[restructure] the site

docs/Gemfile.lock

@@ -24,6 +24,7 @@ GEM
     execjs (2.9.1)
     ffi (1.16.3)
     forwardable-extended (2.6.0)
+    google-protobuf (3.24.4-arm64-darwin)
     google-protobuf (3.24.4-x86_64-darwin)
     google-protobuf (3.24.4-x86_64-linux)
     http_parser.rb (0.8.0)
@@ -81,6 +82,8 @@ GEM
     rexml (3.2.6)
     rouge (4.1.3)
     safe_yaml (1.0.5)
+    sass-embedded (1.69.3-arm64-darwin)
+      google-protobuf (~> 3.23)
     sass-embedded (1.69.3-x86_64-darwin)
       google-protobuf (~> 3.23)
     sass-embedded (1.69.3-x86_64-linux-gnu)
@@ -91,6 +94,7 @@ GEM
     webrick (1.8.1)
 
 PLATFORMS
+  arm64-darwin-23
   x86_64-darwin-19
   x86_64-linux
 

docs/_blog/attention_is_all_you_need_p1.md

@@ -5,7 +5,7 @@ layout: blog_default
 date: 2023-06-02
 keywords: NLP, transformers, attention, BERT, GPT-2, GPT-3, Pytorch, Huggingface, OpenAI, Deep Learning, Machine Learning, Data Science, Python,
 published: true
-tags: deep-learning NLP transformers attention  GPT-2 Pytorch Huggingface OpenAI
+tags: deep-learning nlp transformers attention  gpt-2 pytorch huggingface openai
 ---
 
 
@@ -14,13 +14,9 @@ The impact of the transformer architecture in the field of NLP has been huge. It
 The series of posts are based on the video by Andrej Karpathy. What I will do is to add some extra explanations with some intuition behind the concepts and I will also add some code to make it more practical. To fully understand the transformer architecture, we need to follow the path of the original paper, which means tracing the evolution of the transformer architecture from recurrent neural networks (RNNs) to the final transformer architecture. Here is our roadmap:
 
 
-1. [Understanding the neurons in the deep learning context](#1-understanding-the-neurons-in-the-deep-learning-context)
-2. [Implementing a simple RNN model](#1-implementing-a-simple-rnn-model)
-3. [Implementing a LSTM and GRU model](#3-implementing-a-lstm-and-gru-model)
-4. Implementing a sequence-to-sequence model
-5. Implementing a RNN encoder-decoder model
-6. Implementing a neural machine translation model
-7. Implementing a transformer encoder-decoder model
+- [1. Understanding the neurons in the deep learning context](#1-understanding-the-neurons-in-the-deep-learning-context)
+- [2. Implementing a simple RNN model](#2-implementing-a-simple-rnn-model)
+- [3. Implementing a LSTM and GRU model](#3-implementing-a-lstm-and-gru-model)
 
 
 ## 1. Understanding the neurons in the deep learning context

docs/_blog/the-future-with-ai.md

@@ -5,7 +5,7 @@ layout: blog_default
 date: 2023-07-29
 keywords: AI machine-learning deep-learning ChatGPT GPT-3 GPT-4
 published: true
-tags: machine-learning deep-learning ChatGPT future leadership artificial-intelligence
+tags: machine-learning deep-learning chatgpt future leadership artificial-intelligence
 ---
 
 We are living in a very noisy world. There are so many things happening around us about AI. Almost every one is excited about AI. Almost every one in the community claims that they are doing AI or they are using AI to solve problems.

docs/blog-tags/GPT-2.md

@@ -1,6 +1,6 @@
 ---
 layout: tagpage
-title: "Tag: GPT-2"
-tag: GPT-2
+title: "Tag: gpt-2"
+tag: gpt-2
 robots: noindex
 ---

docs/blog-tags/Huggingface.md

@@ -1,6 +1,6 @@
 ---
 layout: tagpage
-title: "Tag: Huggingface"
-tag: Huggingface
+title: "Tag: huggingface"
+tag: huggingface
 robots: noindex
 ---

docs/blog-tags/NLP.md

@@ -1,6 +1,6 @@
 ---
 layout: tagpage
-title: "Tag: NLP"
-tag: NLP
+title: "Tag: nlp"
+tag: nlp
 robots: noindex
 ---

docs/blog-tags/OpenAI.md

@@ -1,6 +1,6 @@
 ---
 layout: tagpage
-title: "Tag: OpenAI"
-tag: OpenAI
+title: "Tag: openai"
+tag: openai
 robots: noindex
 ---