Deep Learning with TensorFlow: Build Neural Networks

本课程是 AI Deep Learning Projects with TensorFlow 专项课程的一部分

位教师：EDUCBA

访问权限由 New York State Department of Labor 提供

2个模块

深入了解一个主题并学习基础知识。

5 小时完成

灵活的计划

自行安排学习进度

2个模块

深入了解一个主题并学习基础知识。

5 小时完成

灵活的计划

自行安排学习进度

您将学到什么

Build and train neural networks in TensorFlow with parameter initialization.
Implement CNNs for image processing and real-world dataset classification.
Apply transfer learning to adapt pre-trained models for specialized tasks.

您将获得的技能

要了解的详细信息

可分享的证书

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作业

6 项作业

授课语言：英语（English）

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积累特定领域的专业知识

本课程是 AI Deep Learning Projects with TensorFlow 专项课程专项课程的一部分

在注册此课程时，您还会同时注册此专项课程。

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获得对主题或工具的基础理解
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获得可共享的职业证书

该课程共有2个模块

By the end of this course, learners will be able to explain the fundamentals of neural networks, apply TensorFlow to build and train models, implement convolutional neural networks for image processing, and adapt transfer learning strategies for real-world applications.

This course is designed to help learners bridge the gap between theory and practice in deep learning. Starting with perceptrons and core neural network principles, participants will gain hands-on experience in building models, initializing parameters effectively, and processing image data through CNNs. Moving forward, they will learn to classify real-world datasets like dogs vs. cats and master advanced transfer learning techniques to optimize pre-trained models for specialized tasks. Unlike other tutorials, this course uniquely combines step-by-step TensorFlow implementation with conceptual clarity, ensuring learners not only follow code but also understand the reasoning behind each decision. Whether aiming to enhance AI career prospects or apply deep learning in projects, learners will leave equipped with the skills to design, train, and deploy robust neural network models confidently.

his module introduces learners to the fundamentals of deep learning by combining theoretical foundations with practical implementations in TensorFlow. It covers perceptrons, neural network construction, model initialization, and convolutional neural networks, enabling learners to understand and build the backbone of modern AI applications.

涵盖的内容

8个视频3个作业

8个视频总计101分钟

Overview of DLUT 9分钟
Scenario of Perceptron 16分钟
Creating Neural Network Using TensorFlow 11分钟
Perform Multiclass Classification 10分钟
Initializing the Model 13分钟
Initializing the Model Continued 14分钟
Image Processing Using CNN 15分钟
Convolution Intuition 12分钟

3个作业总计50分钟

Foundations of Deep Learning – Graded Quiz 30分钟
The Neural Network Blueprint 10分钟
Building and Training Your First Models 10分钟

This module explores advanced applications of deep learning, from classifying real-world images to applying transfer learning for efficient and scalable model development. Learners will practice handling image datasets, implementing data generators, and leveraging pre-trained models to optimize performance on specialized tasks.