This book focuses on all the relevant topics of Deep Learning. It covers the conceptual, mathematical and practical aspects of deep learning & offers real time practical examples & case studies. It is aimed primarily at graduates, researchers and professionals working in Deep Learning.
This book focuses on all the relevant topics of Deep Learning. It covers the conceptual, mathematical and practical aspects of deep learning & offers real time practical examples & case studies. It is aimed primarily at graduates, researchers and professionals working in Deep Learning.
1. Introduction to Deep Learning. 2. The Tools and Prerequisites. 3. Machine Learning: The Fundamentals 4. The Deep Learning Framework. 5. CNN- Convolutional Neural Networks - A Complete Understanding. 6. CNN Architectures - An Evolution 7. Recurrent Neural Networks. 8. Autoencoders. 9. Generative Models. 10. Transfer Learning. 11. Intel OpenVino - A Must Know Deep Learning Toolkit. 12. Interview Questions and Answers.
1. Introduction to Deep Learning. 2. The Tools and Prerequisites. 3. Machine Learning: The Fundamentals 4. The Deep Learning Framework. 5. CNN- Convolutional Neural Networks - A Complete Understanding. 6. CNN Architectures - An Evolution 7. Recurrent Neural Networks. 8. Autoencoders. 9. Generative Models. 10. Transfer Learning. 11. Intel OpenVino - A Must Know Deep Learning Toolkit. 12. Interview Questions and Answers.
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