UNIT III
RNN
------------------------------------------------------------------------------------------------------------------------
1. Handling Branches2. Layers
3. Nodes
4. Essential Elements
5. Vanilla RNNs
6. GRUs
Showing posts with label RNN. Show all posts
Showing posts with label RNN. Show all posts
Deep Learning: UNIT 3 : RNN : Short Answer Questions
UNIT 3
RNN
Short
Answer Questions
1. List
the different types of Recurrent Neural Networks.
2. List
the different variants in of RNNs with RNN Nodes.
3. What
is the purpose of RNN?
4. List
the different applications of RNNs.
5. Define
RNN Layer.
6. Define
RNN Node.
7. List
the essential elements of RNN.
8. How
can you handle branches in RNN?
Deep Learning: UNIT- 3: RNN: Long Answer Questions
UNIT 3
RNN
Long
Answer Questions
1. Explain
RNNLayer with neat diagram.
2. Explain
RNNNode with example.
3. What
is the drawback of Artificial Neural Network? How can you overcome that?
Explain.
4. Explain
how RNNs handle sequential data.
5. Explain
the basic three structures of RNN
6.
What is the role of the hidden state in an
RNN?
7. What is a Recurrent Neural Network (RNN) and how does it
differ from a feedforward neural network?
8. Draw
and explain the architecture of Recurrent Neural Networks.
9. Draw
and explain Schematic diagram of a recurrent neural network?
10. List
the different types of Recurrent Neural Networks. Explain them with example.
11. Explain
the essential elements of RNNNodes.
Deep Learning: UNIT 3 (A): RNN: Notes
UNIT III (A)
RNN
--------------------------------------------------------------------------------------------------------------------
1. Handling
Branches
2. Layers
3. Nodes
4. Essential Elements
Deep Learning: UNIT 3 (A) : RNN : PPTs
UNIT III (A)
RNN
1. Handling
Branches
2. Layers
3. Nodes
4. Essential
Elements
About Deep Learning
Deep Learning
UNIT I
Deep Learning: Fundamentals, Introduction, Building Block of Neural
Networks, Layers, MLPs, Forward pass, backward pass, class, trainer
and optimizer, The Vanishing and Exploding Gradient
Problems, Difficulties in Convergence, Local and Spurious Optima,
Preprocessing, Momentum, learning
rate Decay, Weight Initialization, Regularization, Dropout, SoftMax, Cross
Entropy loss function, Activation
Functions.
👉Deep Learning: UNIT 1 (A) Notes: Deep Learning: Fundamentals Part 1 Notes
👉Deep Learning: UNIT 1 (A) PPTs: Deep Learning Fundamentals Part 1 PPTs
👉Deep Learning: Unit 1 (B) Notes: Deep Learning Fundamentals Part 2 Notes
👉Deep Learning: UNIT 1 (B): Deep Learning: Fundamentals Part2 PPTs
👉Deep Learning: UNIT 1: Deep Learning - Fundamentals: Long Answer Questions
👉Deep Learning: UNIT 1: Deep Learning - Fundamentals : Short Answer Questions
UNIT II
CNN: Introduction, striding
and padding, pooling
layers, structure, operations and prediction of CNN with layers, CNN -Case study
with MNIST, CNN VS
Fully Connected.
👉Deep Learning: UNIT-2: CNN PPTs
👉Deep Learning: UNIT-2: CNN Notes
👉Deep Learning: UNIT 2: CNN : Long Answer Questions
👉Deep Learning: UNIT 2: CNN: Short Answer Questions
UNIT III
RNN: Handling Branches, Layers, Nodes, Essential Elements-Vanilla RNNs, GRUs, LSTM
👉Deep Learning: UNIT 3 (A): RNN: Notes
👉Deep Learning: UNIT 3 (A) : RNN : PPTs
👉Deep Learning: UNIT- 3: RNN: Long Answer Questions
👉Deep Learning: UNIT 3 : RNN : Short Answer Questions
UNIT IV
Autoencoders: Denoising Autoencoders, Sparse Autoencoders, Deep Autoencoders, Variational Autoencoders, GANS
👉Deep Learning: UNIT 4: Autoencoders PPTs
👉Deep Learning: UNIT 4: Autoencoders NOTEs
UNIT V
Transfer Learning- Types, Methodologies, Diving into Transfer Learning, Challenges
👉Deep Learning : UNIT 5: Transfer Learning Notes
👉Deep Learning : UNIT 5: Transfer Learning PPTs
Text Books:
1. Seth Weidman, “Deep Learning from Scratch”, O'Reilly
Media, Inc., 2019
2. Ian Goodfellow, Yoshua Bengio and Aaron Courville, “Deep Learning” , MIT Press, 2015
3. Dipanjan Sarkar, Raghav Bali, “Transfer Learning in Action”, Manning Publications, 2021
Reference Books:
1. Giancarlo Zaccone,
Md. Rezaul Karim,
Ahmed Menshawy "Deep Learning with TensorFlow: Explore neural networks with Python", Packt Publisher, 2017.
2. Antonio Gulli,
Sujit Pal, "Deep Learning with Keras", Packt Publishers, 2017.
3. Francois Chollet,
"Deep Learning with Python",
Manning Publications, 2017.
Deep Learning
Deep Learning
- 👉Deep Learning: Fundamentals
- 👉CNN
- 👉RNN
- 👉Autoencoders
- 👉Transfer Learning
UNIT I
Deep Learning: Fundamentals, Introduction, Building Block of Neural Networks, Layers, MLPs, Forward pass, backward pass, class, trainer and optimizer, The Vanishing and Exploding Gradient Problems, Difficulties in Convergence, Local and Spurious Optima, Preprocessing, Momentum, learning rate Decay, Weight Initialization, Regularization, Dropout, SoftMax, Cross Entropy loss function, Activation Functions.
UNIT II
CNN: Introduction, striding and padding, pooling layers, structure, operations and prediction of CNN with layers, CNN -Case study with MNIST, CNN VS Fully Connected
UNIT III
RNN: Handling Branches, Layers, Nodes, Essential Elements-Vanilla RNNs, GRUs, LSTM
UNIT IV
Autoencoders: Denoising Autoencoders, Sparse Autoencoders, Deep Autoencoders, Variational Autoencoders, GANS
UNIT V
Transfer Learning- Types, Methodologies, Diving into Transfer Learning, Challenges
Text Books:
1. Seth Weidman, “Deep Learning from Scratch”, O'Reilly
Media, Inc., 2019
2. Ian Goodfellow, Yoshua Bengio and Aaron Courville, “Deep Learning” , MIT Press, 2015
3. Dipanjan Sarkar, Raghav Bali, “Transfer Learning in Action”,
Manning Publications, 2021
Reference Books:
1. Giancarlo Zaccone,
Md. Rezaul Karim,
Ahmed Menshawy "Deep Learning with TensorFlow: Explore neural networks with Python", Packt Publisher, 2017.
2. Antonio Gulli,
Sujit Pal, "Deep Learning with Keras", Packt Publishers, 2017.
3. Francois Chollet,
"Deep Learning with Python",
Manning Publications, 2017.
MCQs👇
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