Neural Network Architectures

Complete syllabus

Every chapter and lesson

1. 01

   Chapter 1 · 5 lessons

   Overview of Neural Network Architectures

   1. 1.1

      Feedforward Neural Networks: Structure and Function

      8 min · Quick check

   2. 1.2

      Convolutional Neural Networks: Key Components

      8 min · Quick check

   3. 1.3

      Recurrent Neural Networks: Handling Sequential Data

      8 min · Quick check

   4. 1.4

      Generative Adversarial Networks: A Dual Approach

      8 min · Quick check

   5. 1.5

      Transfer Learning: Leveraging Pre-trained Models

      8 min · Quick check

2. 02

   Chapter 2 · 5 lessons

   Strengths and Weaknesses of Architectures

   1. 2.1

      Comparative Analysis of CNNs and RNNs

      7 min · Quick check

   2. 2.2

      Use Cases for Generative Adversarial Networks (GANs)

      7 min · Quick check

   3. 2.3

      Limitations of Traditional Neural Network Architectures

      7 min · Quick check

   4. 2.4

      Emerging Architectures and Their Potential

      7 min · Quick check

   5. 2.5

      Performance Metrics for Neural Network Evaluation

      7 min · Quick check

3. 03

   Chapter 3 · 5 lessons

   Capstone: Implementing and Evaluating Architectures

   1. 3.1

      Building a CNN for Image Classification

      9 min · Quick check

   2. 3.2

      Creating an RNN for Sequence Prediction

      9 min · Quick check

   3. 3.3

      Using a GAN for Image Generation

      9 min · Quick check

   4. 3.4

      Evaluating Model Performance Using Metrics

      9 min · Quick check

   5. 3.5

      Documenting the Implementation Process

      9 min · Quick check