diff --git a/tfjs-core/src/ops/array_ops.ts b/tfjs-core/src/ops/array_ops.ts
index f770b48c584..58a21f9cd4c 100644
--- a/tfjs-core/src/ops/array_ops.ts
+++ b/tfjs-core/src/ops/array_ops.ts
@@ -26,6 +26,72 @@ import {op} from './operation';
 import {MPRandGauss, RandGamma, UniformRandom} from './rand';
 import {zeros, zerosLike} from './tensor_ops';
 
+/** Broadcast an array to a compatible shape NumPy-style.
+ *  
+ *  The tensor's shape is compared to the broadcast shape from end to beginning.
+ *  Ones are prepended to the tensor's shape until is has the same length as
+ *  the broadcast shape. If input.shape[i]==shape[i], the (i+1)-th axis is
+ *  already broadcast-compatible. If input.shape[i]==1 and shape[i]==N, then
+ *  the input tensor is tiled N times along that axis (using tf.tile).
+ *  
+ *  @param input The tensor that is to be broadcasted.
+ *  @param shape The input is to be broadcast to this shape.
+ */
+/** @doc {heading: 'Tensors', subheading: 'Transformations'} */
+function broadcastTo_<R extends Rank>(
+  x: Tensor|TensorLike, shape: ShapeMap[R]
+): Tensor<R>
+{
+  let input = convertToTensor(x, 'broadcastTo', 'x');
+  const xShape = input.shape;
+
+  if( shape.some(d => !(d > 0) || d%1 !== 0) ) {
+    throw new Error(`broadcastTo(): Invalid broadcast shape [${shape}].`);
+  }
+
+  if( shape.length < input.rank ) {
+    throw new Error(
+      `broadcastTo(): shape.length=${shape.length} < input.rank=${input.rank}.`
+    );
+  }
+
+  if( shape.length > input.rank )
+  {
+    const newShape =  input.shape.slice();
+    while(newShape.length < shape.length ) {
+          newShape.unshift(1);
+    }
+    input = input.reshape(newShape);
+  }
+
+  const reps: number[] = Array.from(shape);
+  for( let i=shape.length-1; i >= 0; i-- )
+  {
+    if( input.shape[i] === shape[i] ) {
+      reps[i] = 1;
+    }
+    else if( input.shape[i] !== 1 ) {
+      throw new Error(
+        `broadcastTo(): [${xShape}] cannot be broadcast to [${shape}].`
+      );
+    }
+  }
+
+  const axes = reps.map( ( n,i)  => n > 1 ? i : -1 ).filter( i => i >= 0 );
+
+  if( axes.length === 0 ) {
+    return input.clone() as Tensor<R>;
+  }
+
+  return ENGINE.runKernelFunc(
+    backend => backend.tile(input,reps),
+    {input},
+    (dy: Tensor) => ({
+      input: () => dy.sum(axes,/*keepDims=*/true)
+    })
+  ) as Tensor<R>;
+}
+
 /**
  * Creates a new tensor with the same values and shape as the specified
  * tensor.
@@ -1126,6 +1192,7 @@ export {
 };
 
 export const batchToSpaceND = op({batchToSpaceND_});
+export const broadcastTo = op({broadcastTo_});
 export const cast = op({cast_});
 export const clone = op({clone_});
 export const cumsum = op({cumsum_});
diff --git a/tfjs-core/src/ops/array_ops_test.ts b/tfjs-core/src/ops/array_ops_test.ts
index fe990494af2..33103efb735 100644
--- a/tfjs-core/src/ops/array_ops_test.ts
+++ b/tfjs-core/src/ops/array_ops_test.ts
@@ -19,10 +19,95 @@ import * as tf from '../index';
 import {ALL_ENVS, BROWSER_ENVS, describeWithFlags, NODE_ENVS} from '../jasmine_util';
 import {expectArraysClose, expectArraysEqual, expectPromiseToFail, expectValuesInRange} from '../test_util';
 import {TypedArray} from '../types';
+import {Tensor} from '../tensor';
 import * as util from '../util';
 
 import {expectArrayInMeanStdRange, jarqueBeraNormalityTest} from './rand_util';
 
+describeWithFlags('broadcastTo', ALL_ENVS, () => {
+  it('[] -> [3,2]', async () => {
+    const a = tf.scalar(4.2);
+    const A = tf.tensor2d([[4.2, 4.2],
+                           [4.2, 4.2],
+                           [4.2, 4.2]]);
+
+    expectArraysClose(
+      await A.array(),
+      await tf.broadcastTo(a,A.shape).array()
+    );
+
+    // test gradients
+    const w = tf.tensor2d([[ 4.7, 4.5],
+                           [-6.1,-6.6],
+                           [-8.1,-3.4]]),
+          f = (a: Tensor) => tf.broadcastTo(a,A.shape).mul(w).mean().asScalar(),
+          h = (a: Tensor) =>                         a.mul(w).mean().asScalar();
+
+    const df = tf.grad(f),
+          dh = tf.grad(h);
+
+    expectArraysClose(
+      await df(a).array(),
+      await dh(a).array()
+    );
+  });
+
+  it('[2] -> [3,2]', async () => {
+    const a = tf.tensor1d( [1,2] );
+    const A = tf.tensor2d([[1,2],
+                           [1,2],
+                           [1,2]]);
+    expectArraysClose(
+      await A.array(),
+      await tf.broadcastTo(a,A.shape).array()
+    );
+
+    // test gradients
+    const w = tf.tensor2d([[ 4.7, 4.5],
+                           [-6.1,-6.6],
+                           [-8.1,-3.4]]),
+          f = (a: Tensor) => tf.broadcastTo(a,A.shape).mul(w).mean().asScalar(),
+          h = (a: Tensor) =>                         a.mul(w).mean().asScalar();
+
+    const df = tf.grad(f),
+          dh = tf.grad(h);
+
+    expectArraysClose(
+      await df(a).array(),
+      await dh(a).array()
+    );
+  });
+
+  it('[3,1] -> [3,2]', async () => {
+    const a = tf.tensor2d([[1],
+                           [2],
+                           [3]]);
+    const A = tf.tensor2d([[1,1],
+                           [2,2],
+                           [3,3]]);
+
+    expectArraysClose(
+      await A.array(),
+      await tf.broadcastTo(a,A.shape).array()
+    );
+
+    // test gradients
+    const w = tf.tensor2d([[ 4.7, 4.5],
+                           [-6.1,-6.6],
+                           [-8.1,-3.4]]),
+          f = (a: Tensor) => tf.broadcastTo(a,A.shape).mul(w).mean().asScalar(),
+          h = (a: Tensor) =>                         a.mul(w).mean().asScalar();
+
+    const df = tf.grad(f),
+          dh = tf.grad(h);
+
+    expectArraysClose(
+      await df(a).array(),
+      await dh(a).array()
+    );
+  });
+});
+
 describeWithFlags('zeros', ALL_ENVS, () => {
   it('1D default dtype', async () => {
     const a: tf.Tensor1D = tf.zeros([3]);