Deep Learning and Neural Networks
Description
Advanced neural network architectures, deep learning techniques, and frameworks for complex pattern recognition and AI applications.
Learning Objectives
Learners will understand neural network fundamentals and architectures, implement deep learning models using TensorFlow and PyTorch, develop convolutional neural networks for computer vision tasks, build recurrent neural networks for sequence processing, and apply transfer learning and fine-tuning techniques for practical applications.
Topics (8)
Sequential data processing using RNNs, Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRUs), bidirectional RNNs, and applications in natural language processing and time series forecasting.
Foundation concepts of artificial neural networks including perceptrons, multi-layer perceptrons, activation functions, weight initialization, forward propagation, and backpropagation algorithm for gradient-based learning.
Deep dive into CNNs including convolution operations, pooling layers, CNN architectures (LeNet, AlexNet, VGG, ResNet), image preprocessing, and applications in computer vision tasks.
Hands-on experience with major deep learning frameworks including TensorFlow, Keras, PyTorch, model building, training pipelines, model deployment, and production considerations for deep learning systems.
Generative Adversarial Networks including GAN architecture, training dynamics, various GAN variants (DCGAN, StyleGAN, CycleGAN), applications in image synthesis, and challenges in GAN training.
Transfer learning concepts including fine-tuning pre-trained models, feature extraction, domain adaptation, few-shot learning, and practical applications using models like BERT, GPT, and ImageNet-trained CNNs.
Advanced optimization techniques for deep learning including Adam, RMSprop, learning rate scheduling, regularization methods (dropout, batch normalization), and strategies for training deep networks effectively.
Applied computer vision using deep learning including object detection (YOLO, R-CNN), image segmentation, facial recognition systems, medical image analysis, and real-world deployment of vision systems.