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Deep Learning: Convolutional Neural Networks with TensorFlow

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Deep Learning: Convolutional Neural Networks with TensorFlow

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Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

4 hours to complete
3 weeks at 1 hour a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

4 hours to complete
3 weeks at 1 hour a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Understand the foundational concepts of Convolutional Neural Networks (CNNs) and their architecture

  • Apply CNN models to real-world image and text classification tasks using TensorFlow

  • Analyze the performance of CNNs and optimize them with techniques like data augmentation and batch normalization

  • Evaluate the effectiveness of transfer learning using pre-trained models on new datasets

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Recently updated!

October 2024

Assessments

1 assignment

Taught in English

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There are 4 modules in this course

In this module, we will introduce you to the author and the key objectives of the course. You will gain insights into the learning approach and understand the resources and prerequisites necessary to begin your learning journey. Additionally, this section outlines the topics and content that will be covered throughout the course.

What's included

2 videos

In this module, we will explore the fundamentals of Convolutional Neural Networks (CNNs), beginning with the core concept of convolution and its mathematical interpretation. You will learn how CNNs are structured and implemented, with hands-on applications using popular datasets like Fashion MNIST and CIFAR-10. Additionally, we'll cover advanced techniques such as data augmentation and batch normalization to enhance model accuracy.

What's included

12 videos

In this module, we will explore the fundamentals of Natural Language Processing (NLP), starting with how text can be represented as sequence data using embeddings. You will learn how to preprocess text data using practical coding examples, and then dive into applying Convolutional Neural Networks (CNNs) to text for sequence analysis. The module concludes with hands-on work on text classification using CNN models.

What's included

5 videos

In this module, we will introduce you to transfer learning and its application in computer vision. You will explore popular pre-trained models, learn to manage large datasets, and implement two different approaches to transfer learning. Through practical coding exercises, you'll apply these techniques with and without data augmentation to enhance your understanding of how transfer learning optimizes deep learning models for new tasks.

What's included

6 videos1 assignment

Instructor

Packt - Course Instructors
Packt
375 Courses14,912 learners

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Recommended if you're interested in Machine Learning

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