Podcast
Questions and Answers
Which optimization technique adjusts the learning rate for each parameter individually, offering improved performance over standard gradient descent?
Which optimization technique adjusts the learning rate for each parameter individually, offering improved performance over standard gradient descent?
- Momentum Optimizer
- Batch Optimization
- RMSProp (correct)
- Gradient Descent
In the context of deep learning, what is the role of 'vectorization'?
In the context of deep learning, what is the role of 'vectorization'?
- Optimizing code to use parallel processing, especially on GPUs, for faster computation. (correct)
- Converting images into a black and white format.
- A method of reducing the number of dimensions in the input data.
- The process of converting sequential data into a matrix format.
What is the primary difference between a fully connected network and a convolutional neural network (CNN) in image processing?
What is the primary difference between a fully connected network and a convolutional neural network (CNN) in image processing?
- A fully connected network can process images of any size, while a CNN is limited to fixed-size images.
- CNNs use backpropagation, while fully connected networks use forward propagation.
- A CNN is specifically designed to exploit spatial hierarchies in data through convolution layers, whereas a fully connected network treats all inputs equally. (correct)
- A fully connected network uses pooling layers which CNNs do not.
What purpose does 'pooling' serve within a convolutional neural network (CNN)?
What purpose does 'pooling' serve within a convolutional neural network (CNN)?
What is the key motivation behind using 'transfer learning' in deep learning?
What is the key motivation behind using 'transfer learning' in deep learning?
In transfer learning, what does 'fine-tuning' typically involve?
In transfer learning, what does 'fine-tuning' typically involve?
Which technique directly addresses overfitting by randomly omitting some neuron outputs during the training process??
Which technique directly addresses overfitting by randomly omitting some neuron outputs during the training process??
How does 'early stopping' prevent overfitting in neural networks?
How does 'early stopping' prevent overfitting in neural networks?
Which type of data are Recurrent Neural Networks (RNNs) specifically designed to process?
Which type of data are Recurrent Neural Networks (RNNs) specifically designed to process?
Which of the following is a common application of RNNs in text processing?
Which of the following is a common application of RNNs in text processing?
What is the purpose of using LSTM or GRU units in recurrent neural networks?
What is the purpose of using LSTM or GRU units in recurrent neural networks?
What is the primary function of a Generative Adversarial Network (GAN)?
What is the primary function of a Generative Adversarial Network (GAN)?
In the context of Generative Deep Learning, what is 'image denoising'?
In the context of Generative Deep Learning, what is 'image denoising'?
Which type of deep learning task involves assigning a label to each pixel in an image?
Which type of deep learning task involves assigning a label to each pixel in an image?
What is the primary goal of 'object detection' in the context of deep learning and computer vision?
What is the primary goal of 'object detection' in the context of deep learning and computer vision?
What is the function of the 'filters' in Convolutional Neural Networks (CNNs)?
What is the function of the 'filters' in Convolutional Neural Networks (CNNs)?
Which of the following techniques helps in dealing with the vanishing gradient problem during the training of very deep neural networks?
Which of the following techniques helps in dealing with the vanishing gradient problem during the training of very deep neural networks?
In deep learning models, what is the role of 'Batch Normalization'?
In deep learning models, what is the role of 'Batch Normalization'?
Bias-variance tradeoff is a central problem in machine learning. What does a high variance typically indicate about a model?
Bias-variance tradeoff is a central problem in machine learning. What does a high variance typically indicate about a model?
Which of the following is NOT a common data augmentation technique used to improve the generalization of image recognition models?
Which of the following is NOT a common data augmentation technique used to improve the generalization of image recognition models?
Flashcards
Deep Learning
Deep Learning
A type of machine learning where models learn hierarchical representations of data, often involving multiple layers of neural networks.
Deep Learning Models
Deep Learning Models
Models implemented in Python using libraries like Keras/PyTorch and trained on real-world datasets.
Convolutional Neural Network (CNN)
Convolutional Neural Network (CNN)
A neural network specialized for processing data that has a grid-like topology, such as images.
