![[Review] AI and Machine Learning for Coders (Laurence Moroney) Summarized.](https://episodes.castos.com/660078c6833215-59505987/images/2661473/c1a-085k3-jppjwr1pb768-ga7hgd.jpg)
Show Notes
AI and Machine Learning for Coders (Laurence Moroney)
- Amazon USA Store: https://www.amazon.com/dp/B0BSMSV1NL?tag=9natree-20
- Amazon Worldwide Store: https://global.buys.trade/AI-and-Machine-Learning-for-Coders-Laurence-Moroney.html
- Apple Books: https://books.apple.com/us/audiobook/ai-and-machine-learning-for-coders-a-programmers/id1669422429?itsct=books_box_link&itscg=30200&ls=1&at=1001l3bAw&ct=9natree
- eBay: https://www.ebay.com/sch/i.html?_nkw=AI+and+Machine+Learning+for+Coders+Laurence+Moroney+&mkcid=1&mkrid=711-53200-19255-0&siteid=0&campid=5339060787&customid=9natree&toolid=10001&mkevt=1
- Read more: https://english.9natree.com/read/B0BSMSV1NL/
#TensorFlowandKeras #Convolutionalneuralnetworks #Tokenizationandembeddings #LSTMsequencemodeling #TensorFlowServing #AIandMachineLearningforCoders
AI and Machine Learning for Coders: A Programmers Guide to Artificial Intelligence is a practical technical introduction by Laurence Moroney, an educator known for developer-facing TensorFlow instruction. Rather than beginning with the mathematical formalism often used in machine learning texts, it approaches the field through runnable code, model-building workflows, and recognizable application types. The book is aimed primarily at programmers who already understand software development and want a usable entry point into machine learning. Its central framework is TensorFlow and its high-level Keras APIs. From that base, Moroney introduces neural-network concepts and develops examples in computer vision, language processing, and sequential data. It also extends beyond training notebooks by addressing how models can be used in web, cloud, mobile, and embedded contexts. The purpose is not to provide a comprehensive theoretical account of artificial intelligence. Instead, it shows how developers can translate familiar programming habits into the experimental workflow of preparing data, training models, evaluating results, and deploying inference. This practical orientation defines both the books accessibility and its limits.
- Amazon USA Store: https://www.amazon.com/dp/B0BSMSV1NL?tag=9natree-20
- Amazon Worldwide Store: https://global.buys.trade/AI-and-Machine-Learning-for-Coders-Laurence-Moroney.html
- Apple Books: https://books.apple.com/us/audiobook/ai-and-machine-learning-for-coders-a-programmers/id1669422429?itsct=books_box_link&itscg=30200&ls=1&at=1001l3bAw&ct=9natree
- eBay: https://www.ebay.com/sch/i.html?_nkw=AI+and+Machine+Learning+for+Coders+Laurence+Moroney+&mkcid=1&mkrid=711-53200-19255-0&siteid=0&campid=5339060787&customid=9natree&toolid=10001&mkevt=1
- Read more: https://english.9natree.com/read/B0BSMSV1NL/
#TensorFlowandKeras #Convolutionalneuralnetworks #Tokenizationandembeddings #LSTMsequencemodeling #TensorFlowServing #AIandMachineLearningforCoders
AI and Machine Learning for Coders: A Programmers Guide to Artificial Intelligence is a practical technical introduction by Laurence Moroney, an educator known for developer-facing TensorFlow instruction. Rather than beginning with the mathematical formalism often used in machine learning texts, it approaches the field through runnable code, model-building workflows, and recognizable application types. The book is aimed primarily at programmers who already understand software development and want a usable entry point into machine learning. Its central framework is TensorFlow and its high-level Keras APIs. From that base, Moroney introduces neural-network concepts and develops examples in computer vision, language processing, and sequential data. It also extends beyond training notebooks by addressing how models can be used in web, cloud, mobile, and embedded contexts. The purpose is not to provide a comprehensive theoretical account of artificial intelligence. Instead, it shows how developers can translate familiar programming habits into the experimental workflow of preparing data, training models, evaluating results, and deploying inference. This practical orientation defines both the books accessibility and its limits.
