Google’s TensorFlow is one of the leading tools for training and deploying deep learning models. It’s able to optimize wildly complex neural-network architectures with hundreds of millions of parameters, and it comes with a wide array of tools for hardware acceleration, distributed training, and production workflows. These powerful features can make it seem intimidating and unnecessary outside of the domain of deep learning.

But TensorFlow can be both accessible and usable for simpler problems not directly related to training deep learning models. At its core, TensorFlow is just an optimized library for tensor operations (vectors, matrices, etc.) and the calculus operations used to perform gradient descent on arbitrary sequences of calculations. Experienced data scientists will recognize “gradient descent” as a fundamental tool for computational mathematics, but it usually requires implementing application-specific code and equations. As we’ll see, this is where TensorFlow’s modern “automatic differentiation” architecture comes in.

TensorFlow Use Cases

• Example 1: Linear Regression with Gradient Descent in TensorFlow 2.0
  • What Is Gradient Descent?
• Example 2: Maximally Spread Unit Vectors
• Example 3: Generating Adversarial AI Inputs
• Final Thoughts: Gradient Descent Optimization
• Gradient Descent in TensorFlow: From Finding Minimums to Attacking AI Systems

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