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所在平台: Udemy |
课程主页: https://www.udemy.com/course/performance-tuning-deep-learning-models-master-class/
课程评论：没有评论
**课程名称：** 深度学习模型性能调优大师课 **课程概述：** 本课程由机器学习工程师 Mike West 教授，旨在帮助开发者、机器学习工程师和数据科学家提升深度学习模型的性能。课程内容聚焦于三个核心目标：加速学习、提高模型泛化能力以及优化最终预测精度。 **课程亮点：** * **实战导向：** 课程采用“边做边学”的方式，提供实践操作的指导和示例，鼓励学员亲自动手实践。 * **内容全面：** * 深入讲解过拟合问题及其多种正则化技术（权重正则化、权重约束、激活正则化）。 * 教授如何通过优化随机梯度下降（SGD）、批次大小、损失函数、学习率以及梯度裁剪来加速学习，避免梯度爆炸。 * 详细介绍 Dropout、添加噪声和早停等技术以对抗过拟合。 * 讲解如何结合多个模型进行预测，包括水平集成和快照集成等集成学习技术。 * 指导学员诊断模型训练中的问题，如欠拟合、过拟合、过早收敛等，并加速模型训练过程。 * 帮助学员诊断最终模型的方差过高问题，并提升平均预测能力。 * **专家经验：** 讲师 Mike West 在机器学习应用领域拥有丰富的经验，曾与多家公司合作，并为微软完成项目，同时也是 Udemy 上的热门课程讲师。 * **学员评价：** 课程获得学员高度评价，被认为是了解模型输入（如 epoch 数量、批次大小、隐藏层和节点数）对结果准确性影响的优秀课程，以及关于神经网络调优的最佳课程之一。 **目标学员：** * 希望提升深度学习模型性能的开发者、机器学习工程师和数据科学家。 * 具备 Python、Keras 和机器学习基础知识的学员（中级至高级）。 **学习目标：** * 掌握深度学习模型性能调优的关键技术。 * 能够有效解决过拟合和欠拟合等常见问题。 * 能够应用集成学习技术提升模型预测能力。 * 能够独立诊断和解决模型训练中的性能瓶颈。 **学习建议：** 为了最大化学习效果，强烈建议学员跟随课程中的所有示例进行操作练习，而非仅仅观看。
** Mike's courses are popular with many of our clients." Josh Gordon, Developer Advocate, Google **Great course to see the impacts of model inputs, such as the quantity of epochs, batch size, hidden layers, and nodes, on the accuracy of the results. - KevinBest course on neural network tuning I've taken thus far. -Jazon SamillanoVery nice explanation. - MohammadWelcome to Performance Tuning Deep Learning Models Master Class.Deep learning neural networks have become easy to create. However, tuning these models for maximum performance remains something of a challenge for most modelers. This course will teach you how to get results as a machine learning practitioner. This is a step-by-step course in getting the most out of deep learning models on your own predictive modeling projects. My name is Mike West and I'm a machine learning engineer in the applied space. I've worked or consulted with over 50 companies and just finished a project with Microsoft. I've published over 50 courses and this is 53 on Udemy. If you're interested in learning what the real-world is really like then you're in good hands.This course was designed around three main activities for getting better results with deep learning models: better or faster learning, better generalization to new data, and better predictions when using final models.Who is this course for? This course is for developers, machine learning engineers and data scientists that want to enhance the performance of their deep learning models. This is an intermediate level to advanced level course. It's highly recommended the learner be proficient with Python, Keras and machine learning. What are you going to Learn? An introduction to the problem of overfitting and a tour of regularization techniquesAccelerate learning through better configured stochastic gradient descent batch size, loss functions, learning rates, and to avoid exploding gradients via gradient clipping.Learn to combat overfitting and an introduction of regularization techniques. Reduce overfitting by updating the loss function using techniques such as weight regularization, weight constraints, and activation regularization.Effectively apply dropout, the addition of noise, and early stopping.Combine the predictions from multiple models and a tour of ensemble learning techniques.Diagnose poor model training and problems such as premature convergence and accelerate the model training process.Combine the predictions from multiple models saved during a single training run with techniques such as horizontal ensembles and snapshot ensembles.Diagnose high variance in a final model and improve the average predictive skill.This course is a hands on-guide. It is a playbook and a workbook intended for you to learn by doing and then apply your new understanding to your own deep learning Keras models. To get the most out of the course, I would recommend working through all the examples in each tutorial. If you watch this course like a movie you'll get little out of it. In the applied space machine learning is programming and programming is a hands on-sport. Thank you for your interest in Performance Tuning Deep Learning Models Master Class.Let's get started!
