diff --git a/gpu_cluster_tutorial_training_script.sh b/gpu_cluster_tutorial_training_script.sh
index 5ee826c..0eb872e 100644
--- a/gpu_cluster_tutorial_training_script.sh
+++ b/gpu_cluster_tutorial_training_script.sh
@@ -30,4 +30,4 @@ export TMP=/disk/scratch/${STUDENT_ID}/
 
 source /home/${STUDENT_ID}/miniconda3/bin/activate mlp
 
-python network_trainer.py --batch_size 128 --epochs 200 --experiment_prefix vgg-net-emnist-sample-exp --dropout_rate 0.4 --batch_norm_use True --strided_dim_reduction True --seed 25012018
+python emnist_network_trainer.py --batch_size 128 --epochs 200 --experiment_prefix vgg-net-emnist-sample-exp --dropout_rate 0.4 --batch_norm_use True --strided_dim_reduction True --seed 25012018
diff --git a/notes/gpu-cluster-quick-start.md b/notes/gpu-cluster-quick-start.md
index 204419e..5ce7d68 100644
--- a/notes/gpu-cluster-quick-start.md
+++ b/notes/gpu-cluster-quick-start.md
@@ -1,4 +1,4 @@
-#GPU Cluster Quick-Start Guide
+# GPU Cluster Quick-Start Guide
 
 This guide is intended to guide students into the basics of using the mlp1/mlp2 GPU clusters. It is not intended to be
 an exhaustive guide that goes deep into micro-details of the Slurm ecosystem. For an exhaustive guide please visit 
@@ -54,7 +54,7 @@ git config --global user.name "[your name]"
 git config --global user.email "[matric-number]@sms.ed.ac.uk"
 ```
 9. Now clone the mlpractical repo using ```git clone https://github.com/CSTR-Edinburgh/mlpractical.git```.
-10. Checkout the mlp_tf_tutorial branch using ```git checkout mlp2017-8/mlp_tf_tutorial```.
+10. Checkout the semester_2 branch using ```git checkout mlp2017-8/semester_2_materials```.
 11. ```cd mlpractical``` and then install the required packages using ```pip install -r requirements_gpu.txt```.
 12. Once this is done you will need to setup the MLP_DATA path using the following block of commands:
 ```bash
@@ -72,7 +72,7 @@ export MLP_DATA_DIR=$HOME/mlpractical/data
 13. This includes all of the required installations. Proceed to the next section outlining how to use the slurm cluster
  management software. Please remember to clean your setup files using ```conda clean -t```
  
-###Using Slurm
+### Using Slurm
 Slurm provides us with some commands that can be used to submit, delete, view, explore current jobs, nodes and resources among others.
 To submit a job one needs to use ```sbatch script.sh``` which will automatically find available nodes and pass the job,
  resources and restrictions required. The script.sh is the bash script containing the job that we want to run. Since we will be using the NVIDIA CUDA and CUDNN libraries 
@@ -86,7 +86,7 @@ To submit a job one needs to use ```sbatch script.sh``` which will automatically
 #SBATCH --mem=16000  # memory in Mb
 #SBATCH -o outfile  # send stdout to outfile
 #SBATCH -e errfile  # send stderr to errfile
-#SBATCH -t 0:01:00  # time requested in hour:minute:seconds
+#SBATCH -t 1:00:00  # time requested in hour:minute:seconds
 
 # Setup CUDA and CUDNN related paths
 export CUDA_HOME=/opt/cuda-8.0.44
@@ -161,6 +161,6 @@ cp ~/output /afs/inf.ed.ac.uk/u/s/<studentUUN>
 
 This should directly copy the files to AFS. Furthermore one can use rsync as shown before.
 
-##Additional Help
+## Additional Help
 
-If you require additional help as usual please post on piazza or ask in the tech support helpdesk.
\ No newline at end of file
+If you require additional help as usual please post on piazza or ask in the tech support helpdesk.