| Description |
1 online resource (805 p.) |
| Contents |
Cover -- Title Page -- Copyright and Credits -- Dedication -- About Packt -- Contributors -- Table of Contents -- Preface -- Section 1 -- Fundamentals of Deep Learning for Computer Vision -- Chapter 1: Artificial Neural Network Fundamentals -- Comparing AI and traditional machine learning -- Learning about the artificial neural network building blocks -- Implementing feedforward propagation -- Calculating the hidden layer unit values -- Applying the activation function -- Calculating the output layer values -- Calculating loss values -- Calculating loss during continuous variable prediction |
|
Calculating loss during categorical variable prediction -- Feedforward propagation in code -- Activation functions in code -- Loss functions in code -- Implementing backpropagation -- Gradient descent in code -- Implementing backpropagation using the chain rule -- Putting feedforward propagation and backpropagation together -- Understanding the impact of the learning rate -- Summarizing the training process of a neural network -- Summary -- Questions -- Chapter 2: PyTorch Fundamentals -- Installing PyTorch -- PyTorch tensors -- Initializing a tensor -- Operations on tensors |
|
Auto gradients of tensor objects -- Advantages of PyTorch's tensors over NumPy's ndarrays -- Building a neural network using PyTorch -- Dataset, DataLoader, and batch size -- Predicting on new data points -- Implementing a custom loss function -- Fetching the values of intermediate layers -- Using a sequential method to build a neural network -- Saving and loading a PyTorch model -- state dict -- Saving -- Loading -- Summary -- Questions -- Chapter 3: Building a Deep Neural Network with PyTorch -- Representing an image -- Converting images into structured arrays and scalars |
|
Why leverage neural networks for image analysis? -- Preparing our data for image classification -- Training a neural network -- Scaling a dataset to improve model accuracy -- Understanding the impact of varying the batch size -- Batch size of 32 -- Batch size of 10,000 -- Understanding the impact of varying the loss optimizer -- Understanding the impact of varying the learning rate -- Impact of the learning rate on a scaled dataset -- High learning rate -- Medium learning rate -- Low learning rate -- Parameter distribution across layers for different learning rates |
|
Impact of varying the learning rate on a non-scaled dataset -- Understanding the impact of learning rate annealing -- Building a deeper neural network -- Understanding the impact of batch normalization -- Very small input values without batch normalization -- Very small input values with batch normalization -- The concept of overfitting -- Impact of adding dropout -- Impact of regularization -- L1 regularization -- L2 regularization -- Summary -- Questions -- Section 2 -- Object Classification and Detection -- Chapter 4: Introducing Convolutional Neural Networks |
| Summary |
Starting from the basics of neural networks, this book covers over 50 applications of computer vision and helps you to gain a solid understanding of the theory of various architectures before implementing them. Each use case is accompanied by a notebook in GitHub with ready-to-execute code and self-assessment questions |
| Notes |
Description based upon print version of record |
| Subject |
Neural networks (Computer science)
|
|
Machine learning.
|
|
Artificial intelligence.
|
|
Python (Computer program language)
|
|
Neural Networks, Computer
|
|
Artificial Intelligence
|
|
Machine Learning
|
|
artificial intelligence.
|
|
Mathematical theory of computation.
|
|
Machine learning.
|
|
Neural networks & fuzzy systems.
|
|
Computers -- Image Processing.
|
|
Computers -- Machine Theory.
|
|
Computers -- Neural Networks.
|
|
Artificial intelligence
|
|
Machine learning
|
|
Neural networks (Computer science)
|
|
Python (Computer program language)
|
| Form |
Electronic book
|
| Author |
Reddy, Yeshwanth
|
| ISBN |
9781839216534 |
|
1839216530 |
|
