Python deep learning : exploring deep learning techniques and neural network architectures with PyTorch, Keras, and TensorFlow /
PyTorch Deep Learning Hands-On : Apply Modern AI Techniques with CNNs, RNNs, GANs, Reinforcement Learning, and More /
Python deep learning : next generation techniques to revolutionize computer vision, AI, speech and data analysis /
The deep learning with Keras workshop : learn how to define and train neural network models with just a few lines of code /
Approximation Methods for Efficient Learning of Bayesian Networks.
Deep learning with PyTorch : a practical approach to building neural network models using PyTorch /
Modern Computer Vision with Pytorch : Explore Deep Learning Concepts and Implement over 50 Real-World Image Applications.
ADVANCED DEEP LEARNING WITH TENSORFLOW 2 AND KERAS : APPLY DL, GANS, VAES, DEEP RL, UNSUPERVISED LEARNING, OBJECT DETECTION AND SEGMENTATION, AND MORE.
A guide to neural computing applications /
Neural systems for control /
Principles of artificial neural networks /
Neural networks and pattern recognition /
Codeless Deep Learning with KNIME : Build, Train, and Deploy Various Deep Neural Network Architectures Using KNIME Analytics Platform.
Neural networks and systolic array design /
Neural nets and chaotic carriers /
Neural network design and the complexity of learning /
Universality and emergent computation in cellular neural networks /
Reinforcement learning with TensorFlow : a beginner's guide to designing self-learning systems with TensorFlow and OpenAI Gym /
Hands-On Neural Networks with Keras : Design and Create Neural Networks Using Deep Learning and Artificial Intelligence Principles.
Strategy for an Army center for network science, technology, and experimentation /
Talking nets : an oral history of neural networks /
Implementation techniques /
Optimization techniques /
Hybrid methods in pattern recognition /
Neuro-fuzzy pattern recognition /
Fractals of brain, fractals of mind : in search of a symmetry bond /
Neural networks and animal behavior /
Compendium of neurosymbolic artificial intelligence /
Natural language processing and computational linguistics : a practical guide to text analysis with Python, Gensim, spaCy, and Keras /
Mastering machine learning with R : advanced prediction, algorithms, and learning methods with R 3.x /
Advances in large margin classifiers /
Machine learning and artificial intelligence : proceedings of MLIS 2020 /
Artificial intelligence by example : develop machine intelligence from scratch using real artificial intelligence use cases /
Hands-on data science and Python machine learning : perform data mining and machine learning efficiently using Python and Spark /
Mastering machine learning algorithms : expert techniques to implement popular machine learning algorithms and fine-tune your models /
Machine learning with Go : implement regression, classification, clustering, time-series models, neural networks, and more using the Go programming language /
Python deep learning cookbook : over 75 practical recipes on neural network modeling, reinforcement learning, and transfer learning using Python /
Dynamic fuzzy machine learning /
Data Science with Python : Combine Python with Machine Learning Principles to Discover Hidden Patterns in Raw Data.
Engineering MLOps : rapidly build, test, and manage production-ready machine learning life cycles at scale /
Mastering Machine Learning Algorithms : Expert techniques for implementing popular machine learning algorithms, fine-tuning your models, and understanding how they work, 2nd Edition. /
Python : deeper insights into machine learning : leverage benefits of machine learning techniques using Python : a course in three modules.
Data analysis with Python : a modern approach /
Machine Learning Algorithms : Popular Algorithms for Data Science and Machine Learning, 2nd Edition.
Machine Learning Engineering with Python : Manage the Production Life Cycle of Machine Learning Models Using MLOps with Practical Examples.
Data Science for Business and Decision Making : an introductory Text for Students and Practitioners /
Machine learning in asset pricing /
Deep neural networks and applications
Hybrid algorithms, techniques and implementations of fuzzy logic /
Relativity and quantum relativistic theories.
Applications of Graph Theory