🔊 Deep Finding out NPTEL Elective Course July 2022
🔴ABOUT THE System :
Deep Learning has obtained a large amount of consideration more than the previous couple of decades and has been used correctly by providers like Google, Microsoft, IBM, Facebook, Twitter and so on. to remedy a extensive selection of difficulties in Pc Vision and Natural Language Processing. In this study course we will learn about the making blocks utilised in these Deep Learning centered solutions. Exclusively, we will discover about feedforward neural networks, convolutional neural networks, recurrent neural networks and interest mechanisms. We will also appear at many optimization algorithms such as Gradient Descent, Nesterov Accelerated Gradient Descent, Adam, AdaGrad and RMSProp which are utilised for coaching these deep neural networks. At the end of this training course students would have understanding of deep architectures used for solving different Eyesight and NLP jobs.
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✨COURSE LAYOUT✨
Week 1 : (Partial) History of Deep Finding out, Deep Mastering Achievement Tales, McCulloch Pitts Neuron, Thresholding Logic, Perceptrons, Perceptron Understanding Algorithm
Week 2 : Multilayer Perceptrons (MLPs), Representation Energy of MLPs, Sigmoid Neurons, Gradient Descent, Feedforward Neural Networks, Illustration Power of Feedforward Neural Networks
7 days 3 : FeedForward Neural Networks, Backpropagation
Week 4 : Gradient Descent (GD), Momentum Centered GD, Nesterov Accelerated GD, Stochastic GD, AdaGrad, RMSProp, Adam, Eigenvalues and eigenvectors, Eigenvalue Decomposition, Foundation
7 days 5 : Principal Component Evaluation and its interpretations, Singular Value Decomposition
Week 6 : Autoencoders and relation to PCA, Regularization in autoencoders, Denoising autoencoders, Sparse autoencoders, Contractive autoencoders
Week 7 : Regularization: Bias Variance Tradeoff, L2 regularization, Early stopping, Dataset augmentation, Parameter sharing and tying, Injecting noise at enter, Ensemble solutions, Dropout
7 days 8 : Greedy Layerwise Pre-schooling, Much better activation functions, Improved bodyweight initialization procedures, Batch Normalization
Week 9 : Learning Vectorial Representations Of Words
Week 10: Convolutional Neural Networks, LeNet, AlexNet, ZF-Internet, VGGNet, GoogLeNet, ResNet, Visualizing Convolutional Neural Networks, Guided Backpropagation, Deep Dream, Deep Artwork, Fooling Convolutional Neural Networks
7 days 11: Recurrent Neural Networks, Backpropagation as a result of time (BPTT), Vanishing and Exploding Gradients, Truncated BPTT, GRU, LSTMs
Week 12: Encoder Decoder Products, Consideration System, Focus around photos
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⚠️Note: We do not assert 💯% precision of provided answers. These answers are primarily based on our sole information. We are posting these solution just for your reference, so we request our student’s local community to do your assignment on your own and verify it.
➡️Kindly take note, if any adjustments are manufactured in the remedy, will be notified in comment area.
➡️If you have any question in the solution make sure you place in remark area, we will try our finest to explain it.
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✨ Subjects Coated ✨
Deep Learning
Deep Understanding week-6 assignment solutions
Nptel Deep Finding out week 6 quiz assignment solutions
Nptel Deep Learning week 6 answers
Deep Mastering 7 days 6
Deep Studying 7 days 6 Assignment Solutions
Deep Learning 7 days-6 assignment responses
Deep Learning 7 days 6 nptel
Nptel Deep Finding out assignment responses 2022
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✨ Tags Used in this movie ✨
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