Readme for models (ivy-llc#85)

* added readme file for alexnet

* updated readme file for alexnet

* updated readme file

* updated the getting started section for alexnet

* Update README.rst

ivy_models/alexnet/README.rst

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+.. image:: https://github.com/unifyai/unifyai.github.io/blob/main/img/externally_linked/logo.png?raw=true#gh-light-mode-only
+   :width: 100%
+   :class: only-light
+
+.. image:: https://github.com/unifyai/unifyai.github.io/blob/main/img/externally_linked/logo_dark.png?raw=true#gh-dark-mode-only
+   :width: 100%
+   :class: only-dark
+
+
+.. raw:: html
+
+    <br/>
+    <a href="https://pypi.org/project/ivy-models">
+        <img class="dark-light" style="float: left; padding-right: 4px; padding-bottom: 4px;" src="https://badge.fury.io/py/ivy-models.svg">
+    </a>
+    <a href="https://github.com/unifyai/models/actions?query=workflow%3Adocs">
+        <img class="dark-light" style="float: left; padding-right: 4px; padding-bottom: 4px;" src="https://github.com/unifyai/models/actions/workflows/docs.yml/badge.svg">
+    </a>
+    <a href="https://github.com/unifyai/models/actions?query=workflow%3Anightly-tests">
+        <img class="dark-light" style="float: left; padding-right: 4px; padding-bottom: 4px;" src="https://github.com/unifyai/models/actions/workflows/nightly-tests.yml/badge.svg">
+    </a>
+    <a href="https://discord.gg/G4aR9Q7DTN">
+        <img class="dark-light" style="float: left; padding-right: 4px; padding-bottom: 4px;" src="https://img.shields.io/discord/799879767196958751?color=blue&label=%20&logo=discord&logoColor=white">
+    </a>
+    <br clear="all" />
+
+AlexNet
+===========
+
+`AlexNet <https://arxiv.org/abs/1404.5997>`_ competed in the ImageNet Large Scale Visual Recognition Challenge on September 30, 2012. 
+
+The network achieved a top-5 error of 15.3%, more than 10.8 percentage points lower than that of the runner up. 
+The original paper’s primary result was that the depth of the model was essential for its high performance, which was computationally expensive, 
+but made feasible due to the utilization of graphics processing units (GPUs) during training.
+
+Getting started
+-----------------
+
+.. code-block:: python
+
+    import ivy
+    ivy.set_backend("torch")
+    from ivy_models.alexnet import alexnet
+
+    # Instantiate the AlexNet Model
+    ivy_alexnet = alexnet()
+
+    # Complile the model with the image preprocessed using torch
+    ivy_alexnet = ivy.compile(ivy_alexnet, args=(ivy.asarray(torch_img.cuda()),))
+
+    # Pass the processed image to the model
+    output = ivy.softmax(ivy_alexnet(ivy.asarray(img))) 
+    classes = ivy.argsort(output[0], descending=True)[:3]  # get the top 3 classes
+    logits = ivy.gather(output[0], classes)  # get the logits
+
+    print("Indices of the top 3 classes are:", classes)
+    print("Logits of the top 3 classes are:", logits)
+    print("Categories of the top 3 classes are:", [categories[i] for i in classes.to_list()])
+
+
+    `Indices of the top 3 classes are: ivy.array([282, 281, 285], dev=gpu:0)`
+    `Logits of the top 3 classes are: ivy.array([0.64773697, 0.29496649, 0.04526037], dev=gpu:0)`
+    `Categories of the top 3 classes are: ['tiger cat', 'tabby', 'Egyptian cat']`
+
+
+The pretrained AlexNet model is now ready to be used, and is compatible with any Tensorflow, Jax and PyTorch code.
+See `this demo <https://unify.ai/demos/examples_and_demos/alexnet_demo.html>`_ for more usage example.
+
+Citation
+--------
+
+::
+
+    @article{
+      title={One weird trick for parallelizing convolutional neural networks},
+      author={Alex Krizhevsky},
+      journal={arXiv preprint arXiv:1404.5997},
+      year={2014}
+    }
+
+
+    @article{lenton2021ivy,
+      title={Ivy: Templated deep learning for inter-framework portability},
+      author={Lenton, Daniel and Pardo, Fabio and Falck, Fabian and James, Stephen and Clark, Ronald},
+      journal={arXiv preprint arXiv:2102.02886},
+      year={2021}
+    }