CS702(B) Unit 3 CNN Architectures and Deep Learning Trends study material for RGPV CSE 7th Semester. Learn Greedy Layerwise Pre-training, activation functions, weight initialization, word representations, CNN, LeNet, AlexNet, ZF-Net, VGGNet, GoogLeNet, ResNet, Guided Backpropagation, Deep Dream and Deep Art.
Unit 3 focuses on modern deep learning architectures, especially Convolutional Neural Networks. It explains CNN evolution from LeNet to ResNet, better activation functions, better weight initialization, word representations and visualization techniques like Guided Backpropagation, Deep Dream and Deep Art.
Study pre-training, activation functions, weight initialization and word representations.
Learn LeNet, AlexNet, ZF-Net, VGGNet, GoogLeNet and ResNet.
Understand Guided Backpropagation, Deep Dream, Deep Art and recent DL trends.
Complete syllabus-based topics of Deep & Reinforcement Learning Unit 3.
Greedy layerwise pre-training trains deep networks one layer at a time before fine-tuning the complete model.
Improved activation functions like ReLU, Leaky ReLU and ELU help deep networks learn faster and reduce gradient problems.
Good weight initialization helps neural networks converge faster and prevents vanishing or exploding gradients.
Word representations convert words into numerical vectors so that neural networks can understand semantic relationships.
CNNs are deep learning models mainly used for image processing, feature extraction and computer vision tasks.
The convolution layer applies filters to input images to detect features like edges, corners and patterns.
Pooling reduces feature map size and helps make CNNs more efficient and less sensitive to small changes.
LeNet is one of the earliest CNN architectures, mainly used for handwritten digit recognition.
AlexNet popularized deep CNNs by achieving major success in image classification using ReLU, dropout and GPUs.
ZF-Net improved AlexNet by tuning filter sizes and strides for better feature visualization and performance.
VGGNet uses many small 3x3 convolution filters and deep architecture for improved image recognition.
GoogLeNet introduced inception modules to improve computational efficiency and multi-scale feature extraction.
ResNet uses residual connections to train very deep networks and reduce degradation problems.
CNN visualization helps understand what features are learned by different layers of the network.
Guided Backpropagation visualizes important input regions that strongly activate neurons in a CNN.
Deep Dream enhances patterns learned by neural networks to visualize what a model has learned.
Deep Art applies neural style transfer to combine content of one image with style of another image.
Recent trends include transformers, attention-based models, self-supervised learning and large-scale pretrained models.
CNN: Image data ke liye feature extraction based deep learning model.
LeNet: Early CNN for digit recognition.
AlexNet: Deep CNN breakthrough using ReLU, dropout and GPU training.
VGGNet: Deep network with small 3x3 filters.
GoogLeNet: Inception module based CNN.
ResNet: Residual connections se very deep networks train karta hai.
| Topic | Expected Frequency | Importance |
|---|---|---|
| CNN Architecture | Very High | ⭐⭐⭐⭐⭐ |
| Convolution & Pooling Layer | Very High | ⭐⭐⭐⭐⭐ |
| LeNet | High | ⭐⭐⭐⭐ |
| AlexNet | Very High | ⭐⭐⭐⭐⭐ |
| VGGNet | Very High | ⭐⭐⭐⭐⭐ |
| GoogLeNet | High | ⭐⭐⭐⭐ |
| ResNet | Very High | ⭐⭐⭐⭐⭐ |
| Guided Backpropagation | Medium | ⭐⭐⭐ |
| Deep Dream & Deep Art | Medium | ⭐⭐⭐ |
| Recent Deep Learning Trends | High | ⭐⭐⭐⭐ |
CNN is a deep learning model used mainly for image recognition, feature extraction and computer vision tasks.
LeNet is an early CNN architecture used for handwritten digit recognition.
AlexNet is a deep CNN architecture that became popular for image classification using ReLU, dropout and GPU training.
ResNet is a CNN architecture that uses residual connections to train very deep networks.
Deep Dream is a visualization technique that enhances patterns learned by a neural network.