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About A Primer to the 42 Most commonly used Machine Learning Algorithms (With Code Samples). (Artificial Intelligence Book 1)
* Unlock the World of Machine Learning!*Dive into and “A Primer to the 42 Most Commonly Used Machine Learning Algorithms (With Code Samples) and ” and embark on a journey that demystifies the complexities of machine learning. This insightful guide is your key to understanding the core algorithms that power the future of technology.Explore with Each chapter unravels a new algorithm, presenting it in a clear, concise manner. From decision trees to neural networks, get ready to explore the intricate world of machine learning with ease.Code Don’t just learn, do. With practical code samples in Python, this book isn’t just about understanding concepts; it’s about applying them. Whether you’re a beginner or looking to expand your skill set, these hands-on examples will solidify your knowledge. and “A Primer to the 42 Most Commonly Used Machine Learning Algorithms (With Code Samples) and ” is more than a book; it’s a journey into the heart of AI. Grab your copy and start transforming your understanding of machine learning today!About the AuthorMurat Durmus is CEO and founder of AISOMA (a Frankfurt am Main (Germany) based company specializing in AI-based technology development and consulting) and Author of the book and “Mindful AI – Reflections on Artificial Intelligence and ” and and “INSIDE ALAN TURING and “The following algorithms are covered in this o ADABOOSTo ADAM OPTIMIZATIONo AGGLOMERATIVE CLUSTERINGo ARMA ARIMA MODELo BERTo CONVOLUTIONAL NEURAL NETWORKo DBSCANo DECISION TREEo DEEP Q-LEARNINGo EFFICIENTNETo FACTOR ANALYSIS OF CORRESPONDENCESo GANo GMMo GPT-3o GRADIENT BOOSTING MACHINEo GRADIENT DESCENTo GRAPH NEURAL NETWORKSo HIERARCHICAL CLUSTERINGo HIDDEN MARKOV MODEL (HMM)o INDEPENDENT COMPONENT ANALYSISo ISOLATION FORESTo K-MEANSo K-NEAREST NEIGHBOURo LINEAR REGRESSIONo LOGISTIC REGRESSIONo LSTMo MEAN SHIFTo MOBILENETo MONTE CARLO ALGORITHMo MULTIMODAL PARALLEL NETWORKo NAIVE BAYES CLASSIFIERSo PROXIMAL POLICY OPTIMIZATIONo PRINCIPAL COMPONENT ANALYSISo Q-LEARNINGo RANDOM FORESTSo RECURRENT NEURAL NETWORKo RESNETo SPATIAL TEMPORAL GRAPH CONVOLUTIONAL NETWORKSo STOCHASTIC GRADIENT DESCENTo SUPPORT VECTOR MACHINEo WAVENETo XGBOOST
Detailed Information
| Author: | Murat Durmus |
|---|---|
| Publication Year: | 2023 |
| Pages: | 192 |
| Language: | English |
| File Size: | 1.9 |
| Format: | |
| Price: | FREE |
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