Master Neural Networks with Practical Examples
Learn to design, train, and deploy neural networks using modern frameworks like PyTorch and TensorFlow. Includes hands-on implementation and visualization techniques.
Start LearningLearn to design, train, and deploy neural networks using modern frameworks like PyTorch and TensorFlow. Includes hands-on implementation and visualization techniques.
Start LearningThis tutorial covers the fundamentals of neural network design and implementation, including feedforward, convolutional, and recurrent network architectures. You'll learn how to train models using optimization techniques and evaluate performance with real-world datasets.
10+ Labs
Hands-On Lessons
Master the key building blocks for constructing and training artificial neural networks
Understand the basic building block of neural networks and its function in binary classification tasks.
Learn how to construct neural networks with directed acyclic connections between input and output layers.
Explore non-linear functions like ReLU, Sigmoid, and Tanh that enable complex pattern recognition.
Discover how CNNs work for image processing and computer vision applications.
Learn to sequence processing and memory patterns for time-series and NLP tasks.
Master error gradient computation for model optimization and weight updates.
Core principles for neural network design and optimization
Learn various SGD, Adam, and RMSprop algorithms for model training.
Apply L1/L2 regularization, dropout, and batch normalization to prevent overfitting.
Explore deep, complex, and specialized network architectures
Create deepfakes and synthetic data using GAN architectures for generative tasks.
Understand attention mechanisms and self-attention in modern NLP models like BERT and GPT.
Apply concepts with guided coding projects and exercises
Build a CNN to recognize handwritten digits using the MNIST dataset with TensorFlow.
Implement transformer-based summarization using PyTorch for NLP applications.