Troubleshooting Python Deep Learning
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Troubleshooting Python Deep Learning
Practical solutions to your problems while building Deep Learning models using CNN, LSTM, scikit-learn, and NumPy About This Video Discover the limitless use of building any application using Deep Learning and ensure its issues aren't a roadblock for your projects Problems are addressed with practical yet unique solutions that are easy to understand and implement Implement scikit-learn and NumPy, to resolve the common problems arising from Deep Learning models In Detail Building Deep Learning models with Python is a strenuous task and there are chances of getting stuck on specific tasks. When that happens, you usually end up searching for solutions and need to manually look for ways to resolve these problems. This wastes both time and effort, and may also lead to reduced performance of your Deep Learning system. After carefully analyzing the most popular errors or problems that arise while working on Deep Learning models, we have identified the most usable models used for classification in this course and provided practical yet unique solutions to each problem that are easy to understand and implement. You can either follow the entire course or directly jump into the section that covers a specific problem you're facing. Some of the common yet important issues we cover include errors while building and training Deep Learning with neural networks, especially without a specific framework. By the end of the course, you will be well-versed to tackle and troubleshoot any errors with your Deep Learning models. Downloading the example code for this course: You can download the example code files for this course on GitHub at the following link: https://github.com/PacktPublishing/Troubleshooting-Python-Deep-Learning . If you require support please email: [email protected].
Troubleshooting Python Machine Learning
"Troubleshooting Python Machine Learning is the answer. We have systematically researched common ML problems documented online around data wrangling, debugging models such as Random Forests and SVMs, and visualizing tricky results. We leverage statistics from Stack Overflow, Medium, and GitHub to get a cross-section of what data scientists struggle with. We have collated for you the top issues, such as retrieving the most important regression features and explaining your results after clustering, and their corresponding solutions. We present these case studies in a problem-solution format, making it very easy for you to incorporate this into your knowledge. Taking this course will help you to precisely debug your models and research pipelines, so you can focus on pitching new ideas and not fixing old bugs."--Resource description page.
Hands-On Deep Learning Architectures with Python
Author: Yuxi (Hayden) Liu
language: en
Publisher: Packt Publishing Ltd
Release Date: 2019-04-30
Concepts, tools, and techniques to explore deep learning architectures and methodologies Key FeaturesExplore advanced deep learning architectures using various datasets and frameworksImplement deep architectures for neural network models such as CNN, RNN, GAN, and many moreDiscover design patterns and different challenges for various deep learning architecturesBook Description Deep learning architectures are composed of multilevel nonlinear operations that represent high-level abstractions; this allows you to learn useful feature representations from the data. This book will help you learn and implement deep learning architectures to resolve various deep learning research problems. Hands-On Deep Learning Architectures with Python explains the essential learning algorithms used for deep and shallow architectures. Packed with practical implementations and ideas to help you build efficient artificial intelligence systems (AI), this book will help you learn how neural networks play a major role in building deep architectures. You will understand various deep learning architectures (such as AlexNet, VGG Net, GoogleNet) with easy-to-follow code and diagrams. In addition to this, the book will also guide you in building and training various deep architectures such as the Boltzmann mechanism, autoencoders, convolutional neural networks (CNNs), recurrent neural networks (RNNs), natural language processing (NLP), GAN, and more—all with practical implementations. By the end of this book, you will be able to construct deep models using popular frameworks and datasets with the required design patterns for each architecture. You will be ready to explore the potential of deep architectures in today's world. What you will learnImplement CNNs, RNNs, and other commonly used architectures with PythonExplore architectures such as VGGNet, AlexNet, and GoogLeNetBuild deep learning architectures for AI applications such as face and image recognition, fraud detection, and many moreUnderstand the architectures and applications of Boltzmann machines and autoencoders with concrete examples Master artificial intelligence and neural network concepts and apply them to your architectureUnderstand deep learning architectures for mobile and embedded systemsWho this book is for If you’re a data scientist, machine learning developer/engineer, or deep learning practitioner, or are curious about AI and want to upgrade your knowledge of various deep learning architectures, this book will appeal to you. You are expected to have some knowledge of statistics and machine learning algorithms to get the best out of this book