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Hands-on Machine Learning with Python (Softcover)
Implement Neural Network Solutions with Scikit-learn and PyTorch
First Edition - Erschienen 06.03.2022 - Kartoniert, 356 Seiten, 254mm x 178mm x 20mm, Sprache(n): eng
Print on Demand. Lieferbar innerhalb von 7 bis 10 Tagen
Explains machine learning process through validation, evaluation, hyperparameter tuning and regularizationDiscusses neural network architectures for predicting sequences in the form of Recurrent Neural NetworksCovers projects with an end-to-end solution with neural networks using Pytorch
Mehr anzeigenExplains machine learning process through validation, evaluation, hyperparameter tuning and regularizationDiscusses neural network architectures for predicting sequences in the form of Recurrent Neural NetworksCovers projects with an end-to-end solution with neural networks using Pytorch
- Produktart:
- 📚 Bücher
- Anbieter:
- MARZIES Buch- und Medienhandel
- Bestell-Nr.:
- A42720268
- Kategorie(n):
- Programmier- und Skriptsprachen, allgemein | Künstliche Intelligenz | Maschinelles Lernen
- ISBN | EAN:
- 1484279204 | 9781484279205
- Stichworte:
- CNN, DataScience, Keras, LSTM, NumPy, PANDAS, PyTorch, Python, RNN, machinelearning, matplotlib, tensorflow…
- Zahlungsarten:
- Vorauskasse, PayPal
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Here is the perfect comprehensive guide for readers with basic to intermediate level knowledge of machine learning and deep learning. It introduces tools such as NumPy for numerical processing, Pandas for panel data analysis, Matplotlib for visualization, Scikit-learn for machine learning, and Pytorch for deep learning with Python. It also serves as a long-term reference manual for the practitioners who will find solutions to commonly occurring scenarios.
The book is divided into three sections. The first section introduces you to number crunching and data analysis tools using Python with in-depth explanation on environment configuration, data loading, numerical processing, data analysis, and visualizations. The second section covers machine learning basics and Scikit-learn library. It also explains supervised learning, unsupervised learning, implementation, and classification of regression algorithms, and ensemble learning methods in an easy manner with theoreticaland practical lessons. The third section explains complex neural network architectures with details on internal working and implementation of convolutional neural networks. The final chapter contains a detailed end-to-end solution with neural networks in Pytorch.
After completing Hands-on Machine Learning with Python, you will be able to implement machine learning and neural network solutions and extend them to your advantage.
What You'll LearnReview data structures in NumPy and Pandas
Demonstrate machine learning techniques and algorithm
Understand supervised learning and unsupervised learning
Examine convolutional neural networks and Recurrent neural networks
Get acquainted with scikit-learn and PyTorch
Predict sequences in recurrent neural networks and long short term memory
Who This Book Is For
Data scientists, machine learning engineers, and software professionals with basic skills in Python programming.
The book is divided into three sections. The first section introduces you to number crunching and data analysis tools using Python with in-depth explanation on environment configuration, data loading, numerical processing, data analysis, and visualizations. The second section covers machine learning basics and Scikit-learn library. It also explains supervised learning, unsupervised learning, implementation, and classification of regression algorithms, and ensemble learning methods in an easy manner with theoreticaland practical lessons. The third section explains complex neural network architectures with details on internal working and implementation of convolutional neural networks. The final chapter contains a detailed end-to-end solution with neural networks in Pytorch.
After completing Hands-on Machine Learning with Python, you will be able to implement machine learning and neural network solutions and extend them to your advantage.
What You'll LearnReview data structures in NumPy and Pandas
Demonstrate machine learning techniques and algorithm
Understand supervised learning and unsupervised learning
Examine convolutional neural networks and Recurrent neural networks
Get acquainted with scikit-learn and PyTorch
Predict sequences in recurrent neural networks and long short term memory
Who This Book Is For
Data scientists, machine learning engineers, and software professionals with basic skills in Python programming.
Ashwin Pajankar holds a Master of Technology from IIIT Hyderabad, and has over 25 years of programming experience. He started his journey in programming and electronics with BASIC programming language and is now proficient in Assembly programming, C, C++, Java, Shell Scripting, and Python. Other technical experience includes single board computers such as Raspberry Pi and Banana Pro, and Arduino. He is currently a freelance online instructor teaching programming bootcamps to more than 60,000 students from tech companies and colleges. His Youtube channel has an audience of 10000 subscribers and he has published more than 15 books on programming and electronics with many international publications.
Aditya Joshi has worked in data science and machine learning engineering roles since the completion of his MS (By Research) from IIIT Hyderabad. He has conducted tutorials, workshops, invited lectures, and full courses for students and professionals who want to move tothe field of data science. His past academic research publications include works on natural language processing, specifically fine grain sentiment analysis and code mixed text. He has been the organizing committee member and program committee member of academic conferences on data science and natural language processing.
Aditya Joshi has worked in data science and machine learning engineering roles since the completion of his MS (By Research) from IIIT Hyderabad. He has conducted tutorials, workshops, invited lectures, and full courses for students and professionals who want to move tothe field of data science. His past academic research publications include works on natural language processing, specifically fine grain sentiment analysis and code mixed text. He has been the organizing committee member and program committee member of academic conferences on data science and natural language processing.
