Fundamentals of Neural Networks

Neural networks simulate brain-like structures with multiple layers of interconnected neurons. Deep learning extends these architectures (CNN, RNN) to handle images, sequences, and more. Transfer learning, GANs, and deployment are advanced topics for specialized tasks.

High-level neural network ideas originate from simulating how neurons in the brain process information. This section establishes the differences between traditional machine learning and deep learning, the major frameworks, and the basic structure of neural networks—covering neuron components, feedforward topologies, and popular activation functions.

FOUNDATIONS: AI, MACHINE LEARNING, AND DEEP LEARNING

Learning Objectives

• Clarify how deep learning differs from traditional ML

• Cite major frameworks (TensorFlow, PyTorch) and real-world use cases (vision, NLP, speech)

• Understand GPU acceleration and typical model-building workflows

Indicative Content

• AI vs. ML vs. Deep Learning

  • Key distinctions in scope and complexity

• Framework Ecosystem

  • Keras, Torch, ONNX

• Use Cases

  • Image classification, text generation, speech recognition

NEURAL NETWORK BASICS

Learning Objectives

• Describe neuron structure (weights, bias, activation) and multi-layer topologies

• Differentiate feedforward vs. CNN vs. RNN approaches

• Explore ReLU, sigmoid, tanh, softmax activations

Indicative Content

• Multi-Layer Perceptron

  • Basic classification/regression

• Activation Functions

  • Nonlinear transformations (ReLU, sigmoid, tanh, softmax)

• Variants

  • CNN (convolutional), RNN (recurrent), MLP (fully connected)

TOOLS & METHODOLOGIES (FUNDAMENTALS OF NEURAL NETWORKS)

• Frameworks

  • TensorFlow, PyTorch for neural network construction

• Accelerated Computing

  • GPU usage (CUDA) or specialized hardware

• Model-Building Workflow

  • Data preparation → model definition → compilation → initial training/testing

NETWORK TRAINING & ARCHITECTURES

Deep learning success depends on effective training and the right architectural choice. This section focuses on how forward and backward propagation update weights, along with suitable loss functions and optimizers. It also explores the main neural network types—MLP, CNN, RNN—and how to evaluate them for various data modalities.

TRAINING NEURAL NETWORKS

Learning Objectives

• Explain forward propagation (computing outputs) and backpropagation (updating weights)

• Choose loss functions (MSE, cross-entropy) and optimizers (SGD, Adam)

• Monitor convergence and tune hyperparameters (learning rate, batch size)

Indicative Content

• Forward Pass

  • Weighted sums, activation layers

• Backpropagation

  • Chain rule, gradient descent

• Optimization

  • Mini-batch SGD, Adam, scheduling learning rates

FEEDFORWARD, CNN, AND RNN ARCHITECTURES

Learning Objectives

• Implement MLP for tabular data, CNN for images, RNN for sequences

• Evaluate using classification metrics or custom domain criteria

• Understand specialized layers (convolution/pooling, LSTM/GRU)

Indicative Content

• MLP

  • Dense layers, hidden units, dropout

• CNN

  • Filters, strides, pooling, deeper networks (ResNet)

• RNN

  • LSTM gates, GRU units, sequence modeling for text/time series

TOOLS & METHODOLOGIES (NETWORK TRAINING & ARCHITECTURES)

• Training Libraries

  • Built-in modules for forward/backprop, dynamic computation graphs

• Loss & Optimization

  • Cross-entropy for classification, MSE for regression, advanced optimizers (Adam)

• Evaluation

  • Accuracy, precision/recall, domain-specific metrics (e.g., BLEU for language models)