Analyze and Build Deep Learning Models with TensorFlow

位教师：EDUCBA

访问权限由 Coursera Learning Team 提供

2个模块

深入了解一个主题并学习基础知识。

初级等级

推荐体验

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。

初级等级

推荐体验

5 小时完成

灵活的计划

自行安排学习进度

您将学到什么

Explain deep learning fundamentals, neural network architectures, and learning mechanisms.
Build and train end-to-end deep learning models using TensorFlow and Keras.
Prepare datasets, evaluate multiple models, and select optimal solutions for classification tasks.

您将获得的技能

Artificial Intelligence and Machine Learning (AI/ML)

要了解的详细信息

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作业

6 项作业

授课语言：英语（English）

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该课程共有2个模块

By the end of this course, learners will be able to explain core deep learning concepts, analyze neural network architectures, apply activation and optimization techniques, and implement end-to-end deep learning models using TensorFlow and Keras. Learners will also be able to prepare datasets, identify key data components, and evaluate multiple models to select appropriate solutions for classification problems.

This course is designed to help learners build a strong conceptual foundation in deep learning while steadily transitioning into practical, hands-on implementation. Through a structured progression from neural network fundamentals to real-world model development, learners gain clarity on how data flows through networks, how learning occurs, and how modern frameworks simplify complex computations. What makes this course unique is its balanced emphasis on theory and practice. Instead of treating deep learning as a black box, the course demystifies internal mechanisms such as activation functions, backpropagation, and model evaluation. Learners benefit by developing job-ready skills aligned with industry tools, enabling them to confidently design, implement, and assess deep learning models for real-world applications.

This module introduces the fundamental concepts of deep learning, focusing on neural network architecture, data flow across layers, activation functions, and optimization techniques. Learners gain a conceptual foundation necessary to understand how deep learning models learn complex patterns and how training processes such as backpropagation improve model performance.

涵盖的内容

5个视频3个作业

This module focuses on practical implementation of deep learning models using industry-standard frameworks such as TensorFlow and Keras. Learners explore environment setup, neural package implementation, dataset preparation, feature engineering, and model evaluation to build and assess real-world deep learning applications.