|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/progressive-deep-learning-with-keras-in-practice/
课程评论：没有评论
课程名称：Keras 实践中的渐进式深度学习 课程概述： 本课程介绍了 Keras，一个用 Python 编写的开源神经网络库，旨在快速、高效地训练深度学习模型。Keras 是一个轻量且高度模块化的框架，能够在 CPU 和 GPU 上运行，能帮助用户在最短时间内实现想法。由于其易用性，Keras 在短时间内获得了广泛的关注。 本课程采取逐步实践的方法，共分为三个部分，涵盖图像处理、自然语言处理（NLP）和强化学习项目。最初，学员将学习反向传播的基本概念，安装和配置 Keras，并理解回调函数的使用，以自定义训练过程。学员将构建、训练和运行全连接网络、卷积神经网络（CNN）和循环神经网络（RNN），同时解决使用图像、文本和时间序列的监督和非监督学习问题。 随着课程的深入，学员将应用自编码器和生成对抗网络（GAN）的相关概念、直观理解和应用。课程的最后，学员将完成图像处理、NLP 和强化学习的项目，实现尖端深度学习模型。 课程内容及结构： 该课程包含三个完整的子课程，旨在提供全面的训练。 1. Keras 深度学习基础课程：介绍如何用 Python 实现深度学习神经网络，帮助学员在实践中掌握 Keras 的基本知识。 2. Keras 高级深度学习课程：深入探讨深度学习及其流行框架 Keras，介绍神经网络和优化技术，及卷积和循环神经网络的使用，涵盖推荐系统、风格迁移、小数据集训练技巧、高级概念如生成对抗网络。 3. Keras 深度学习项目课程：围绕图像处理、NLP 和强化学习的实际项目，展示如何利用 Keras 构建和训练高性能的深度学习模型，帮助学员掌握深度学习的进阶概念及其实施。 作者介绍： 本课程由多位领域专家授课，包括软件执行官 Antonio Gulli，技术研究主管 Sujit Pal，研究工程师 Philippe Remy 和开发者 Tsvetoslav Tsekov，他们在深度学习及相关技术方面拥有丰富的经验和实践背景。 通过本课程，学员将获取实施高效深度学习模型所需的知识，可以解决多种问题。
Keras is an (Open source Neural Network library written in Python) Deep Learning library for fast, efficient training of Deep Learning models. It is a minimal, highly modular framework that runs on both CPUs and GPUs, and allows you to put your ideas into action in the shortest possible time. Because it is lightweight and very easy to use, Keras has gained quite a lot of popularity in a very short time.This comprehensive 3-in-1 course takes a step-by-step practical approach to implement fast and efficient Deep Learning models: Projects on Image Processing, NLP, and Reinforcement Learning. Initially, you'll learn backpropagation, install and configure Keras and understand callbacks and for customizing the process. You'll build, train, and run fully-connected, Convolutional and Recurrent Neural Networks. You'll also solve Supervised and Unsupervised learning problems using images, text and time series. Moving further, you'll use concepts, intuitive understating and applications of Autoencoders and Generative Adversarial Networks. Finally, you'll build projects on Image Processing, NLP, and Reinforcement Learning and build cutting-edge Deep Learning models in a simple, easy to understand way.Towards the end of this course, you'll get to grips with the basics of Keras to implement fast and efficient Deep Learning models: Projects on Image Processing, NLP, and Reinforcement Learning.Contents and OverviewThis training program includes 3 complete courses, carefully chosen to give you the most comprehensive training possible.The first course, Deep Learning with Keras, covers implementing deep learning neural networks with Python. Keras is a high-level neural network library written in Python and runs on top of either Theano or TensorFlow. It is a minimal, highly modular framework that runs on both CPUs and GPUs, and allows you to put your ideas into action in the shortest possible time. This course will help you get started with the basics of Keras, in a highly practical manner.The second course, Advanced Deep Learning with Keras, covers Deep learning with one of it's most popular frameworks: Keras. This course provides a comprehensive introduction to deep learning. We start by presenting some famous success stories and a brief recap of the most common concepts found in machine learning. Then, we introduce neural networks and the optimization techniques to train them. We'll show you how to get ready with Keras API to start training deep learning models, both on CPU and on GPU. Then, we present two types of neural architecture: convolutional and recurrent neural networks. First, we present a well-known use case of deep learning: recommender systems, where we try to predict the "rating" or "preference" that a user would give to an item. Then, we introduce an interesting subject called style transfer. Deep learning has this ability to transform images based on a set of inputs, so we'll morph an image with a style image to combine them into a very realistic result. In the third section, we present techniques to train on very small datasets. This comprises transfer learning, data augmentation, and