Deep Learning CNN: Convolutional Neural Networks with Python

使用 TensorFlow、CNN 进行图像识别和 CNN 进行对象检测来学习卷积神经网络。通过实时编码了解 CNN 与数据科学相关的概念和方法。

关于本视频

从易于理解、详尽、富有表现力的 75 多个视频以及详细的代码笔记本
中学习 结构化课程，具有扎实的基础知识，并与先进的实践概念一起前进
使用 Python 进行实际解释和实时编码，以构建您自己的应用程序

详细

卷积神经网络 (CNN) 被认为是计算机视觉领域的游戏规则改变者，尤其是在 2012 年 AlexNet 之后。它们现在无处不在，从音频处理到更高级的强化学习。因此，在数据科学的所有领域中，对 CNN 的理解几乎是不可避免的。通过本课程，您可以通过专家掌握数据科学中 CNN 的概念和实现，将您的职业生涯提升到一个新的水平。

本课程首先介绍并记下卷积神经网络 (CNN) 在数据科学中的重要性。然后，您将了解一些经典的计算机视觉技术，例如图像处理和对象检测。紧随其后的是深度神经网络，主题包括感知器和多层感知器。然后，您将继续深入学习 CNN。您将首先了解 CNN 的架构，然后了解 CNN 中的梯度下降，了解 TensorFlow、经典 CNN、迁移学习和 YOLO 案例研究。

最后，您将从事两个项目：神经风格迁移（使用 TensorFlow-hub）和人脸验证（使用 VGGFace2）。

在本课程结束时，您将了解使用真实数据集进行数据科学的 CNN 方法。除此之外，您将能够轻松地将计算机视觉中的概念和理论与 CNN 联系起来。

观众

本课程专为数据科学和深度学习的初学者设计。任何想要在数据科学中使用真实数据集学习 CNN、在实际项目中学习 CNN 及其实施并掌握他们的数据说话的人都将从本课程中受益匪浅。

不需要先验知识。你从基础开始，慢慢建立你对该主题的知识。愿意学习和实践只是这门课程的先决条件。

Released 08/2022
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 15h 27m | Size: 3.94 GB

Video description
Learn Convolution Neural Networks using TensorFlow, CNN for Image Recognition, and CNN for Object Detection. Understand the concepts and methodologies of CNNs with respect to data science with live coding throughout.

About This Video

Learn from easy-to-understand, exhaustive, expressive, 75+ videos along with detailed code notebooks
Structured course with solid basic understanding and moving ahead with the advanced practical concepts
Practical explanation and live coding with Python to build your own application
In Detail

Convolutional Neural Networks (CNNs) are considered game-changers in the field of computer vision, particularly after AlexNet in 2012. They are everywhere now, ranging from audio processing to more advanced reinforcement learning. So, the understanding of CNNs becomes almost inevitable in all fields of data science. With this course, you can take your career to the next level with an expert grip on the concepts and implementations of CNNs in data science.

The course starts with introducing and jotting down the importance of Convolutional Neural Networks (CNNs) in data science. You will then look at some classical computer vision techniques such as image processing and object detection. It will be followed by deep neural networks with topics such as perceptron and multi-layered perceptron. Then, you will move ahead with learning in-depth about CNNs. You will first look at the architecture of a CNN, then gradient descent in CNN, get introduced to TensorFlow, classical CNNs, transfer learning, and a case study with YOLO.

Finally, you will work on two projects: Neural Style Transfer (using TensorFlow-hub) and Face Verification (using VGGFace2).

By the end of this course, you will have understood the methodology of CNNs with data science using real datasets. Apart from this, you will easily be able to relate the concepts and theories in computer vision with CNNs.

Audience

This course is designed for beginners in data science and deep learning. Any individual who wants to learn CNNs with real datasets in data science, learn CNNs along with its implementation in realistic projects, and master their data speak will gain a lot from this course.

No prior knowledge is needed. You start from the basics and slowly build your knowledge of the subject. A willingness to learn and practice is just the prerequisite for this course.