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Python for AI, Machine Learning, and Deep Learning. Build practical AI applications with XGBoost, TensorFlow, PyTorch, transformers, and fine-tuning Vivian Aranha

Książka w języku angielskim angielski
Python for AI, Machine Learning, and Deep Learning. Build practical AI applications with XGBoost, TensorFlow, PyTorch, transformers, and fine-tuning Vivian Aranha - okladka książki

Python for AI, Machine Learning, and Deep Learning. Build practical AI applications with XGBoost, TensorFlow, PyTorch, transformers, and fine-tuning Vivian Aranha - okladka książki

Python for AI, Machine Learning, and Deep Learning. Build practical AI applications with XGBoost, TensorFlow, PyTorch, transformers, and fine-tuning Vivian Aranha - audiobook MP3

Python for AI, Machine Learning, and Deep Learning. Build practical AI applications with XGBoost, TensorFlow, PyTorch, transformers, and fine-tuning Vivian Aranha - audiobook CD

Autor:
Vivian Aranha
Wydawnictwo:
Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
Ocena:
Bądź pierwszym, który oceni tę książkę
Stron:
752
Dostępne formaty:
     PDF
     ePub

Ebook
125,10 zł 139,00 zł (-10%)
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Do przechowalni

Learning modern AI often feels fragmented: Python in one resource, mathematics in another, and deep learning or transformers somewhere else. This book brings those foundations into one practical progression, helping you connect the concepts behind machine learning with the code used to build models.

You will start with Python programming, NumPy, Pandas, data visualization, exploratory data analysis, linear algebra, calculus, probability, and statistics. From there, you will build supervised learning skills across regression, classification, k-NN, feature engineering, model evaluation, cross-validation, and hyperparameter tuning. You will then work with ensemble methods including random forests, gradient boosting, XGBoost, LightGBM, and CatBoost, while learning to handle imbalanced data and justify modeling decisions.

The final part moves into neural networks and deep learning with TensorFlow, Keras, and PyTorch. You will build CNNs for image classification, explore RNNs, LSTMs, GRUs, word embeddings, and sequence-to-sequence models, and understand attention and transformer architectures through BERT and GPT examples. You will finish by applying transfer learning and fine-tuning to computer vision and NLP tasks, supported by projects that turn theory into practical AI and machine learning skills for real-world problem solving.

O autorze książki

Vivian Aranha is an AI educator, technology leader, and founder of School of AI, with over 20 years of industry experience. He earned a Bachelor's degree in Information Technology in 2004 and a Master's degree in Computer Science in 2006. His career spans web technologies, mobile app development for iOS and Android, blockchain solutions, and AI systems and applications.
Vivian has worked with Fortune 500 organizations, including The Washington Post, Delta Air Lines, and IBM. An instructor since 2009, he has trained professionals worldwide and now teaches AI globally. His courses have attracted over 2.5 million enrollments, with more than 500,000 students learning through School of AI, Udemy, Skool, and Maven.

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