What will you learn in this Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning Course
Best practices for using TensorFlow, a popular open-source machine learning framework.
Building basic neural networks in TensorFlow.
Training neural networks for computer vision applications.
Understanding and implementing convolutions to enhance neural network performance.
Program Overview
1. A New Programming Paradigm
⏳ 5 hours
Introduction to machine learning and deep learning concepts.
Understanding the shift from traditional programming to machine learning paradigms.
Building and training a simple neural network using TensorFlow.
2. Introduction to Computer Vision
⏳ 5 hours
Basics of computer vision and image processing.
Implementing neural networks for image classification tasks.
Utilizing callbacks to monitor and control training processes.
3. Enhancing Vision with Convolutional Neural Networks
⏳ 5 hours
Understanding convolutions and pooling layers.
Building convolutional neural networks (CNNs) for improved image recognition.
Applying CNNs to real-world datasets for better accuracy.
4. Using Real-world Images
⏳ 7 hours
Handling complex, real-world image data.
Data augmentation techniques to improve model generalization.
Implementing transfer learning to leverage pre-trained models.
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Job Outlook
High demand for professionals skilled in TensorFlow for roles such as AI Engineer, Machine Learning Engineer, and Data Scientist.
Applicable skills in industries like healthcare, finance, automotive, and technology.
Foundation for advanced studies in deep learning and AI specializations.
Specification: Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
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