Neural Networks for Pattern Recognition
( 25 Modules )

Module #1
Introduction to Pattern Recognition
Overview of pattern recognition, importance, and applications

Module #2
Introduction to Neural Networks
Basic concepts, history, and types of neural networks

Module #3
Mathematical Fundamentals
Linear algebra, calculus, and probability theory review

Module #4
Perceptron and Multilayer Perceptrons
Introduction to perceptron, multilayer perceptrons, and backpropagation

Module #5
Activation Functions
Types of activation functions, their properties, and applications

Module #6
Supervised Learning
Introduction to supervised learning, types, and objectives

Module #7
Cost Functions and Optimization
Mean squared error, cross-entropy, and optimization techniques

Module #8
Gradient Descent and Its Variants
Batch gradient descent, stochastic gradient descent, and mini-batch gradient descent

Module #9
Convolutional Neural Networks (CNNs)
Introduction to CNNs, architecture, and applications

Module #10
Recurrent Neural Networks (RNNs)
Introduction to RNNs, architecture, and applications

Module #11
Long Short-Term Memory (LSTM) Networks
Introduction to LSTMs, architecture, and applications

Module #12
Autoencoders
Introduction to autoencoders, architecture, and applications

Module #13
Unsupervised Learning
Introduction to unsupervised learning, types, and objectives

Module #14
Clustering Algorithms
K-means, hierarchical clustering, and density-based clustering

Module #15
Dimensionality Reduction
Principal component analysis, t-SNE, and autoencoders for dimensionality reduction

Module #16
Neural Network Architectures
Introduction to ResNet, Inception, and other prominent architectures

Module #17
Deep Learning for Computer Vision
Applications of deep learning in computer vision, including object detection and segmentation

Module #18
Deep Learning for Natural Language Processing
Applications of deep learning in NLP, including language models and text classification

Module #19
Deep Learning for Speech Recognition
Applications of deep learning in speech recognition

Module #20
Regularization Techniques
Dropout, L1 and L2 regularization, and early stopping

Module #21
Hyperparameter Tuning
Introduction to hyperparameter tuning, including grid search and random search

Module #22
Model Evaluation
Metrics for evaluating neural network performance, including accuracy, precision, and recall

Module #23
Model Deployment
Introduction to deploying neural networks, including TensorFlow, PyTorch, and Keras

Module #24
Advanced Topics in Neural Networks
Introduction to attention mechanisms, transformers, and generative adversarial networks

Module #25
Course Wrap-Up & Conclusion
Planning next steps in Neural Networks for Pattern Recognition career

WIZAPE

ONLINE RESOURCES

ABOUT WIZAPE

ONLINE PORTAL

Our priority is to cultivate a vibrant community before considering the release of a token. By focusing on engagement and support, we can create a solid foundation for sustainable growth. Let’s build this together!

We're giving our website a fresh new look and feel! 🎉 Stay tuned as we work behind the scenes to enhance your experience.
Get ready for a revamped site that’s sleeker, and packed with new features. Thank you for your patience. Great things are coming!

CONTACT-US PRIVACY POLICY

Neural Networks for Pattern Recognition ( 25 Modules )

Neural Networks for Pattern Recognition
( 25 Modules )