Deep Learning- Convolutional Neural Networks with TensorFlow

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Deep Learning- Convolutional Neural Networks with TensorFlow

• Course Info
• Curriculum
• Instructors

Description

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

What you'll learn in this course?

• Build your subject-matter expertise
• This course is part of the Deep Learning with TensorFlow Specialization
• When you enroll in this course, you'll also be enrolled in this Specialization.

• Learn new concepts from industry experts
• Gain a foundational understanding of a subject or too

Course Curriculum

Welcome

• Introduction
• Outline

Convolutional Neural Networks (CNNs)

• What Is Convolution. (Part 1)
• What Is Convolution. (Part 2)
• What Is Convolution. (Part 3)
• Convolution on Color Images
• CNN Architecture
• CNN Code Preparation
• CNN for Fashion MNIST
• CNN for CIFAR-10
• Data Augmentation
• Batch Normalization
• Improving CIFAR-10 Results
• Suggestion Box

Natural Language Processing (NLP)

• Embeddings
• Code Preparation (NLP)
• Text Preprocessing
• CNNs for Text
• Text Classification with CNNs

Transfer Learning for Computer Vision Status

• Transfer Learning Theory
• Some Pre-Trained Models (VGG, ResNet, Inception, MobileNet)
• Large Datasets and Data Generators
• 2 Approaches to Transfer Learning
• Transfer Learning Code (Part 1)
• Transfer Learning Code (Part 2)

Instructors

Dhanvika Y

Experienced instructor passionate about teaching and sharing knowledge.