Machine_Learning_Examples

Machine learning Pytorch and Tensorflow example codes

1. MNIST_DATA_Model.py : (Python 3.9 version ) MNIST data (28*28) -> Conv2d+FC layer pytorch implementation example -> Accuracy 91%

2. Simple RNN: (Python 3.11 version) # # Introduction to Logistic Regression in PyTorch: to build a very simple neural network in PyTorch to do handwritten digit classification.

3. Word_Embedding_SWEM_RNN_LSTM_GRU.py (Python 3.11) -> SENTIMENT ANALYSIS Example code from 4A coursera. How the word to embeddings are obtained. Embeddings are trained SWEM model is created on the embeddings RNN model is created and trained Parameter Comparision for RNN. LSTM, GRU

4. SentimentAnalysisNLP.py (Python 3.11 version ) ## Sentiment Analysis https://github.com/DenysLins/introduction_to_machine_learning/blob/master/4B_Natural_Language_Processing_Assignment.ipynb 4A Jupyter notebook Load the file and pull out words and embeddings

5. Reinforcement learning examples using OpenAi GYM (ReinforcementLearningCartpole.py and ReinforcementLearningFrozenLake.py): coursera examples

6. Chatbot using sklearn and nltk libraries (chatbotMain.py) : reading from chatbot.txt : based on cosine similarity

LLaMa model executable link:

https://github.com/Mozilla-Ocho/llamafile/blob/main/README.md#quickstart The executable is 4 Gb. Can be downloaded to run locally Multimodal: Text and Image both This is all accomplished by combining llama.cpp with Cosmopolitan Libc, which provides some useful capabilities: