Chapter 1: Introduction PyTorch, Tensors, Tensor Operations and Basics.- Chapter 2: Probability distributions using PyTorch.- Chapter 3: Convolutional Neural Network and RNN using PyTorch.- Chapter 4: Introduction to Neural Networks, Tensor Differentiation .- Chapter 5: Supervised Learning using PyTorch.- Chapter 6: Fine Tuning Deep Learning Algorithms using PyTorch.- Chapter 7: NLP and Text Processing using PyTorch.-
Implement Solutions to Model Explainability and Interpretability with Python
Focusing on the practical application of Explainable AI (XAI), this book guides readers through using XAI libraries to enhance trust in AI and machine learning models. By employing a problem-solution framework, it effectively clarifies complex machine learning concepts and algorithms, making them accessible for practitioners looking to implement transparent AI solutions.
Artificial Intelligence Model Explanations Using Python-Based Libraries, Extensions, and Frameworks
Focusing on the intricacies of AI decision-making, this book delves into biases and the reliability of algorithms, particularly within black-box models. It emphasizes enhancing adaptability, interpretability, and explainability of AI outputs. Readers will engage with practical frameworks, utilizing Python XAI libraries, TensorFlow 2.0+, Keras, and custom Python wrappers to better understand and improve AI predictions.