Step into the future of automation. Master Machine Learning, Neural Networks, and LLM integration.
Free-to-audit course provided by Coursera on Coursera. Certificate available (may require financial aid).
Free-to-audit course provided by Coursera on Coursera. Certificate available (may require financial aid).
Free-to-audit course provided by Coursera on Coursera. Certificate available (may require financial aid).
Start learning this course today on undefined.
Free-to-audit course provided by Coursera on Coursera. Certificate available (may require financial aid).
This course explores the organization of synaptic connectivity as the basis of neural computation and learning. Perceptrons and dynamical theories of recurrent networks including amplifiers, attractors, and hybrid computation are covered. Additional topics include backpropagation and Hebbian learning, as well as models of perception, motor control, memory, and neural development.
Free-to-audit course provided by Coursera on Coursera. Certificate available (may require financial aid).
Free-to-audit course provided by Coursera on Coursera. Certificate available (may require financial aid).
This is MIT's introductory course on deep learning methods with applications to computer vision, natural language processing, biology, and more! Students will gain foundational knowledge of deep learning algorithms and get practical experience in building neural networks in TensorFlow. Course concludes with a project proposal competition with feedback from staff and panel of industry sponsors. Prerequisites assume calculus (i.e. taking derivatives) and linear algebra (i.e. matrix multiplication), and we'll try to explain everything else along the way! Experience in Python is helpful but not necessary.
This is a fast-paced introduction to deep learning with an emphasis on developing a practical understanding of how to build models to solve complex problems involving unstructured data. Topics include the basics of deep neural networks and how to set up and train them, convolutional networks to process images and videos, transformers for natural language processing, generative large language models (such as ChatGPT), and text-to-image models (such as Midjourney). Prior familiarity with Python and fundamental machine learning concepts (such as training/validation/testing, overfitting/underfitting, and regularization) is required.
This course is a graduate introduction to natural language processing - the study of human language from a computational perspective. It covers syntactic, semantic and discourse processing models, emphasizing machine learning or corpus-based methods and algorithms. It also covers applications of these methods and models in syntactic parsing, information extraction, statistical machine translation, dialogue systems, and summarization. The subject qualifies as an Artificial Intelligence and Applications concentration subject.
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