Chapter 1 Introduction Deep Learning A brief introduction to Machine Learning and Deep Learning. Chapter 2 Introduction to PyTorch A quick-start guide to PyTorch and a comprehensive introduction to tensors, linear algebra and mathematical operations for Tensors. The chapter provides the required PyTorch foundations for readers to meaningfully implement practical Deep Learning solutions for various topics within the book. Advanced PyTorch topics are explored as and when touch-based during the course of exercises in later chapter. Chapter 6 Convolutional Neural Networks (35 Pages) Introduction to Convolutional Neural Networks for Computer Vision. MNIST (classification of handwritten digits), and later extend the exercise for a binary classification use-case with the popular cats and dogs' dataset. Chapter 7 Recurrent Neural Networks Introduction to Recurrent Neural Networks and its variants (viz. Bidirectional RNNs and LSTMs). Chapter 8 Recent advances in Deep Learning A brief note of the cutting-edge advancements in the field will be added.
Chapter 1 Introduction Deep Learning A brief introduction to Machine Learning and Deep Learning. Chapter 2 Introduction to PyTorch A quick-start guide to PyTorch and a comprehensive introduction to tensors, linear algebra and mathematical operations for Tensors. The chapter provides the required PyTorch foundations for readers to meaningfully implement practical Deep Learning solutions for various topics within the book. Advanced PyTorch topics are explored as and when touch-based during the course of exercises in later chapter. Chapter 6 Convolutional Neural Networks (35 Pages) Introduction to Convolutional Neural Networks for Computer Vision. MNIST (classification of handwritten digits), and later extend the exercise for a binary classification use-case with the popular cats and dogs' dataset. Chapter 7 Recurrent Neural Networks Introduction to Recurrent Neural Networks and its variants (viz. Bidirectional RNNs and LSTMs). Chapter 8 Recent advances in Deep Learning A brief note of the cutting-edge advancements in the field will be added.
in 2 offers
Chapter 1 Introduction Deep Learning A brief introduction to Machine Learning and Deep Learning. Chapter 2 Introduction to PyTorch A quick-start guide to PyTorch and a comprehensive introduction to tensors, linear algebra and mathematical operations for Tensors. The chapter provides the required PyTorch foundations for readers to meaningfully implement practical Deep Learning solutions for various topics within the book. Advanced PyTorch topics are explored as and when touch-based during the course of exercises in later chapter. Chapter 6 Convolutional Neural Networks (35 Pages) Introduction to Convolutional Neural Networks for Computer Vision. MNIST (classification of handwritten digits), and later extend the exercise for a binary classification use-case with the popular cats and dogs' dataset. Chapter 7 Recurrent Neural Networks Introduction to Recurrent Neural Networks and its variants (viz. Bidirectional RNNs and LSTMs). Chapter 8 Recent advances in Deep Learning A brief note of the cutting-edge advancements in the field will be added.
Chapter 1 Introduction Deep Learning A brief introduction to Machine Learning and Deep Learning. Chapter 2 Introduction to PyTorch A quick-start guide to PyTorch and a comprehensive introduction to tensors, linear algebra and mathematical operations for Tensors. The chapter provides the required PyTorch foundations for readers to meaningfully implement practical Deep Learning solutions for various topics within the book. Advanced PyTorch topics are explored as and when touch-based during the course of exercises in later chapter. Chapter 6 Convolutional Neural Networks (35 Pages) Introduction to Convolutional Neural Networks for Computer Vision. MNIST (classification of handwritten digits), and later extend the exercise for a binary classification use-case with the popular cats and dogs' dataset. Chapter 7 Recurrent Neural Networks Introduction to Recurrent Neural Networks and its variants (viz. Bidirectional RNNs and LSTMs). Chapter 8 Recent advances in Deep Learning A brief note of the cutting-edge advancements in the field will be added.
Last updated at 26/09/2024 19:37:05
Go to store
See 5 more history offers
available 6 months ago
Low stock
available 6 months ago
Low stock
Imprint | Apress |
Pub date | 10 Apr 2021 |
DEWEY edition | 23 |
Language | English |
Spine width | 25mm |
Updated about 14 hours ago
See 5 more history offers
Imprint | Apress |
Pub date | 10 Apr 2021 |
DEWEY edition | 23 |
Language | English |
Spine width | 25mm |