What you’ll learn
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Understand the theory behind principal components analysis (PCA)
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Know why PCA is useful for dimensionality reduction, visualization, de-correlation, and denoising
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Derive the PCA algorithm by hand
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Write the code for PCA
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Understand the theory behind t-SNE
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Use t-SNE in code
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Understand the limitations of PCA and t-SNE
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Understand the theory behind autoencoders
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Write an autoencoder in Theano and Tensorflow
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Understand how stacked autoencoders are used in deep learning
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Write a stacked denoising autoencoder in Theano and Tensorflow
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Understand the theory behind restricted Boltzmann machines (RBMs)
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Understand why RBMs are hard to train
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Understand the contrastive divergence algorithm to train RBMs
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Write your own RBM and deep belief network (DBN) in Theano and Tensorflow
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Visualize and interpret the features learned by autoencoders and RBMs
Who this course is for:
- Students and professionals looking to enhance their deep learning repertoire
- Students and professionals who want to improve the training capabilities of deep neural networks
- Students and professionals who want to learn about the more modern developments in deep learning
Can I download Unsupervised Deep Learning in Python course?
You can download videos for offline viewing in the Android/iOS app. When course instructors enable the downloading feature for lectures of the course, then it can be downloaded for offline viewing on a desktop.Can I get a certificate after completing the course?
Yes, upon successful completion of the course, learners will get the course e-Certification from the course provider. The Unsupervised Deep Learning in Python course certification is a proof that you completed and passed the course. You can download it, attach it to your resume, share it through social media.Are there any other coupons available for this course?
You can check out for more Udemy coupons @ www.coursecouponclub.com
Note: 100% OFF Udemy coupon codes are valid for maximum 3 days only. Look for "ENROLL NOW" button at the end of the post.
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Disclosure: This post may contain affiliate links and we may get small commission if you make a purchase. Read more about Affiliate disclosure here.
