https://www.amazon.com/gp/product/B0B1L5WJR1?ref_=dbs_m_mng_rwt_calw_tkin_4&storeType=ebooks: Image Classification Using Python and Techniques of Computer Vision and Machine Learning

This book implemented six different algorithms to classify images with the prediction accuracy of the testing data as the primary criterion (the higher the better) and the time consumption as the secondary one (the shorter the better).

The six algorithms are: Tiny Images Representation + Classifiers; HOG (Histogram of Oriented Gradients) Features Representation + Classifiers; Bag of SIFT (Scale Invariant Feature Transform) Features Representation + Classifiers; Training a CNN (Convolutional Neural Network) from scratch; Fine Tuning a Pre-Trained Deep Network (AlexNet); and Pre-Trained Deep Network (AlexNet) Features Representation + Classifiers.

For the algorithms that use classifiers, two commonly used classifiers are implemented: the k-Nearest Neighbors (KNN) and the Support Vector Machines (SVM).

The codes were written with Python in Jupyter Notebook, and they could be executed on both CPUs and GPUs.

This book is a great project guidance for students in middle schools, high schools, and colleges.

https://www.amazon.com/gp/product/B09GNLRSZX?ref_=dbs_m_mng_rwt_calw_tkin_0&storeType=ebooks: Image Classification Using Python and Techniques of Computer Vision and Machine Learning: Step-by-step Classifying Images with Python and Techniques of Computer Vision and Machine Learning

This book implemented six different algorithms to classify images with the prediction accuracy of the testing data as the primary criterion (the higher the better) and the time consumption as the secondary one (the shorter the better).

The six algorithms are: Tiny Images Representation + Classifiers; HOG (Histogram of Oriented Gradients) Features Representation + Classifiers; Bag of SIFT (Scale Invariant Feature Transform) Features Representation + Classifiers; Training a CNN (Convolutional Neural Network) from scratch; Fine Tuning a Pre-Trained Deep Network (AlexNet); and Pre-Trained Deep Network (AlexNet) Features Representation + Classifiers.

For the algorithms that use classifiers, two commonly used classifiers are implemented: the k-Nearest Neighbors (KNN) and the Support Vector Machines (SVM).

The codes were written with Python in Jupyter Notebook, and they could be executed on both CPUs and GPUs.

This book is a great project guidance for students in middle schools, high schools, and colleges.

https://www.amazon.com/gp/product/B0CW1LP4TH?ref_=dbs_m_mng_rwt_calw_tkin_0&storeType=ebooks: ChatGPT Odyssey: Journeying Through the Wonders of AI Dialogue

Embark on a journey into Conversational AI:

1. Unlock the Secrets: Explore ChatGPT's architecture, evolution, and milestones in natural language processing.
2. Future Trends and Speculations: Envision a future where AI understands emotions, collaborates in real-time, and transcends language barriers.
3. Impact on Society and Ethics: Examine AI's applications, ethical considerations, and responsible deployment in various industries.
4. Empower with Resources: Curated tools, platforms, and resources for developers, along with tips for creating engaging chatbot experiences.
5. Dream, Innovate, and Shape the Future: An invitation to be part of the ongoing conversation, pushing the boundaries of human-machine collaboration.
6. Craft Your Chatbot: Step-by-step guide to creating a chatbot with ChatGPT, integrating multimedia, and implementing NLP.