使用Python的深度学习和人工智能视频教程

神经网络，TensorFlow，ANN，CNN，RNN，LSTM，自动编码器，GAN，迁移学习，部署深度学习模型，这门综合课程涵盖了使用Python的深度学习和人工智能的最新进展。本课程专为初学者和高级学生设计，向您传授构建和部署深度学习模型所需的基本概念和实践技能。模块1:Python和深度学习简介Python编程语言概述深度学习和神经网络简介模块2:神经网络基础理解激活函数、损失函数、 和优化技术监督和非监督学习概述模块3:从scratch构建神经网络动手编码练习使用python从头构建简单的神经网络模块4:用于深度学习的TensorFlow 2.0 tensor flow 2.0及其特性概述用于深度学习的动手编码练习使用tensor flow实现深度学习模型模块5:高级神经网络架构研究不同的神经网络架构，如前馈、递归、 和卷积网络实现高级神经网络模型的动手编码练习模块6:卷积神经网络(CNNs)卷积神经网络及其应用概述动手编码练习实现用于图像分类和对象检测任务的CNNs模块7:递归神经网络(RNNs)[即将推出]递归神经网络及其应用概述动手编码练习实现用于时序数据(如时间序列和自然语言处理)的RNNs，2023 Python for Deep Learning and Artificial Intelligence

本课程结束时，您将对深度学习及其在人工智能中的应用有深刻的理解，并能够使用 这门课程对于任何想在人工智能领域寻求职业生涯或者只是在这个令人兴奋的领域扩展知识的人来说都是一笔宝贵的财富。

由Laxmi Kant | KGP Talkie创作
MP4 |视频:h264，1280×720 |音频:AAC，44.1 KHz，2声道
类型:电子教学|语言:英语|时长:145节课(17小时4分钟)|大小:5.98 GB

你会学到什么
Python编程语言基础
深度学习和神经网络的基本概念
如何使用Python从头开始构建神经网络
使用TensorFlow 2.0进行深度学习的高级技术
用于图像分类和目标检测的卷积神经网络
用于时序数据和自然语言处理的递归神经网络(RNNs)
用于生成新数据样本的生成对抗网络(GANs)
深度学习中的迁移学习
强化学习及其在人工智能中的应用
深度学习模型的部署选项
深度学习在人工智能中的应用，如计算机视觉、自然语言处理和语音识别
深度学习和人工智能的当前和未来趋势，以及伦理和社会影响。

要求
对编程概念和数学有基本了解
笔记本电脑或有互联网连接的电脑
愿意学习和探索令人兴奋的深度学习和人工智能领域

这门课程是给谁的
想要扩展他们在机器学习方面的知识和技能的数据科学家、分析师和工程师。
希望学习如何在生产环境中构建和部署机器学习模型的开发人员和程序员。
希望了解机器学习最新发展和应用的研究人员和学者。
希望学习如何应用机器学习来解决其组织中现实世界问题的商业专业人士和经理。
希望在机器学习方面获得坚实基础并追求数据科学或人工智能职业生涯的学生和应届毕业生。
任何对机器学习感到好奇，并希望了解更多关于其应用以及如何在行业中使用的人。
Neural Networks, TensorFlow, ANN, CNN, RNN, LSTM, Auto Encoders, GAN, Transfer Learning, Deploying Deep Learning Models

What you’ll learn
The basics of Python programming language
Foundational concepts of deep learning and neural networks
How to build a neural network from scratch using Python
Advanced techniques in deep learning using TensorFlow 2.0
Convolutional neural networks (CNNs) for image classification and object detection
Recurrent neural networks (RNNs) for sequential data such as time series and natural language processing
Generative adversarial networks (GANs) for generating new data samples
Transfer learning in deep learning
Reinforcement learning and its applications in AI
Deployment options for deep learning models
Applications of deep learning in AI, such as computer vision, natural language processing, and speech recognition
The current and future trends in deep learning and AI, as well as ethical and societal implications.

Requirements
Basic understanding of programming concepts and mathematics
A laptop or a computer with an internet connection
A willingness to learn and explore the exciting field of deep learning and artificial intelligence

Description
This comprehensive course covers the latest advancements in deep learning and artificial intelligence using Python. Designed for both beginner and advanced students, this course teaches you the foundational concepts and practical skills necessary to build and deploy deep learning models.Module 1: Introduction to Python and Deep LearningOverview of Python programming languageIntroduction to deep learning and neural networksModule 2: Neural Network FundamentalsUnderstanding activation functions, loss functions, and optimization techniquesOverview of supervised and unsupervised learningModule 3: Building a Neural Network from ScratchHands-on coding exercise to build a simple neural network from scratch using PythonModule 4: TensorFlow 2.0 for Deep LearningOverview of TensorFlow 2.0 and its features for deep learningHands-on coding exercises to implement deep learning models using TensorFlowModule 5: Advanced Neural Network ArchitecturesStudy of different neural network architectures such as feedforward, recurrent, and convolutional networksHands-on coding exercises to implement advanced neural network modelsModule 6: Convolutional Neural Networks (CNNs)Overview of convolutional neural networks and their applicationsHands-on coding exercises to implement CNNs for image classification and object detection tasksModule 7: Recurrent Neural Networks (RNNs)[Coming Soon]Overview of recurrent neural networks and their applicationsHands-on coding exercises to implement RNNs for sequential data such as time series and natural language processingBy the end of this course, you will have a strong understanding of deep learning and its applications in AI, and the ability to build and deploy deep learning models using Python and TensorFlow 2.0. This course will be a valuable asset for anyone looking to pursue a career in AI or simply expand their knowledge in this exciting field.

Who this course is for
Data scientists, analysts, and engineers who want to expand their knowledge and skills in machine learning.
Developers and programmers who want to learn how to build and deploy machine learning models in a production environment.
Researchers and academics who want to understand the latest developments and applications of machine learning.
Business professionals and managers who want to learn how to apply machine learning to solve real-world problems in their organizations.
Students and recent graduates who want to gain a solid foundation in machine learning and pursue a career in data science or artificial intelligence.
Anyone who is curious about machine learning and wants to learn more about its applications and how it is used in the industry.