Artificial Intelligence, Machine Learning, and Deep Learning (eBook, ePUB)
A Practical Guide to Advanced AI Techniques
Redaktion: Mercury Learning and Information
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This book introduces AI, then explores machine learning, deep learning, natural language processing (NLP), and reinforcement learning. Readers learn about classifiers like logistic regression, k-NN, decision trees, random forests, and SVMs. It delves into deep learning architectures such as CNNs, RNNs, LSTMs, and autoencoders, with Keras-based code samples supplementing the theory.Starting with a foundational AI overview, the course progresses into machine learning, explaining classifiers and their applications. It continues with deep learning, focusing on architectures like CNNs and RNNs. Adv...
This book introduces AI, then explores machine learning, deep learning, natural language processing (NLP), and reinforcement learning. Readers learn about classifiers like logistic regression, k-NN, decision trees, random forests, and SVMs. It delves into deep learning architectures such as CNNs, RNNs, LSTMs, and autoencoders, with Keras-based code samples supplementing the theory.
Starting with a foundational AI overview, the course progresses into machine learning, explaining classifiers and their applications. It continues with deep learning, focusing on architectures like CNNs and RNNs. Advanced topics include LSTMs and autoencoders, essential for modern AI. The book also covers NLP and reinforcement learning, emphasizing their importance.
Understanding these concepts is vital for developing advanced AI systems. This book transitions you from beginner to proficient AI practitioner, combining theoretical knowledge and practical skills. Appendices on Keras, TensorFlow 2, and Pandas enrich the learning experience. By the end, readers will understand AI principles and be ready to apply them in real-world scenarios.
Starting with a foundational AI overview, the course progresses into machine learning, explaining classifiers and their applications. It continues with deep learning, focusing on architectures like CNNs and RNNs. Advanced topics include LSTMs and autoencoders, essential for modern AI. The book also covers NLP and reinforcement learning, emphasizing their importance.
Understanding these concepts is vital for developing advanced AI systems. This book transitions you from beginner to proficient AI practitioner, combining theoretical knowledge and practical skills. Appendices on Keras, TensorFlow 2, and Pandas enrich the learning experience. By the end, readers will understand AI principles and be ready to apply them in real-world scenarios.
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