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81061201
Advanced Deep Learning with Python: Design and implement advanced next-generation AI solutions using TensorFlow and PyTorch
86% of respondents would recommend this to a friend
Item #:
81061201
MYR 322
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What Stands Out
Comprehensive Curriculum
Offers in-depth coverage of advanced deep learning techniques, empowering learners to master complex algorithms and architectures critical for cutting-edge AI applications.
Practical Applications
Includes real-world projects and case studies, ensuring that users can apply theoretical knowledge to solve tangible problems in various industries.
Expert Guidance
Delivered by industry experts, providing insights and best practices that are often overlooked in standard tutorials, enhancing the overall learning experience.
Maklumat produk
- Gain expertise in advanced deep learning domains such as neural networks, meta-learning, graph neural networks, and memory augmented neural networks using the Python ecosystemKey FeaturesGet to grips with building faster and more robust deep learning architecturesInvestigate and train convolutional neural network (CNN) models with GPU-accelerated libraries such as TensorFlow and PyTorchApply deep neural networks (DNNs) to computer vision problems, NLP, and GANsBook DescriptionIn order to build robust deep learning systems, you'll need to understand everything from how neural networks work to training CNN models. In this book, you'll discover newly developed deep learning models, methodologies used in the domain, and their implementation based on areas of application.You'll start by understanding the building blocks and the math behind neural networks, and then move on to CNNs and their advanced applications in computer vision. You'll also learn to apply the most popular CNN architectures in object detection and image segmentation. Further on, you'll focus on variational autoencoders and GANs. You'll then use neural networks to extract sophisticated vector representations of words, before going on to cover various types of recurrent networks, such as LSTM and GRU. You'll even explore the attention mechanism to process sequential data without the help of recurrent neural networks (RNNs). Later, you'll use graph neural networks for processing structured data, along with covering meta-learning, which allows you to train neural networks with fewer training samples. Finally, you'll understand how to apply deep learning to autonomous vehicles.By the end of this book, you'll have mastered key deep learning concepts and the different applications of deep learning models in the real world.What you will learnCover advanced and state-of-the-art neural network architecturesUnderstand the theory and math behind neural networksTrain DNNs and apply them to modern deep learning problemsUse CNNs for object detection and image segmentationImplement generative adversarial networks (GANs) and variational autoencoders to generate new imagesSolve natural language processing (NLP) tasks, such as machine translation, using sequence-to-sequence modelsUnderstand DL techniques, such as meta-learning and graph neural networksWho this book is forThis book is for data scientists, deep learning engineers and researchers, and AI developers who want to further their knowledge of deep learning and build innovative and unique deep learning projects. Anyone looking to get to grips with advanced use cases and methodologies adopted in the deep learning domain using real-world examples will also find this book useful. Basic understanding of deep learning concepts and working knowledge of the Python programming language is assumed.Table of ContentsThe Nuts and Bolts of Neural NetworksUnderstanding Convolutional NetworksAdvanced Convolutional NetworksObject Detection and Image SegmentationGenerative ModelsLanguage ModellingUnderstanding Recurrent NetworksSequence-to-Sequence Models and AttentionEmerging Neural Network DesignsMeta LearningDeep Learning for Autonomous Vehicles
| Publisher | Packt Publishing |
| Publication date | December 12, 2019 |
| Language | English |
| Print length | 468 pages |
| ISBN-10 | 178995617X |
| ISBN-13 | 978-1789956177 |
| Item Weight | 1.76 pounds (800 grams) |
| Dimensions | 7.5 x 1.06 x 9.25 inches (19.1 x 2.7 x 23.5 cm) |
Who Should Buy?
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Data Scientists
Ideal for data scientists wanting to deepen their understanding of deep learning concepts and enhance practical application skills.
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Machine Learning Enthusiasts
Great for hobbyists and learners who are passionate about AI and wish to explore advanced techniques in deep learning.
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AI Researchers
Beneficial for researchers aiming to implement cutting-edge deep learning solutions and stay updated with current trends.
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Beginners
Not suitable for those new to programming or data science, as prior knowledge is necessary to grasp advanced topics.
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Neural Networks Editorial Review
**** "Advanced Deep Learning with Python" emerges as a robust resource for individuals already familiar with the fundamentals of AI and Machine Learning. Targeting data scientists, deep learning engineers, and researchers, the book assumes some mathematical foundations, particularly in Linear Algebra and Statistics, and suggests readers have a basic understanding of Python. Initial impressions highlight the author’s adeptness in reviving essential mathematical concepts useful for neural networks in an accessible manner. The first chapter serves as a strong refresher for those whose skills may have dulled over time, offering clear examples, definitions, and diagrams that make re-learning both engaging and memorable. This accessibility is further complemented by practical programming examples—which are also available on GitHub—enabling readers to experiment with different datasets. The organization of the book allows for independent chapter focus, meaning readers can selectively dive into topics of interest without grappling with the entire text. Particularly valuable are the "Put it together" sections at the conclusion of each chapter, which provide excellent summarization and revision material. While the book generally garners praise for its depth and clarity, some readers may find portions of the code challenging if they lack fluency in Python. However, the author’s assurances that Python is an easy language to pick up could mitigate this concern for many learners. Overall, the book stands out as an excellent read for those eager to deepen their understanding of advanced deep learning strategies and the underlying math. It's recommended for anyone who has a foundational grasp of the prerequisites; for those lacking this background, it would be wise to engage with the relevant mathematical concepts beforehand. **
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Kebaikan
- Strong introductory section ideal for refreshing knowledge in Linear Algebra and Statistics.
- Practical code examples available on GitHub for hands-on experience.
- "Put it together" summaries enhance retention and understanding.
- Chapters are mostly independent, allowing for targeted reading.
- In-depth exploration of advanced topics, including text classification and BERT.
Keburukan
- Some code may be challenging for those not proficient in Python, although Python is generally easy to learn.
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MYR 322
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- Build faster and more robust deep learning architectures
- Train CNN models with GPU-accelerated libraries like TensorFlow and PyTorch
- Apply DNNs to computer vision problems, NLP, and GANs
- Understand advanced and state-of-the-art neural network architectures
- Learn DL techniques like meta-learning and graph neural networks
- Ideal for data scientists, deep learning engineers, AI developers, and anyone looking to explore advanced deep learning use cases
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