Pull request for code updation

code/chapter-2/README.md

@@ -8,15 +8,15 @@ We also hear the motivating personal journey of **François Chollet**, the creat
 
 Run the following commands prior to running the scripts in this directory:
 
-`$ pip install tensorflow==2.0.0`
+`$ pip install tensorflow==2.12.0`
 
 `$ pip install keras -U`
 
 `$ pip install numpy -U`
 
 `$ pip install matplotlib -U`
 
-`$ pip install tf-explain==0.1.0`
+`$ pip install tf-explain==0.3.1`
 
 `$ pip install pillow -U`
 
@@ -27,7 +27,7 @@ Run the following commands prior to running the scripts in this directory:
 The following Jupyter Notebooks are provided. Please go through the code in the given order:
 
 1. [1-predict-class.ipynb](https://github.com/practicaldl/Practical-Deep-Learning-Book/blob/master/code/chapter-2/1-predict-class.ipynb): We examine the image classification task, to answer the question “Does the image contain X” where X can be a cat, dog or any other category/class of objects.
-2. [2-what-does-my-neural-network-think.ipynb](https://github.com/practicaldl/Practical-Deep-Learning-Book/blob/master/code/chapter-2/2-what-does-my-neural-network-think.ipynb): We try to understand why the neural network made a particular prediction. We use visualization (a heatmap) to understand the decision-making that is going on within the network. Using color, we visually identify the areas within an image that prompted a decision. “Hot” spots, represented by warmer colors (red, orange, and yellow) highlight the areas with the maximum signal, whereas cooler colors (blue, purple) indicate low signal. (To run on Colab, use [2-colab-what-does-my-neural-network-think.ipynb](https://github.com/practicaldl/Practical-Deep-Learning-Book/blob/master/code/chapter-2/2-colab-what-does-my-neural-network-think.ipynb)) instead. For the Colab version you will be prompted to restart the runtime and rerun the code after `TensorFlow 2.0.0` is installed through `pip install tensorflow==2.0.0`.)
+2. [2-what-does-my-neural-network-think.ipynb](https://github.com/practicaldl/Practical-Deep-Learning-Book/blob/master/code/chapter-2/2-what-does-my-neural-network-think.ipynb): We try to understand why the neural network made a particular prediction. We use visualization (a heatmap) to understand the decision-making that is going on within the network. Using color, we visually identify the areas within an image that prompted a decision. “Hot” spots, represented by warmer colors (red, orange, and yellow) highlight the areas with the maximum signal, whereas cooler colors (blue, purple) indicate low signal. (To run on Colab, use [2-colab-what-does-my-neural-network-think.ipynb](https://github.com/practicaldl/Practical-Deep-Learning-Book/blob/master/code/chapter-2/2-colab-what-does-my-neural-network-think.ipynb)) instead. For the Colab version you will be prompted to restart the runtime and rerun the code after `TensorFlow 2.12.0` is installed through `pip install tensorflow==2.12.0`.)
 
 ### Scripts