Deep Learning with R
Optimize Algorithms and achieve greater levels of accuracy with Deep learning
Deep learning refers to artificial neural networks that are composed of many layers. Deep learning is a powerful set of techniques for finding accurate information from raw data.
This tutorial will teach you how to leverage deep learning to make sense of your raw data by exploring various hidden layers of data. Each section in this course provides a clear and concise introduction of a key topic, one or more example of implementations of these concepts in R, and guidance for additional learning, exploration, and application of the skills learned therein. You will start by understanding the basics of Deep Learning and Artificial neural Networks and move on to exploring advanced ANN’s and RNN’s. You will deep dive into Convolutional Neural Networks and Unsupervised Learning. You will also learn about the applications of Deep Learning in various fields and understand the practical implementations of Scalability, HPC and Feature Engineering.
Starting out at a basic level, users will be learning how to develop and implement Deep Learning algorithms using R in real world scenarios.
Your Instructor
Hi! We are UpDegree, providing high quality interactive course.
We are the team of 100+ instructor over the globe. Adel (Oxford), Anand (Standford), Akash (IITB), Rajib (IITK), Partha (IITM) are few of them.
Updegree courses are different, In each and every course you not only get the videos lectures but you also get quiz,code,assignment etc to test your practical understanding!
Course Curriculum
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StartIntroduction and Outline (7:22)
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StartFundamental Concepts in Deep Learning (4:33)
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StartClassification with Two-Layers Artificial Neural Networks (7:29)
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StartProbabilistic Predictions with Two-Layer ANNs (8:24)
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StartTuning ANNs Hyper-Parameters and Best Practices (5:56)
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StartNeural Network Architectures (5:59)
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StartNeural Network Architectures (Conti (3:28)
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StartOptimization Algorithms and Stochastic Gradient Descent (7:52)
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StartBackpropagation (1:33)
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StartBackpropagation (4:45)
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StartIntroduction to Convolutional Neural Networks (11:57)
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StartCNNs in R (4:28)
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StartClassifying Real-World Images with Pre-Trained Models (4:05)
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StartIntroduction to Long Short-Term Memory (4:22)
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StartRNNs in R (14:49)
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StartUse-Case – Learning How to Spell English Words from Scratch (3:42)
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StartAutoencoders (1:40)
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StartRestricted Boltzmann Machines and Deep Belief Networks (3:00)
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StartReinforcement Learning with ANNs (9:02)
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StartUse-Case – Anomaly Detection through Denoising Autoencoders (7:51)
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StartDeep Learning for Natural Language Processings (19:09)
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StartDeep Learning for Complex Multimodal Tasks (23:20)
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StartOther Important Applications of Deep Learning (3:51)
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StartA Complete Comparison of Every DL Packages in R (5:43)
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StartResearch Directions and Open Questions (30:04)