Deep Learning, also known as deep structured learning or hierarchical learning, is a new area of Machine Learning research that is introduced with the objective of moving Machine Learning closer to one of its original goals: Artificial Intelligence.
The [course_title] course covers a basic introduction of Deep learning including applications to machine translation, image recognition, game playing, image generation. Upon completion, you will develop a solid understanding of the motivation for deep learning.
This course does not involve any written exams. Students need to answer 5 assignment questions to complete the course, the answers will be in the form of written work in pdf or word. Students can write the answers in their own time. Each answer needs to be 200 words (1 Page). Once the answers are submitted, the tutor will check and assess the work.
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Course Credit: Massachusetts Institute of Technology
|Introduction to Deep Learning||00:42:00|
|Sequence Modeling with Neural Networks||00:27:00|
|Convolutional Neural Networks||00:35:00|
|Deep Generative Modeling||00:44:00|
|Deep Reinforcement Learning||00:32:00|
|Deep Learning Limitations and New Frontiers||00:31:00|
|Issues in Image Classification||00:17:00|
|Faster ML Development with TensorFlow||00:19:00|
|Deep Learning – A Personal Perspective||00:32:00|
|Beyond Deep Learning: Learning+Reasoning||00:32:00|
|Computer Vision Meets Social Networks||00:34:00|
|Submit Your Assignment||00:00:00|
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