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所在平台: Udemy |
课程主页: https://www.udemy.com/course/a-deep-dive-in-deep-learning-ocean-with-pytorch-tensorflow/
课程评论：没有评论
课程名称：《深度学习海洋深潜：Pytorch与TensorFlow》 课程概述： 本课程全面深入地探讨深度学习及其在人工智能和数据科学领域的革命性影响。深度学习教会计算机以人脑的方式处理数据。课程将涵盖深度学习模型背后的理论与直觉，并在Pytorch和TensorFlow中实施所有深度学习模型，旨在提供以实践为导向的解释。学习本课程不需要任何先备知识，我们将从基础开始教授所有内容，符合求职导向。 课程结构包括： - 课程介绍 - Google Colab介绍 - Python基础课程 - 数据预处理 - 回归分析 - 逻辑回归 - 神经网络简介与深度学习 - 激活函数 - 损失函数 - 反向传播 - 用于回归分析的神经网络 - 用于分类的神经网络 - Dropout正则化与批规范化 - 优化器 - 向神经网络添加自定义损失函数与自定义层 - 卷积神经网络 (CNN) - 一维CNN - 在CNN中设置早停准则 - 循环神经网络 (RNN) - 长短时记忆网络 (LSTM) - 双向LSTM - 生成对抗网络 (GAN) - DCGANs - 自动编码器 - LSTM自动编码器 - 变分自动编码器 - 神经风格转移 - 变压器 - 视觉变压器 - 时间序列变压器 - K均值聚类 - 主成分分析 - Pytorch与TensorFlow中深度学习模型的实现 本课程适合所有对深度学习感兴趣的人士，提供丰富的理论和实践内容，助力学习者在人工智能领域成长。
Course ContentsDeep Learning and revolutionized Artificial Intelligence and data science. Deep Learning teaches computers to process data in a way that is inspired by the human brain.This is complete and comprehensive course on deep learning. This course covers the theory and intuition behind deep learning models and then implementing all the deep learning models both in Pytorch and Tensor flow.Practical Oriented explanations Deep Learning Models with implementation both in Pytorch and Tensor Flow.No need of any prerequisites. I will teach you everything from scratch.Job Oriented StructureSections of the Course· Introduction of the Course· Introduction to Google Colab· Python Crash Course· Data Preprocessing· Regression Analysis· Logistic Regression· Introduction to Neural Networks and Deep Learning· Activation Functions· Loss Functions· Back Propagation· Neural Networks for Regression Analysis· Neural Networks for Classification· Dropout Regularization and Batch Normalization· Optimizers· Adding Custom Loss Function and Custom Layers to Neural Networks· Convolutional Neural Network (CNN)· One Dimensional CNN· Setting Early Stopping Criterion in CNN· Recurrent Neural Network (RNN)· Long Short-Term Memory (LSTM) Network· Bidirectional LSTM· Generative Adversarial Network (GAN)· DCGANs· Autoencoders· LSTM Autoencoders· Variational Autoencoders· Neural Style Transfer· Transformers· Vision Transformer· Time Series Transformers. K-means Clustering. Principle Component Analysis. Deep Learning Models with implementation both in Pytorch and Tensor Flow.
