Understanding Neural Networks: Building Blocks of Deep Learning

 


Introduction

  • Briefly introduce neural networks and their role in deep learning.
  • Mention real-world applications (e.g., image recognition, NLP, self-driving cars).
  • Provide a simple analogy (e.g., comparing neurons in the brain to artificial neurons in a network).

1. What is a Neural Network?

  • Define neural networks in the context of artificial intelligence.
  • Explain how they are inspired by the human brain.
  • Introduce basic terms: neurons, layers, activation functions.

2. Architecture of a Neural Network

  • Input Layer: Where data enters the network.
  • Hidden Layers: Where computations happen.
  • Output Layer: Produces predictions.
  • Visual representation of a simple feedforward network.

3. Key Components of Neural Networks

  • Weights & Biases: How they influence predictions.
  • Activation Functions: ReLU, Sigmoid, Tanh (with examples).
  • Loss Function: Measures model performance (MSE, Cross-Entropy).
  • Backpropagation & Gradient Descent: Learning process of the network.

4. Types of Neural Networks

  • Feedforward Neural Networks (FNN)
  • Convolutional Neural Networks (CNNs): For image processing.
  • Recurrent Neural Networks (RNNs): For sequential data like text & speech.
  • Transformers: Modern architecture for NLP (BERT, GPT).

5. Training a Neural Network

  • Data preprocessing: Normalization, encoding, augmentation.
  • Splitting dataset: Training, validation, and test sets.
  • Hyperparameter tuning: Learning rate, batch size, number of layers.

6. Challenges in Neural Networks

  • Overfitting & Underfitting.
  • Vanishing & Exploding Gradients.
  • Computational cost and scalability.

7. Tools & Frameworks for Building Neural Networks

  • TensorFlow, Keras, PyTorch.
  • Example: Simple neural network in Python.

Conclusion

  • Recap key takeaways.
  • Encourage exploration of deep learning projects.

WEBSITE: https://www.ficusoft.in/deep-learning-training-in-chennai/

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