Spaces:
Sleeping
Sleeping
app updated
Browse files
app.py
CHANGED
|
@@ -17,5 +17,179 @@ st.markdown(
|
|
| 17 |
|
| 18 |
- Click on `Files`, and then go to `docs/notebooks` for access of python notebooks
|
| 19 |
- For more information, go [here](https://wyn-education.streamlit.app/)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 20 |
"""
|
| 21 |
)
|
|
|
|
| 17 |
|
| 18 |
- Click on `Files`, and then go to `docs/notebooks` for access of python notebooks
|
| 19 |
- For more information, go [here](https://wyn-education.streamlit.app/)
|
| 20 |
+
|
| 21 |
+
## Table of Content
|
| 22 |
+
|
| 23 |
+
| Index | Title |
|
| 24 |
+
|-------|-------------------------------------------------------------------------------------------|
|
| 25 |
+
| 0 | intro to python - creating pi |
|
| 26 |
+
| 0 | intro to python |
|
| 27 |
+
| 01 | numpy, pandas, matplotlib |
|
| 28 |
+
| 02 | ann and cnn |
|
| 29 |
+
| 02 | gradient descent in neural networks |
|
| 30 |
+
| 02 | run a neural network models on tpu |
|
| 31 |
+
| 03 | run an installed neuralnet |
|
| 32 |
+
| 04a | more in cnn (famous cnn) |
|
| 33 |
+
| 04a | more in cnn |
|
| 34 |
+
| 04a | popular cnn walkthrough with training and evaluating on test set |
|
| 35 |
+
| 04b | 3d cnn using captcha ocr |
|
| 36 |
+
| 04b | vit classifier on mnist |
|
| 37 |
+
| 04c | chestxray classification |
|
| 38 |
+
| 04d | class activation map |
|
| 39 |
+
| 05 | fine tuning neural network |
|
| 40 |
+
| 06a | autoencoder |
|
| 41 |
+
| 06b | image denoising |
|
| 42 |
+
| 07a | variational autoencoder |
|
| 43 |
+
| 07b | neural network regressor + bayesian last layer |
|
| 44 |
+
| 08 | inference of autoencoder |
|
| 45 |
+
| 09a | image segmentation |
|
| 46 |
+
| 09b | image segmentation unet |
|
| 47 |
+
| 09c | image segmentation unet dense style |
|
| 48 |
+
| 09d | image segmentation unet attention style |
|
| 49 |
+
| 10 | dcgan on masked mnist |
|
| 50 |
+
| 10 | masked image model |
|
| 51 |
+
| 10 | reconstruct mnist fashion image from ae to vapaad |
|
| 52 |
+
| 10 | reconstruct mnist image from ae to vapaad |
|
| 53 |
+
| 10 | vapad test v1 |
|
| 54 |
+
| 10 | vapad test v2 |
|
| 55 |
+
| 10a | dcgan |
|
| 56 |
+
| 10b | dcgan on masked mnist |
|
| 57 |
+
| 11a | huggingface on names |
|
| 58 |
+
| 11b | transformers |
|
| 59 |
+
| 11c | lstm on IMDB |
|
| 60 |
+
| 11c | simple RNN on sine function |
|
| 61 |
+
| 11d | text encoder using transformers |
|
| 62 |
+
| 11e | attention layer sample |
|
| 63 |
+
| 11f | convolutional lstm next frame prediction |
|
| 64 |
+
| 11g | convolutional lstm next frame prediction |
|
| 65 |
+
| 11h | next frame prediction convolutional lstm |
|
| 66 |
+
| 11i | next frame prediction convolutional lstm + attention |
|
| 67 |
+
| 11j | next frame prediction vapaad |
|
| 68 |
+
| 11k | next frame ecoli prediction instruct-vapaad class (updated) with stop gradient |
|
| 69 |
+
| 11k | next frame prediction instruct-vapaad class (updated) with stop gradient |
|
| 70 |
+
| 11k | next frame prediction instruct-vapaad class with stop gradient |
|
| 71 |
+
| 11k | next frame prediction instruct-vapaad with stop gradient |
|
| 72 |
+
| 11k | next frame prediction instruct-vapaad |
|
| 73 |
+
| 13 | bert on IMDB |
|
| 74 |
+
| 14 | music generation |
|
| 75 |
+
| 15 | functional api and siamise network |
