Deep Learning AL503(B) Unit 5 || AIML 5th Semester || RGPV || #rgpv #deeplearning #codes_with_duo

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In This Video We Cover All The Topics is Related to Deep Learning Unit 1 AL-503(B) ||

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UNIT 1 :- https://youtu.be/S8bF6FxMCtE?si=TPj9XL25Jm-J6V7U
UNIT 2 :- https://youtu.be/eq_p4-1mRJA?si=YJ3EEZYoGeUkfoH3
UNIT 3 :- https://youtu.be/wE3CTfzwxVg?si=NS1npsXiVV9QGuyy
UNIT 4 :- https://youtu.be/CR03c1ECHUo?si=3JoujX3-elwCrnOO

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TOPICS COVERED IN THIS ONE SHOT :-
1. Introduction to Deep Generative Models
2. Restricted Boltzmann Machines (RBMs),
3. Gibbs Sampling for training RBMs
4. Deep belief networks, Markov Networks, Markov
Chains
5. Auto-regressive Models: NADE, MADE, PixelRNN
6. Generative Adversarial Networks (GANs)
7. Applications of Deep Learning in Object detection, speech/ image
recognition, video analysis, NLP, medical science etc.
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#ai #machinelearning #computervision #naturallanguageprocessing #artificialintelligence #normalization

Unit I:Introduction History of Deep Learning, McCulloch Pitts Neuron, Multilayer
Perceptions (MLPs), Representation Power of MLPs, Sigmoid Neurons, Feed Forward
Neural Networks, Back propagation, weight initialization methods, Batch Normalization,
Representation Learning, GPU implementation, Decomposition – PCA and SVD.

Unit II:Deep Feedforward Neural Networks, Gradient Descent (GD), Momentum Based GD,
Nesterov Accelerated GD, Stochastic GD, AdaGrad, Adam, RMSProp, Auto-encoder,
Regularization in auto-encoders, Denoising auto-encoders, Sparse auto-encoders, Contractive
auto-encoders,Variational auto-encoder, Auto-encoders relationship with PCA and SVD,
Dataset augmentation.Denoising auto encoders,
Unit III:Introduction to Convolutional neural Networks (CNN) and its architectures, CCN
terminologies: ReLu activation function, Stride, padding, pooling, convolutions operations,
Convolutional kernels, types of layers: Convolutional, pooling, fully connected, Visualizing
CNN, CNN examples: LeNet, AlexNet, ZF-Net, VGGNet, GoogLeNet, ResNet, RCNNetc.
Deep Dream, Deep Art. Regularization: Dropout, drop Connect, unit pruning, stochastic
pooling, artificial data, injecting noise in input, early stopping, Limit Number of parameters,
Weight decay etc.
Unit IV:Introduction to Deep Recurrent Neural Networks and its architectures,
Backpropagation Through Time (BPTT), Vanishing and Exploding Gradients, Truncated
BPTT, Gated Recurrent Units (GRUs), Long Short Term Memory (LSTM), Solving the
vanishing gradient problem with LSTMs, Encoding and decoding in RNN network, Attention
Mechanism, Attention over images, Hierarchical Attention, Directed Graphical Models.
Applications of Deep RNN in Image Processing, Natural Language Processing, Speech
recognition, Video Analytics.
Unit V:Introduction to Deep Generative Models, Restricted Boltzmann Machines (RBMs),
Gibbs Sampling for training RBMs, Deep belief networks, Markov Networks, Markov
Chains, Auto-regressive Models: NADE, MADE, PixelRNN, Generative Adversarial
Networks (GANs), Applications of Deep Learning in Object detection, speech/ image
recognition, video analysis, NLP, medical science etc.

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