It is a subfield of machine learning that uses algorithms inspired by the structure and function of the brain, known as artificial neural networks, to process and analyze large datasets. It allows for the automatic extraction of features from data and can be used for tasks such as image recognition, speech recognition, and natural language processing.
Our deep learning syllabus may cover the following topics
- Introduction to machine learning and deep learning:
This module covers the basics of machine learning, the types of machine learning, the differences between traditional machine learning and deep learning.
- Neural networks and artificial intelligence:
This module provides an overview of neural networks and artificial intelligence and how they relate to deep learning.
- Multilayer Perceptrons (MLP) and activation functions:
This module covers the basics of Multilayer Perceptrons (MLPs), activation functions such as sigmoid, tanh, ReLU, and their properties.
- Convolutional Neural Networks (CNN):
This module covers the basics of Convolutional Neural Networks (CNNs), how they work, and their use in computer vision tasks.
- Recurrent Neural Networks (RNN) and Long-Short Term Memory (LSTM):
This module covers the basics of Recurrent Neural Networks (RNNs), Long-Short Term Memory (LSTM) and their applications in natural language processing.
- Generative Adversarial Networks (GAN):
This module covers the basics of Generative Adversarial Networks (GANs), how they work and their applications.
This module covers the basics of autoencoders, their types and applications.
- Transfer learning and fine-tuning pre-trained models:
This module covers the basics of transfer learning, fine-tuning pre-trained models, and the benefits and limitations of these techniques.
- Applications of deep learning in computer vision, natural language processing, and speech recognition:
This module covers the various applications of deep learning in computer vision, natural language processing, and speech recognition.
- Model evaluation, regularization and optimization:
This module covers the basics of model evaluation, regularization techniques such as L1 and L2 regularization, and optimization techniques such as stochastic gradient descent, Adam, and others.
- Deep learning libraries such as TensorFlow, PyTorch, and Keras:
This module covers the basics of popular deep learning libraries such as TensorFlow, PyTorch, and Keras and how to use them to build deep learning models.
- Projects and hands-on experience in building deep learning models:
This module provides students with the opportunity to work on real-world deep learning projects, build models, and get hands-on experience.
Application of Deep learning
Deep learning has a wide range of applications in various domains, including:
- Computer vision:
Image and video classification, object detection, segmentation, and analysis.
- Natural language processing (NLP):
Sentiment analysis, text classification, language translation, and text generation.
- Speech recognition:
Speech-to-text, speaker recognition, and language identification.
Perception, decision-making, and control.
Medical image analysis, diagnosis, and treatment recommendation.
Fraud detection, risk management, and stock market prediction.
Game playing and recommendation systems.
Customer segmentation, behavior analysis, and recommendation systems.
Autonomous driving, traffic prediction, and optimization.
Quality control, predictive maintenance, and supply chain optimization.