|
| 76 |
+
| 16a | use lstm to forecast stock price |
|
| 77 |
+
| 16b | use neuralprophet to forecast stock price |
|
| 78 |
+
| 16c | use finviz to get basic stock data |
|
| 79 |
+
| 16d | dynamic time warping |
|
| 80 |
+
| 17 | introduction to modeling gcl |
|
| 81 |
+
| 18a | image classification with vit |
|
| 82 |
+
| 18b | transformer |
|
| 83 |
+
| 18c | transformers can do anything |
|
| 84 |
+
| 18d | attention |
|
| 85 |
+
| 18e | transformers and multi-head attention |
|
| 86 |
+
| 19a | text generation with GPT |
|
| 87 |
+
| 19b | quick usage of chatGPT |
|
| 88 |
+
| 19c | build quick chatbot using clinical trails data |
|
| 89 |
+
| 19c | fine tune chatgpt clinical trials data - part 1 |
|
| 90 |
+
| 19c | fine tune chatgpt clinical trials data - part 2 |
|
| 91 |
+
| 19c | fine tune chatgpt olympics data - part 1 |
|
| 92 |
+
| 19d | distances between two sentences |
|
| 93 |
+
| 20b | generate ai photo by leapai |
|
| 94 |
+
| 21 | neural machine learning translation |
|
| 95 |
+
| 21a | image classification with vision transformer |
|
| 96 |
+
| 21b | image segmentation |
|
| 97 |
+
| 21b | image_classification_with_vision_transformer_brain_tumor |
|
| 98 |
+
| 21b | object detection using vision transformer |
|
| 99 |
+
| 21b | shiftvit on cifar10 |
|
| 100 |
+
| 21c | face recognition |
|
| 101 |
+
| 21d | neural style transfer |
|
| 102 |
+
| 21e | 3d image classification |
|
| 103 |
+
| 21f | object detection inference from huggingface |
|
| 104 |
+
| 21f | object detection inference |
|
| 105 |
+
| 22a | monte carlo policy gradient |
|
| 106 |
+
| 22b | dql carpole |
|
| 107 |
+
| 22c | dqn carpole keras |
|
| 108 |
+
| 23a | actor-critic intro using toy data |
|
| 109 |
+
| 23a | actor-critic intro |
|
| 110 |
+
| 23b | actor-critic with ppo |
|
| 111 |
+
| 24a | basic langchain tutorial |
|
| 112 |
+
| 24a | fine tune falcon on qlora |
|
| 113 |
+
| 24a | fine tune llm bert using hugginface transformer |
|
| 114 |
+
| 24a | semantic_similarity_with_bert |
|
| 115 |
+
| 24b | character level text generation using lstm |
|
| 116 |
+
| 24b | custom agent with plugin retrieval using langchain |
|
| 117 |
+
| 24b | fast bert embedding |
|
| 118 |
+
| 24b | internet search by key words |
|
| 119 |
+
| 24b | palm api getting started |
|
| 120 |
+
| 24b | pandasAI demo |
|
| 121 |
+
| 24b | scrape any PDF for QA pairs |
|
| 122 |
+
| 24b | scrape internet with public URL |
|
| 123 |
+
| 24b | self refinement prompt engineering |
|
| 124 |
+
| 24b | semantic similarity with keras nlp |
|
| 125 |
+
| 24b | serpapi openai |
|
| 126 |
+
| 24c | fine tune customized qa model |
|
| 127 |
+
| 24d | fine tune llm tf-f5 |
|
| 128 |
+
| 24d | langchain integrations of vector stores |
|
| 129 |
+
| 24d | performance evaluation of finetuned model, chatgpt, langchain, and rag |
|
| 130 |
+
| 24e | working with langchain agents |
|
| 131 |
+
| 24f | api call to aws lambda with llama2 deployed |
|
| 132 |
+
| 24f | fine tune bert using mrpc dataset and push to huggingface hub |
|
| 133 |
+
| 24f | fine tune Llama 2 using ysa data in colab |
|
| 134 |
+
| 24f | fine tune llama2 in colab |
|
| 135 |
+
| 24f | fine tune llama2 using guanaco in colab |
|
| 136 |
+
| 24f | fine tune llama3 with orpo |
|
| 137 |
+
| 24f | fine tune Mistral_7B_v0_1 using dataset openassistant guanaco |
