add results to tuning
"**Interpret the Tuning Results**\n",
"\n",
"The chart shows 4 different threads running in parallel with different learning rate, and the number of total runs is 8. By comparing the 'Best Metric' which is `best_val_loss` in our case, we can pick the best learning rate. The HyperDrive run automatically shows the tracking charts (example in the following) to let users understand the tuning process.\n",
- "![Tuning](https://nlpbp.blob.core.windows.net/images/tuning.PNG)**HyperParameter Tuning with HyperDrive**\n",
+ "![Tuning](https://nlpbp.blob.core.windows.net/images/tuning.PNG)\n",
"\n",
- "From the results in section [2.3.5 Monitor your run](#2.3.5-Monitor-your-run), the `best_val_loss` for 1 nodes is 4.81, but with tuning we can easily achieve better performance around "
+ "**From the results in section [2.3.5 Monitor your run](#2.3.5-Monitor-your-run), the `best_val_loss` for 1 nodes is 4.81, but with tuning we can easily achieve better performance around 4.65.**"
]
},
{
