Advanced deep learning with Python: design and implement adavnced next-generation AI solutions using TensorFlow and PyTorch

Cover
Title Page
Copyright and Credits
About Packt
Contributors
Table of Contents
Preface
Section 1: Core Concepts
Chapter 1: The Nuts and Bolts of Neural Networks
	The mathematical apparatus of NNs
		Linear algebra
			Vector and matrix operations
		Introduction to probability
			Probability and sets
			Conditional probability and the Bayes rule
			Random variables and probability distributions
				Probability distributions
			Information theory
		Differential calculus
	A short introduction to NNs
		Neurons
		Layers as operations
		NNs
		Activation functions
		The universal approximation theorem
	Training NNs
		Gradient descent
		Cost functions
		Backpropagation
		Weight initialization
		SGD improvements
	Summary
Section 2: Computer Vision
Chapter 2: Understanding Convolutional Networks
	Understanding CNNs
		Types of convolutions
			Transposed convolutions
			1×1 convolutions
			Depth-wise separable convolutions
			Dilated convolutions
		Improving the efficiency of CNNs
			Convolution as matrix multiplication
			Winograd convolutions
		Visualizing CNNs
			Guided backpropagation
			Gradient-weighted class activation mapping
		CNN regularization
	Introducing transfer learning
		Implementing transfer learning with PyTorch
		Transfer learning with TensorFlow 2.0
	Summary
Chapter 3: Advanced Convolutional Networks
	Introducing AlexNet
	An introduction to Visual Geometry Group 
		VGG with PyTorch and TensorFlow
	Understanding residual networks
		Implementing residual blocks
	Understanding Inception networks
		Inception v1
		Inception v2 and v3
		Inception v4 and Inception-ResNet
	Introducing Xception
	Introducing MobileNet
	An introduction to DenseNets
	The workings of neural architecture search
	Introducing capsule networks
		The limitations of convolutional networks
		Capsules
			Dynamic routing
		The structure of the capsule network
	Summary
Chapter 4: Object Detection and Image Segmentation
	Introduction to object detection
		Approaches to object detection
		Object detection with YOLOv3
			A code example of YOLOv3 with OpenCV
		Object detection with Faster R-CNN
			Region proposal network
			Detection network
			Implementing Faster R-CNN with PyTorch
	Introducing image segmentation
		Semantic segmentation with U-Net
		Instance segmentation with Mask R-CNN
			Implementing Mask R-CNN with PyTorch
	Summary
Chapter 5: Generative Models
	Intuition and justification of generative models
	Introduction to VAEs
		Generating new MNIST digits with VAE
	Introduction to GANs
		Training GANs
			Training the discriminator
			Training the generator
		Putting it all together
		Problems with training GANs
	Types of GAN
		Deep Convolutional GAN
			Implementing DCGAN
		Conditional GAN
			Implementing CGAN
		Wasserstein GAN
			Implementing WGAN
		Image-to-image translation with CycleGAN
			Implementing CycleGAN
				Building the generator and discriminator
				Putting it all together
	Introducing artistic style transfer
	Summary
Section 3: Natural Language and Sequence Processing
Chapter 6: Language Modeling
	Understanding n-grams
	Introducing neural language models
		Neural probabilistic language model
		Word2Vec
			CBOW
			Skip-gram
			fastText
		Global Vectors for Word Representation model
	Implementing language models
		Training the embedding model
		Visualizing embedding vectors
	Summary
Chapter 7: Understanding Recurrent Networks
	Introduction to RNNs
		RNN implementation and training
			Backpropagation through time
			Vanishing and exploding gradients
	Introducing long short-term memory
		Implementing LSTM
	Introducing gated recurrent units
		Implementing GRUs
	Implementing text classification
	Summary
Chapter 8: Sequence-to-Sequence Models and Attention
	Introducing seq2seq models
	Seq2seq with attention
		Bahdanau attention
		Luong attention
		General attention
		Implementing seq2seq with attention
			Implementing the encoder
			Implementing the decoder
			Implementing the decoder with attention
			Training and evaluation
	Understanding transformers
		The transformer attention
		The transformer model
		Implementing transformers
			Multihead attention
			Encoder
			Decoder
			Putting it all together
	Transformer language models
		Bidirectional encoder representations from transformers
			Input data representation
			Pretraining
			Fine-tuning
		Transformer-XL
			Segment-level recurrence with state reuse
			Relative positional encodings
		XLNet
		Generating text with a transformer language model
	Summary
Section 4: A Look to the Future
Chapter 9: Emerging Neural Network Designs
	Introducing Graph NNs
		Recurrent GNNs
		Convolutional Graph Networks
			Spectral-based convolutions
			Spatial-based convolutions with attention
		Graph autoencoders
		Neural graph learning
			Implementing graph regularization
	Introducing memory-augmented NNs
		Neural Turing machines
		MANN*
	Summary
Chapter 10: Meta Learning
	Introduction to meta learning
		Zero-shot learning
		One-shot learning
		Meta-training and meta-testing
	Metric-based meta learning
		Matching networks for one-shot learning
		Siamese networks
			Implementing Siamese networks
		Prototypical networks
	Optimization-based learning
	Summary
Chapter 11: Deep Learning for Autonomous Vehicles
	Introduction to AVs
		Brief history of AV research
		Levels of automation
	Components of an AV system 
		Environment perception
			Sensing
			Localization
			Moving object detection and tracking
		Path planning
	Introduction to 3D data processing
	Imitation driving policy
		Behavioral cloning with PyTorch
			Generating the training dataset
			Implementing the agent neural network
			Training
			Letting the agent drive
			Putting it all together
	Driving policy with ChauffeurNet
		Input and output representations
		Model architecture
		Training
	Summary
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Index