Deep Learning with TensorFlow: Build AI Models
Master the fundamentals of deep learning and build powerful neural networks using TensorFlow. From basic concepts to advanced architectures, launch your AI journey.
Course Syllabus: Deep Learning with TensorFlow
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Introduction:
Overview of deep learning, its applications, and the role of TensorFlow as a leading framework.
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Neural Networks Intro:
Understand the foundational concepts of neural networks, including neurons, layers, and activation functions.
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Setting up the environment:
Guide to installing TensorFlow and configuring your development environment for deep learning projects.
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Minimal example:
Build and train your first simple neural network in TensorFlow to grasp the basic workflow.
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Introduction to TensorFlow:
Explore TensorFlow’s core components, data structures (tensors), and computational graph concepts.
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Deep nets overview:
Understand the architecture and principles of various deep neural network types, such as CNNs and RNNs.
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Overfitting:
Learn to identify and mitigate overfitting in neural networks using techniques like regularization and dropout.
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Initialization:
Discover different weight initialization strategies for neural networks and their impact on training stability and performance.
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Optimizers:
Explore various optimization algorithms (e.g., SGD, Adam, RMSprop) that accelerate neural network training and improve convergence.
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Preprocessing:
Master essential data preprocessing techniques for deep learning, including normalization, standardization, and data augmentation.
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Deeper example:
Work through a more complex deep learning example, applying multiple concepts learned throughout the course.
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Business case:
Analyze a real-world business problem and develop a deep learning solution, demonstrating practical application of your skills.
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Conclusion:
Review key concepts and discuss next steps for advancing your deep learning expertise and career.