Regularization, Training Optimization, Hyperparameter Selection
Regularization, Training Optimization, Hyperparameter Selection
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Recurrent Neural Network (RNN)
Recurrent Neural Network (RNN)
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Generative Deep Learning
Generative Deep Learning
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Artificial Neural Network
Artificial Neural Network
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Multilayer Perceptron (MLP)
Multilayer Perceptron (MLP)
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Forward Propagation
Forward Propagation
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Back Propagation Learning
Back Propagation Learning
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Optimization Techniques
Optimization Techniques
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Gradient Descent
Gradient Descent
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Batch Optimization
Batch Optimization
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Transfer Learning
Transfer Learning
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Hyperparameter tuning
Hyperparameter tuning
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Dropout
Dropout
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Sequential Models
Sequential Models
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Generative Adversarial Network
Generative Adversarial Network
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Study Notes
- BTAIC601 is a Deep Learning course with PCC7, 3 lecture hours, 1 tutorial hour, and 4 credits
- Examination includes continuous assessment (20 marks), mid-semester exam (20 marks), and end-semester exam (60 marks, 3-hour duration)
- Course pre-requisites include basic Machine Learning, Soft Computing, Data Structures, and Python knowledge
Course Objectives
- Attendees will understand neural networks and deep learning.
- Attendees will have working knowledge of neural networks and deep learning.
- Attendees will explore parameters for neural networks.
- Attendees will use CNN and RNN to solve real-world problems.
Course Outcomes
- Students will implement deep learning models in Python using Keras/PyTorch with real-world datasets (CO1).
- Students will design convolution networks for image classification (CO2).
- Students will perform regularization, training optimization, and hyperparameter selection on deep models (CO3).
- Students will design Recurrent Neural Networks for text and sequence classification (CO4).
- Students will apply Generative Deep Learning for generating images (CO5).
Unit 1: Introduction to Neural Network (8 Hours)
- Topics include Simple Artificial Neural Networks, Multilayer Perceptrons, Forward Propagation, Back Propagation Learning
- Other topics are Building Blocks of Deep Neural Networks, Optimization Techniques, Gradient Descent and variants, Batch Optimization
- Momentum Optimizer, RMSProp, Adam, Vectorization, Linear/Logistic Regression with Deep Neural Networks are also covered
Unit 2: Convolutional Neural Network (7 Hours)
- Introduction to CNNs, distinguishing them from Fully Connected Networks.
- Building blocks of CNNs include Filters, Convolution, Pooling and Activations.
- Topics also include CNN training procedures, feeding images and videos, CNN architectures, Residual Networks, and Skip Connections.
Unit 3: Transfer Learning and Effective training in Deep Net (7 Hours)
- Transfer learning is introduced, including the need for it, feature extraction, and fine-tuning
- Focus on bias-variance tradeoff and techniques for managing overfitting and underfitting
- Regularization, Data Augmentation, Early Stopping, Dropout, and Batch/Instance/Group Normalization are key topics
Unit 4: Deep Learning for Text and Sequences (7 Hours)
- Introduces Sequential/Temporal Data and Sequential Models like Recurrent Neural Networks (RNNs)
- Covers data representation using RNNs and working with text data.
- Text generation using LSTM, LSTM/GRU, and Transformer Networks are discussed
Unit 5: Generative Deep Learning (7 Hours)
- Neural Style Transfer, Variational Autoencoders, and Generative Adversarial Networks are key concepts.
- Classical Supervised Tasks with Deep Learning, Image Denoising, Semantic Segmentation, and Object Detection are covered
Text Books
- One is "Deep Learning with Python" by Francois Challot, 2nd edition
- The other is "Deep Learning with Pytorch" by Francois Challot, 2nd edition
Reference Books
- "Neural Networks and Deep Learning" by Michael Nielsen, 2016
- "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville (The MIT Press)
- "Pattern Classification" by Richard O. Duda, Peter E. Hart, David G. Stork, John Wiley & Sons Inc.
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