A tutorial on the sigmoid function, its properties, and its use as an activation function in neural networks to learn non-linear decision boundaries.

Deep Learning: Recurrent Neural Networks in Python, GRU, LSTM, more modern deep learning, machine learning, and data science for sequences Created by Lazy Programmer Inc. English [Auto], Indonesian [Auto], 5 more Preview this Course - GET COUPON CODE Description Like the course I just released on Hidden Markov Models, Recurrent Neural Networks are all about learning sequences - but whereas Markov Models are limited by the Markov assumption, Recurrent Neural Networks are not - and as a result, they are more expressive, and more powerful than anything we've seen on tasks that we haven't made progress on in decades. So what's going to be in this course and how will it build on the previous neural network courses and Hidden Markov Models? In the first section of the course we are going to add the concept of time to our neural networks. I'll introduce you to the Simple Recurrent Unit, also known as the Elman unit. We are going to revisit the XOR problem, but we're going to extend it so that it becomes the parity problem - you'll see that regular feedforward neural networks will have trouble solving this problem but recurrent networks will work because the key is to treat the input as a sequence.

This document focuses on modeling a complex situations to achieve an advantage within a competitive context. Our goal is to devise the characteristics of games to teach and exercise non-easily quantifiable tasks crucial to the math-modeling process. A computerized game to exercise the math-modeling process and optimization problem formulation is introduced. The game is named The Formula 1 Championship, and models of the game were developed in the computerized simulation platform MoNet. It resembles some situations in which team managers must make crucial decisions to enhance their racing cars up to the feasible, most advantageous conditions. This paper describes the game's rules, limitations, and five Formula 1 circuit simulators used for the championship development. We present several formulations of this situation in the form of optimization problems. Administering the budget to reach the best car adjustment to a set of circuits to win the respective races can be an approach. Focusing on the best distribution of each Grand Prix's budget and then deciding how to use the assigned money to improve the car is also the right approach. In general, there may be a degree of conflict among these approaches because they are different aspects of the same multi-scale optimization problem. Therefore, we evaluate the impact of assigning the highest priority to an element, or another, when formulating the optimization problem. Studying the effectiveness of solving such optimization problems turns out to be an exciting way of evaluating the advantages of focusing on one scale or another. Another thread of this research directs to the meaning of the game in the teaching-learning process. We believe applying the Formula 1 Game is an effective way to discover opportunities in a complex-system situation and formulate them to finally extract and concrete the related benefit to the context described.

Deep Learning: Convolutional Neural Networks in Python, Computer Vision and Data Science and Machine Learning combined! In Theano and TensorFlow Created by Lazy Programmer Inc. Preview this Course  - GET COUPON CODE 100% Off Udemy Coupon . Free Udemy Courses . Online Classes

This course is in an "early bird" release, and we're still updating and adding content to it, please keep in mind before enrolling that the course is not yet complete. "The future is already here – it's just not very evenly distributed." Have you ever wondered how Artificial Intelligence actually works? Do you want to be able to harness the power of neural networks and reinforcement learning to create intelligent agents that can solve tasks with human level complexity? This is the ultimate course online for learning how to use Python to harness the power of Neural Networks to create Artificially Intelligent agents! This course focuses on a practical approach that puts you in the driver's seat to actually build and create intelligent agents, instead of just showing you small toy examples like many other online courses.

Udemy Coupon - A Complete Guide on TensorFlow 2.0 using Keras API, Build Amazing Applications of Deep Learning and Artificial Intelligence in TensorFlow 2.0 Created by Hadelin de Ponteves, Kirill Eremenko, SuperDataScience Team, Luka Anicin  English [Auto-generated] Students also bought Complete Guide to TensorFlow for Deep Learning with Python Tensorflow 2.0: Deep Learning and Artificial Intelligence Complete Tensorflow 2 and Keras Deep Learning Bootcamp Modern Deep Learning in Python TensorFlow 2.0 Practical Preview this Course GET COUPON CODE 100% Off Udemy Coupon . Free Udemy Courses . Online Classes

Artificial neural networks (ANNs), also known as neural networks (NNs), are computer systems that are modelled after the biological neural networks that make up animal brains. In this course,we will learn to create our own neural networks with python. Introduction to artificial neural network: Artificial neural networks simulates the functioning of human brain .This section,we will learn the basics of artificial neural network.We will also learn various types of neural network.,techniques of neural networks. Tasks associated with neural network with examples,feed forward and feed back neural networks and more…. Python basics: Python is the language widely used for development.We can use python in desktop,web and ML development.

Deep Learning A-Z™: Hands-On Artificial Neural Networks Free Coupon Discount - Learn to create Deep Learning Algorithms in Python from two Machine Learning & Data Science experts. Templates included. BESTSELLER 4.6 (25,470 ratings) Created by Kirill Eremenko, Hadelin de Ponteves, SuperDataScience Team  English, French [Auto-generated], 4 more Preview this Udemy Course - GET COUPON CODE 100% Off Udemy Coupon . Free Udemy Courses . Online Classes

Created by Lazy Programmer Inc. English [Auto-generated], German [Auto-generated], 3 more Created by Lazy Programmer Inc. In this course you will build MULTIPLE practical systems using natural language processing, or NLP - the branch of machine learning and data science that deals with text and speech. This course is not part of my deep learning series, so it doesn't contain any hard math - just straight up coding in Python. All the materials for this course are FREE. After a brief discussion about what NLP is and what it can do, we will begin building very useful stuff.

Artificial intelligence (AI) is playing an increasingly important part in our daily lives, whether it is providing our personalised social media feeds, online shopping or streaming movie suggestions, or even the mapping apps that route us around traffic jams. On a bigger scale, AI is already having a major impact on healthcare, finance, farming and many other sectors and its influence is predicted to expand rapidly in the coming years. One area where there is considerable untapped potential for AI is in the field of Earth observation, where it can be used to help manage large datasets, find new insights in data and generate new products and services. With this in mind, EUMETSAT, ECMWF, Mercator Ocean International and the EEA have joined up to develop a new massive open online course (MOOC) on AI and Earth monitoring. The idea for the course is to introduce participants to the wealth of Copernicus Earth observation data and the AI and machine learning techniques that can be used to work with it.