|
| 138 |
+
| 24f | hqq 1bit |
|
| 139 |
+
| 24f | inference endpoint interaction from huggingface |
|
| 140 |
+
| 24f | inference from llama-2-7b-miniguanaco |
|
| 141 |
+
| 24f | jax gemma on colab tpu |
|
| 142 |
+
| 24f | llm classifier tutorials |
|
| 143 |
+
| 24f | load and save models from transformers package locally |
|
| 144 |
+
| 24f | load sciq formatted dataset from huggingface into chroma |
|
| 145 |
+
| 24f | load ysa formatted dataset from huggingface into chroma |
|
| 146 |
+
| 24f | ludwig efficient fine tune Llama2 7b |
|
| 147 |
+
| 24f | process any custom data from pdf to create qa pairs for rag system and push to huggingface |
|
| 148 |
+
| 24f | process custom data from pdf and push to huggingface to prep for fine tune task of llama 2 using lora |
|
| 149 |
+
| 24f | prompt tuning using peft |
|
| 150 |
+
| 24f | started with llama 65b |
|
| 151 |
+
| 24f | what to do when rag system hallucinates |
|
| 152 |
+
| 24g | check performance boost from QA context pipeline |
|
| 153 |
+
| 24h | text generation gpt |
|
| 154 |
+
| 24i | google gemini rest api |
|
| 155 |
+
| 26 | aws textract api call via post method |
|
| 156 |
+
| 27a | image captioning vit-gpt2 on coco2014 data |
|
| 157 |
+
| 27b | image captioning cnn+transformer using flickr8 (from fine-tune to HF) |
|
| 158 |
+
| 27b | image captioning cnn+transformer using flickr8 data save and load locally |
|
| 159 |
+
| 27c | keras integration with huggingface tutorial |
|
| 160 |
+
| 27d | stock chart captioning (from data cleanup to push to HF) |
|
| 161 |
+
| 27d | stock chart image classification using vit part 1+2 |
|
| 162 |
+
| 27d | stock chart image classifier using vit |
|
| 163 |
+
| 27e | keras greedy image captioning (inference) |
|
| 164 |
+
| 27e | keras greedy image captioning (training) |
|
| 165 |
+
| 28a | quantized influence versus cosine similarity |
|
| 166 |
+
| 28b | quantized influence versus cosine similarity |
|
| 167 |
+
| 28c | quantized influence versus cosine similarity |
|
| 168 |
+
| 29a | dna generation to protein folding |
|
| 169 |
+
| 30a | v-jepa (ish) on mnist data |
|
| 170 |
+
| 30a | vapad test v1 |
|
| 171 |
+
| 30a | vapad test v2 |
|
| 172 |
+
| 30e | moving stock returns instruct-vapaad class (success) |
|
| 173 |
+
| 30e | redo rag from scratch using openai embed and qim |
|
| 174 |
+
| 31a | redo rag from scratch using openai embed and qim |
|
| 175 |
+
| 31b | redo rag from scratch using openai embed + qim + llama3 |
|
| 176 |
+
| 31c | redo rag with auto question generation |
|
| 177 |
+
| 32a | text-to-video initial attempt |
|
| 178 |
+
| _ | audio processing in python |
|
| 179 |
+
| _ | blockchain tutorial (long) |
|
| 180 |
+
| _ | blockchain tutorial |
|
| 181 |
+
| _ | dataframe querying using pandasAI |
|
| 182 |
+
| _ | extract nii files |
|
| 183 |
+
| _ | fake patient bloodtest generator |
|
| 184 |
+
| _ | Image Processing in Python_Final |
|
| 185 |
+
| _ | kmeans_from_scratch |
|
| 186 |
+
| _ | Manifold learning |
|
| 187 |
+
| _ | openai new api |
|
| 188 |
+
| _ | pca |
|
| 189 |
+
| _ | rocauc |
|
| 190 |
+
| _ | simulate grading rubrics with and without max function |
|
| 191 |
+
| _ | simulation of solar eclipse |
|
| 192 |
+
| _ | Unrar, Unzip, Untar Rar, Zip, Tar in GDrive |
|
| 193 |
+
|
| 194 |
"""
|
| 195 |
)
|