It is difficult, perhaps, to link this to neural networks, but the basic intuition of calculus is achieved. If you are looking for a more full treatment of this branch of mathematics, you will want to seek out some more robust learning tools. Here are 3 courses and a textbook to help out, all from MIT's Open Courseware initiative, which will cover everything you need to know about calculus to understand deep learning -- and far beyond.

Object tracking is a subarea of Computer Vision which aims to locate an object in successive frames of a video. An example of application is a video surveillance and security system, in which suspicious actions can be detected. Other examples are the monitoring of traffic on highways and also the analysis of the movement of players in a soccer match! In this last example, it is possible to trace the complete route that the player followed during the match. To take you to this area, in this course you will learn the main object tracking algorithms using the Python language and the OpenCV library!

Computer Vision Masterclass, Learn in practice everything you need to know about Computer Vision! Build projects step by step using Python! Understand the basic intuition about Cascade and HOG classifiers to detect faces Implement face detection using OpenCV and Dlib library Learn how to detect other objects using OpenCV, such as cars, clocks, eyes, and full body of people Compare the results of three face detectors: Haarcascade, HOG (Histogram of Oriented Gradients) and CNN (Convolutional Neural Networks) Detect faces using images and the webcam Understand the basic intuition about LBPH algorithm to recognize faces Implement face recognition using OpenCV and Dlib library Recognize faces using images and the webcam Understand the basic intuition about KCF and CSRT algorithms to perform object tracking Learn how to track objects in videos using OpenCV library Learn everything you need to know about the theory behind neural networks, such as: perceptron, activation functions, weight update, backpropagation, gradient descent and a lot more Implement dense neural networks to classify images Learn how to extract pixels and features from images in order to build neural networks Learn the theory behind convolutional neural networks and implement them using Python and TensorFlow Implement transfer learning and fine tuning to get incredible results when classifying images Use convolutional neural networks to classify the following emotions in images and videos: happy, anger, disgust, fear, surprise and neutral Compress images using linear and convolutional autoencoders Detect objects in images in videos using YOLO, one of the most powerful algorithms today Recognize gestures and actions in videos using OpenCV Learn how to create hallucinogenic images with Deep Dream Learn how to revive famous artists with style transfer Create images that don't exist in the real world with GANs (Generative Adversarial Networks) Implement image segmentation do extract useful information from images and videos Create images that don't exist in the real world with GANs (Generative Adversarial Networks) Computer Vision is a subarea of Artificial Intelligence focused on creating systems that can process, analyze and identify visual data in a similar way to the human eye. There are many commercial applications in various departments, such as: security, marketing, decision making and production. Smartphones use Computer Vision to unlock devices using face recognition, self-driving cars use it to detect pedestrians and keep a safe distance from other cars, as well as security cameras use it to identify whether there are people in the environment for the alarm to be triggered. In this course you will learn everything you need to know in order to get in this world.

The discriminator connects to two loss functions. During discriminator training, the discriminator ignores the generator loss and just uses the discriminator loss. We use the generator loss during generator training. The discriminator's training data comes from two sources: In the given figure above, the two "Sample" boxes represent these two data sources feeding into the discriminator. During discriminator training the generator does not train. Its weights remains fixed while it produces examples for the discriminator to train on.

Whenever we come across Machine learning models involving classification of image data or have to deal with vectors of a complex dimensionality, computation becomes a barrier in getting timely results. It is therefore intuitive to use algorithms that reduce computation complexity involving vectors and help in getting timely and better results. And we will talk about one such technique of classification that uses dimensionality reduction - Linear Discriminant Analysis or LDA. Suppose we are working on a dataset containing images. Our aim is to train a model that classifies the images into two or more categories.

It is difficult, perhaps, to link this to neural networks, but the basic intuition of calculus is achieved. If you are looking for a more full treatment of this branch of mathematics, you will want to seek out some more robust learning tools. Here are 3 courses and a textbook to help out, all from MIT's Open Courseware initiative, which will cover everything you need to know about calculus to understand deep learning -- and far beyond.