Learn Keras Machine Learning

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Keras is a high-level API, which is aimed to build and train deep learning with Python. This tutorial is created by data scientist and app developer Nimish Narang, which already created more than 20 Mammoth Interactive courses - online tutorials about the web, app, and game development. Python deep learning tutorial is for you if you want to learn the concept of machine learning with practical tasks using Keras, Python, and PyCharm. Any person who understands that technologies shape the way of communication should enroll in this deep learning tutorial for beginners as well. Trust me, after completing this course, new possibilities will open up, as you'll get a new set of skills on new technologies, which are the skills that many employers are looking for. So don't wait up and enroll in this Python machine learning tutorial right away!


Artificial Neural Networks (ANN) with Keras in Python and R

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Artificial Neural Networks (ANN) with Keras in Python and R, Understand Deep Learning and build Neural Networks using TensorFlow 2.0 and Keras in Python and R Created by Start-Tech AcademyPreview this Course - GET COUPON CODE You're looking for a complete Course on Deep Learning using Keras and Tensorflow that teaches you everything you need to create a Neural Network model in Python and R, right? You've found the right Neural Networks course! After completing this course you will be able to: Identify the business problem which can be solved using Neural network Models. Have a clear understanding of Advanced Neural network concepts such as Gradient Descent, forward and Backward Propagation etc. Create Neural network models in Python and R using Keras and Tensorflow libraries and analyze their results. Confidently practice, discuss and understand Deep Learning concepts How this course will help you?