Convolutional Neural Networks with TensorFlow Practice Exam
Convolutional Neural Networks with TensorFlow Practice Exam
About the Convolutional Neural Networks with TensorFlow Exam
Convolutional Neural Networks (CNNs) with TensorFlow teaches how to build and train deep learning models for image recognition and processing. Learners will explore key CNN architectures, feature extraction, and techniques like pooling and optimization. With hands-on practice, this course equips learners to develop efficient AI models for tasks such as object detection, facial recognition, and medical image analysis using TensorFlow’s powerful framework.
Skills Required to learn
- Basic understanding of Python programming.
- Familiarity with machine learning and deep learning concepts.
- Knowledge of linear algebra, probability, and statistics.
- Experience with TensorFlow or any deep learning framework (optional but helpful).
- Understanding of neural networks and how they process data.
- Basic knowledge of image processing and computer vision techniques.
- Familiarity with data manipulation libraries like NumPy and Pandas.
Knowledge Gained
- Proficiency in building and training Convolutional Neural Networks (CNNs) using TensorFlow.
- In-depth understanding of key CNN components, such as convolutional layers, pooling, and activation functions.
- Hands-on experience in implementing CNN architectures for image recognition and classification.
- Skills in optimizing CNN models for better accuracy and performance.
- Knowledge of advanced techniques like data augmentation, dropout, and transfer learning.
- Ability to apply CNNs for tasks like object detection, facial recognition, and medical image analysis.
- Experience in evaluating and fine-tuning CNN models for real-world applications.
Who should take the Exam?
- Aspiring AI and machine learning engineers specializing in computer vision.
- Data scientists and analysts who want to deepen their expertise in deep learning for image-related tasks.
- Software developers interested in creating AI-powered image recognition applications.
- Researchers and academicians working on image analysis and object detection projects.
- IT professionals aiming to transition into deep learning and computer vision roles.
- Students preparing for careers in artificial intelligence, particularly in image processing.
- Individuals looking to validate their skills in CNNs and TensorFlow through certification.
Course Outline
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
- 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)
