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Diffstat (limited to 'docs/posts/2024-03-21-Polynomial-Regression-in-TensorFlow-2.html')
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1 files changed, 3 insertions, 7 deletions
diff --git a/docs/posts/2024-03-21-Polynomial-Regression-in-TensorFlow-2.html b/docs/posts/2024-03-21-Polynomial-Regression-in-TensorFlow-2.html index 7a25daf..ab46ec7 100644 --- a/docs/posts/2024-03-21-Polynomial-Regression-in-TensorFlow-2.html +++ b/docs/posts/2024-03-21-Polynomial-Regression-in-TensorFlow-2.html @@ -107,9 +107,7 @@ <script src="https://cdn.jsdelivr.net/npm/mathjax@4.0.0-beta.4/input/tex/extensions/noerrors.js" charset="UTF-8"></script> -<p>$$ -y = ax^3 + bx^2 + cx + d -$$</p> +<math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><mrow><mi>y</mi><mo>=</mo><mi>a</mi><msup><mi>x</mi><mn>3</mn></msup><mo>+</mo><mi>b</mi><msup><mi>x</mi><mn>2</mn></msup><mo>+</mo><mi>c</mi><mi>x</mi><mo>+</mo><mi>d</mi></mrow></math> <h3>Optimizer Selection & Training</h3> @@ -134,9 +132,7 @@ $$</p> <p>Our loss function is Mean Squared Error (MSE):</p> -<p>$$ -= \frac{1}{n} \sum_{i=1}^{n}{(Y_i - \hat{Y_i})^2} -$$</p> +<math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><mrow><mo>=</mo><mfrac><mrow><mn>1</mn></mrow><mrow><mi>n</mi></mrow></mfrac><msubsup><mo>∑</mo><mrow><mi>i</mi><mo>=</mo><mn>1</mn></mrow><mrow><mi>n</mi></mrow></msubsup><mrow><mo stretchy="false">(</mo><mi>Y</mi><mi>_</mi><mi>i</mi><mo>−</mo><mover><mrow><mi>Y</mi><mi>_</mi><mi>i</mi></mrow><mo stretchy="false">^</mo></mover><msup><mo stretchy="false">)</mo><mn>2</mn></msup></mrow></mrow></math> <p>Where <math xmlns="http://www.w3.org/1998/Math/MathML"><mover><msub><mi>Y</mi><mi>i</mi></msub><mo stretchy="false" style="math-style:normal;math-depth:0;">^</mo></mover></math> is the predicted value and <math xmlns="http://www.w3.org/1998/Math/MathML"><msub><mi>Y</mi><mi>i</mi></msub></math> is the actual value</p> @@ -276,7 +272,7 @@ $$</p> <p>How would you modify this code to use another type of nonlinear regression? Say, </p> -<p>$$ y = ab^x $$</p> +<math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><mrow><mi>y</mi><mo>=</mo><mi>a</mi><msup><mi>b</mi><mi>x</mi></msup></mrow></math> <p>Hint: Your loss calculation would be similar to:</p> |