
Discover how artificial neural networks mimic biological neurons to classify patterns in massive data sets and enable tasks like language translation, face recognition, and drug discovery.
Neural networks learn by finding patterns in massive data with machine learning, enabling image classification and language translation. Emphasize data quality, empirical experiments, and tuning algorithms.
Learn how a neural network uses weights, activation, and bias to turn binary inputs into a confidence score via the sigmoid function.
Explore how artificial neural networks classify images by converting pixels into neural activations across input, hidden, and output layers, learning patterns through weights and activations.
Explore how a neural network processes a dog image from 625 input pixels through hidden layers to an output class, using weights, biases, and activation functions.
Explore how neural networks learn with a cost function, gradient descent, and backpropagation, tuning weights and biases across layers to improve confident, accurate classifications.
Explore how back propagation tunes weights and biases to minimize the cost function in neural networks, using activation levels, batches of training data, and gradient descent.
Explore how neural networks require massive data and learn to classify with supervised learning or discover patterns with unsupervised clustering, while emphasizing data quality and diverse training samples.
Explore how neural networks drive machine learning under artificial intelligence, excel at pattern matching, and learn through backpropagation to classify or cluster data such as images, sounds, text, or video.
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.
By the end of this course you will have a fair understanding of how you can leverage the power of artificial intelligence and how to implement neural network models in your applications. Each concept is backed by a generic and real-world problem, making you independent and able to solve any problem with neural networks. All of the content is demystified by a simple and straightforward approach.
Enroll now and start learning artificial intelligence.