chore(examples): typo fix (risc0#1204)

minor typos fix in README.md files, one is in a template ("flaot")

have a nice sunday

examples/profiling/README.md

@@ -35,7 +35,7 @@ RISC0_PPROF_OUT=./profile.pb cargo run
 ```
 
 The above command will run the Fibonacci computation for 1000 iterations and write the profiling output to `profile.pb`.
-Use the environment variable `RISC0_PPROF_OUT` to set to the desired output path for the profiling data.
+Use the environment variable `RISC0_PPROF_OUT` to set the desired output path for the profiling data.
 
 ### Step 3: Visualization
 
@@ -67,7 +67,7 @@ When you visualize the profiling data, you can see the relative performance in t
 This can be helpful in understanding the efficiency of various algorithms and their performance implications.
 
 Use the pprof web interface to compare the performance of the 3 Fibonacci implementations.
-Refer to the [pprof docs] for more inforamtion about the web interface.
+Refer to the [pprof docs] for more information about the web interface.
 
 [install Go]: https://go.dev/doc/install
 [official pprof documentation]: https://github.com/google/pprof/blob/main/doc/README.md
@@ -78,7 +78,7 @@ Refer to the [pprof docs] for more inforamtion about the web interface.
 <!-- prettier-ignore-start -->
 [^1]:
     Here “sampling” is in quotes because the profiler actually captures the call stack at every cycle of program execution. Capturing a call stack on every cycle of execution is not done in most programs on physical CPUs for a few reasons:
-    <!-- HACK: This comment prevents the list below from being interpretted to be a code block -->
+    <!-- HACK: This comment prevents the list below from being interpreted to be a code block -->
     - It would be cost prohibitive to do so for all but quite short program executions.
     - Introducing such heavy profiling would actually alter the performance characteristics in significant ways.
     <!-- -->

examples/smartcore-ml/README.md

@@ -18,7 +18,7 @@ res/ml-model and res/input-data come preloaded with a trained decision tree clas
 
 ## Model Types
 
-It is important to specificy the generic types for each model so that they can be succesfully deserialized.  The input data for a trained model is typically formatted as a DenseMatrix, so be sure to add the following import:
+It is important to specify the generic types for each model so that they can be successfully deserialized.  The input data for a trained model is typically formatted as a DenseMatrix, so be sure to add the following import:
 `use smartcore::linalg::basic::matrix::DenseMatrix;`
 when using KNN, be sure to add the following import:
 `use smartcore::metrics::distance::euclidian::Euclidian;`
@@ -37,7 +37,7 @@ let model: GaussianNB<{float}, _, DenseMatrix<{float}>, _> = trained_model;
 ```
 
 ```
-let model: LogisticRegression<{flaot}, u32, DenseMatrix<{float}>, Vec<u32>> = trained_model;
+let model: LogisticRegression<{float}, u32, DenseMatrix<{float}>, Vec<u32>> = trained_model;
 ```
 
 ```