Machine learning
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1
Label
Machine learning
Name
Machine learning
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Incoming Resources
- Machine Learning with Spark and Python, 2nd Edition, Bowles, Michael
- TinyML, li yu TensorFlow Lite zai Arduino he chao di gong hao wei kong zhi qi shang bu shu ji qi xue xi = TinyML : machine learning with TensorFlow Lite on Arduino and ultra-low-power microcontrollers, Pete Warden, Daniel Situnayake zhu ; Wei Lan, Bu Jie yi
- A practical guide to algorithmic bias and explainability in machine learning, Alejandro Saucedo
- Probabilistic deep learning, with Python, Keras, and TensorFlow Probability, Oliver Dürr, Beate Sick ; with Elvis Murina
- Java data science cookbook, explore the power of MLlib, DL4j, Weka and more, Rushdi Shams
- Hello, TensorFlow!, building and training your first TensorFlow graph from the ground up, Aaron Schumacher
- AI as a Service, Peter Elger
- Manipulating and Measuring Model Interpretability, Forough Poursabzi-Sangdeh
- Deep reinforcement learning in action, Alexander Zai, Brandon Brown
- Game engines and machine learning, Paris Buttfield-Addison, Mars Geldard, Tim Nugent
- Informatics and machine learning, from Martingales to metaheuristics, Stephen Winters-Hilt
- Machine learning and data science in the oil and gas industry, best practices, tools, and case studies, edited by Patrick Bangert
- Mahout in action, Sean Owen [and others]
- Deep learning with applications using Python, chatbots and face, object, and speech recognition with TensorFlow and Keras, Navin Kumar Manaswi
- Machine learning and cognitive computing for mobile communications and wireless networks, edited by Krishna Kant Singh, KIET Group of Institutions, Delhi-NCR, Ghaziabad, India, Akansha Singh, Department of CSE, ASET, Amity University Uttar Pradesh, Noida, India, Korhan Cengiz, Electrical-Electronics Engineering Department, Trakya University, Edine, Turkey, and Dac-Nhuong Le, Faculty of Information Technology, Haiphong University, Vietnam
- Knowledge discovery from data streams, João Gama
- Machine learning algorithms, reference guide for popular algorithms for data science and machine learning, Giuseppe Bonaccorso
- Introduction to deep learning, concepts and fundamentals, with Laura Graesser
- Working on a "hands-on-keyboard" ML model with PySpark
- TensorFlow Machine Learning Cookbook
- QUANTUM MACHINE LEARNING AND OPTIMISATION IN FINANCE, on the road to quantum advantage /, Antoine Jacquier, Oleksiy Kondratyev ; foreword by Alexander Lipton & Marcos López de Prado
- Deep Learning mit Python und Keras, Das Praxis-Handbuch vom Entwickler der Keras-Bibliothek, Francois Chollet ; Übersetzung aus dem Amerikanischen von Knut Lorenzen
- Practical feature engineering, Ted Dunning
- Hands-on Markov models with Python, implement probabilistic models for learning complex data sequences using Python ecosystem, Ankur Ankan, Abinash Panda
- Deep Learning for Natural Language Processing, Solve Your Natural Language Processing Problems with Smart Deep Neural Networks
- Machine learning with Python for everyone, Mark E. Fenner
- AWS Redshift data platform fundamentals
- Meet the expert, Roger Magoulas on AI adoption in the enterprise in 2020, Roger Magoulas
- Training a reinforcement learning agent to play soccer (football)
- MATLAB for machine learning, functions, algorithms, and use cases, Giuseppe Ciaburro
- Preparing and exploring security data for machine learning
- Oil, gas, and data, high-performance data tools in the production of industrial power, Daniel Cowles
- Implementing machine learning for finance, a systematic approach to predictive risk and performance analysis for investment portfolios, Tshepo Chris Nokeri
- Practical machine learning with Rust, creating intelligent applications in Rust, Joydeep Bhattacharjee
- The TensorFlow Workshop, Moocarme, Matthew
- Pragmatic AI and machine learning core principles, LiveLessons, Noah Gift
- Machine learning for auditors, automating fraud investigations through artificial intelligence, Maris Sekar
- Machine learning for economics and finance in TensorFlow 2, deep learning models for research and industry, Isaiah Hull
- TensorFlow, powerful predictive analytics with TensorFlow : predict valuable insights of your data with TensorFlow, Md. Rezaul Karim
- Assimilate TensorFlow with Rust, Alfredo Deza, Noah Gift
- Data statistics with full stack Python, Teclov
- Spotlight on data, the power of deep learning in the hands of domain experts, with Jeremy Howard and Hirokazu Narui
- Train Word embeddings from scratch with Nessvec and PyTorch
- Deep learning using Keras, a complete and compact guide for beginners, Abhilash Nelson
- Hands-on artificial intelligence for beginners, an introduction to AI concepts, algorithms, and their implementation, Patrick D. Smith
- Deep learning for recommender systems, or How to compare pears with apples, Marcel Kurovski
- Deep learning receptury, Douwe Osinga
- Applied deep learning, a case-based approach to understanding neural networks, Umberto Michelucci
- AI and the index management problem, Data Science Salon
- Machine learning fundamentals with Amazon SageMaker on AWS, LiveLessons, Asli Bilgin