diff --git a/mdp.ipynb b/mdp.ipynb
index b9952f528..b348c761b 100644
--- a/mdp.ipynb
+++ b/mdp.ipynb
@@ -288,7 +288,7 @@
    "metadata": {},
    "source": [
     "Now let us implement the simple MDP in the image below. States A, B have actions X, Y available in them. Their probabilities are shown just above the arrows. We start with using MDP as base class for our CustomMDP. Obviously we need to make a few changes to suit our case. We make use of a transition matrix as our transitions are not very simple.\n",
-    "<img src=\"files/images/mdp-a.png\">"
+    "<img src=\"images/mdp-a.png\">"
    ]
   },
   {
diff --git a/probability.ipynb b/probability.ipynb
index fe9643a83..14eda4a20 100644
--- a/probability.ipynb
+++ b/probability.ipynb
@@ -1288,7 +1288,7 @@
     "\n",
     "The example below where we implement the network shown in **Figure 14.3** of the book will make this more clear.\n",
     "\n",
-    "<img src=\"files/images/bayesnet.png\">\n",
+    "<img src=\"images/bayesnet.png\">\n",
     "\n",
     "The alarm node can be made as follows: "
    ]
@@ -3275,7 +3275,7 @@
    "source": [
     "The function **prior_sample** implements the algorithm described in **Figure 14.13** of the book. Nodes are sampled in the topological order. The old value of the event is passed as evidence for parent values. We will use the Bayesian Network in **Figure 14.12** to try out the **prior_sample**\n",
     "\n",
-    "<img src=\"files/images/sprinklernet.jpg\" height=\"500\" width=\"500\">\n",
+    "<img src=\"images/sprinklernet.jpg\" height=\"500\" width=\"500\">\n",
     "\n",
     "Traversing the graph in topological order is important.\n",
     "There are two possible topological orderings for this particular directed acyclic graph.\n",
diff --git a/reinforcement_learning.ipynb b/reinforcement_learning.ipynb
index ee3b6a5eb..585337ac6 100644
--- a/reinforcement_learning.ipynb
+++ b/reinforcement_learning.ipynb
@@ -128,7 +128,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The `sequential_decision_environment` is a GridMDP object as shown below. The rewards are **+1** and **-1** in the terminal states, and **-0.04** in the rest. <img src=\"files/images/mdp.png\"> Now we define actions and a policy similar to **Fig 21.1** in the book."
+    "The `sequential_decision_environment` is a GridMDP object as shown below. The rewards are **+1** and **-1** in the terminal states, and **-0.04** in the rest. <img src=\"images/mdp.png\"> Now we define actions and a policy similar to **Fig 21.1** in the book."
    ]
   },
   {