hyperparameter search, to avoid overfitting and to preserve the generalization property of the network. Finally, we complete this course by what Yann LeCun, Director at Facebook, considered as the biggest breakthrough in Machine Learning of the last decade: Generative Adversarial Networks. These networks are amazingly good at capturing the underlying distribution of a set of images to generate new images.The third course, Keras Deep Learning Projects, covers Projects on Image Processing, NLP, and Reinforcement Learning. This course will show you how to leverage the power of Keras to build and train high performance, high accuracy deep learning models, by implementing practical projects in real-world domains. Spanning over three hours, this course will help you master even the most advanced concepts in deep learning and how to implement them with Keras. You will train CNNs, RNNs, LSTMs, Autoencoders and Generative Adversarial Networks using real-world training datasets. These datasets will be from domains such as Image Processing and Computer Vision, Natural Language Processing, Reinforcement Learning and more. By the end of this highly practical course, you will be well-versed with deep learning and its implementation with Keras. By the end of this course, you will have all the knowledge you need to train your own deep learning models to solve different kinds of problems.Towards the end of this course, you'll get to grips with the basics of Keras to implement fast and efficient Deep Learning models: Projects on Image Processing, NLP, and Reinforcement Learning.About the AuthorsAntonio Gulli is a software executive and business leader with a passion for establishing and managing global technological talent, innovation, and execution. He is an expert in search engines, online services, machine learning, information retrieval, analytics, and cloud computing. So far, he has been lucky enough to gain professional experience in four different countries in Europe and has managed people in six different countries in Europe and America. Antonio served as CEO, GM, CTO, VP, director, and site lead in multiple fields ranging from publishing (Elsevier) to consumer internet (Ask and Tiscali) and high-tech R & D (Microsoft and Google).Sujit Pal is a technology research director at Elsevier Labs, working on building intelligent systems around research content and metadata. His primary interests are information retrieval, ontologies, natural language processing, machine learning, and distributed processing. He is currently working on image classification and similarity using deep learning models. Prior to this, he worked in the consumer healthcare industry, where he helped build ontology-backed semantic search, contextual advertising, and EMR data processing platforms. He writes about technology on his blog at Salmon Run.Philippe Remy is a research engineer and entrepreneur working on deep learning and living in Tokyo, Japan. As a research engineer, Philippe reads scientific papers and implements artificial intelligence algorithms related to handwriting character recognition, time series analysis, and natural language processing. As an entrepreneur, his vision is to bring a meaningful and transformative impact on society with the ultimate goal of enhancing the overall quality of life and pushing the limits of what is considered possible today. Philippe contributes to different open source projects related to deep learning and fintech (github/philipperemy). You can visit Philippe Remy's blog on philipperemy.Tsvetoslav Tsekov has worked for 5 years on various software development projects - desktop applications, backend applications, WinCE embedded software, RESTful APIs. He then became exceedingly interested in Artificial Intelligence and particularly Deep Learning. After receiving his Deep Learning Nanodegree, he has worked on numerous projects - Image Classification, Sports Results Prediction, Fraud Detection, and Machine Translation. He is also very interested in General AI research and is always trying to stay up to date with the cutting-edge developments in the field.
