AI-GenAI-Collection-2025-07

Introducing our comprehensive collection of Python notebooks, expertly curated for AI and Generative AI enthusiasts! This master zip file delves into cutting-edge topics, including supervised fine-tuning for machine learning models, reinforcement learning techniques, and the latest in Large Language Models (LLMs). Unlock the potential of LangChain and LangGraph to enhance advanced applications, alongside AutoGen frameworks designed for automated content generation. Each notebook is a hands-on resource, providing practical examples and code snippets to help you implement these pivotal concepts effectively. Whether you’re looking to refine your understanding of AI methodologies or develop sophisticated agentic AI frameworks, this collection will provide you with the tools needed to innovate and excel. Dive into the future of AI with these valuable resources, perfect for researchers, developers, and AI practitioners alike. Get ready to elevate your projects and insights in the rapidly evolving landscape of artificial intelligence!

With less than $50 bucks, you get 421 python notebooks in a zip file ready to use and learn! A list of contents can be found below:

• dwave ex1 - introduction - stock selling strategy using leap hybrid cqm solver.ipynb
• dwave ex2 - feature selection sklearn.ipynb
• ex_ - gemma3 inference from huggingface.ipynb
• ex_ - wyn agent x.ipynb
• ex_ - use azure api to invoke deepseek model.ipynb
• ex_ - ai engineer must know blanked out.ipynb
• ex_ - ai engineer must know.ipynb
• ex_ - anthropic claude api basic tutorial.ipynb
• ex_ - anthropic claude api basics.ipynb
• ex_ - audio processing in python.ipynb
• ex_ - auto dependency mapping.ipynb
• ex_ - aws bedrock invoke script.ipynb
• ex_ - azure function api invoke test.ipynb
• ex_ - baidu llm ernie tutorial.ipynb
• ex_ - basic llm api calls from nvidia.ipynb
• ex_ - blockchain tutorial (long).ipynb
• ex_ - blockchain tutorial.ipynb
• ex_ - brownian motion simulation.ipynb
• ex_ - burtenshaw function_calling.ipynb
• ex_ - burtenshaw structured_outputs.ipynb
• ex_ - claude basic tutorial parsing amazon 10k financial report.ipynb
• ex_ - claude image captioning.ipynb
• ex_ - cohere sample using api from azure.ipynb
• ex_ - cosmograph mobius widget 🎗️.ipynb
• ex_ - cosmograph tutorial basics.ipynb
• ex_ - cosmograph widget in colab ✌️.ipynb
• ex_ - create warren buffett letter dataset (1998-2024).ipynb
• ex_ - credibility score huggingface api tutorial.ipynb
• ex_ - credibility score v2.ipynb
• ex_ - credibility score.ipynb
• ex_ - data ingest from azure function app put api to azure cosmos db.ipynb
• ex_ - data ingest from http api to azure storage.ipynb
• ex_ - data querying using pandasai.ipynb
• ex_ - dependency resolution simulation for agent-based application part 1.ipynb
• ex_ - dependency resolution simulation for agent-based application part 2.ipynb
• ex_ - dependency resolution simulation for agent-based application part 3.ipynb
• ex_ - dependency resolution simulation for agent-based application part 4.ipynb
• ex_ - deploy gemma3 using docker on sagemaker endpoint.ipynb
• ex_ - duckduckgo search.ipynb
• ex_ - evaluation huggingface model.ipynb
• ex_ - fake patient bloodtest generator.ipynb
• ex_ - from data to pickle file save and load.ipynb
• ex_ - gpt4o quick api call template.ipynb
• ex_ - grok 4 tutorial api basics.ipynb
• ex_ - how to scrape a website.ipynb
• ex_ - huggingface data explore and generate o1 reasoning style training data.ipynb
• ex_ - huggingface image text to text api tutorial (operational).ipynb
• ex_ - image classification tutorial on mnist dataset with simple lenet model.ipynb
• ex_ - Image Processing in Python_Final.ipynb
• ex_ - image to embed using microsoft llm2clip.ipynb
• ex_ - inference qwen-distilled-scout-1.5b-gen2 (public).ipynb
• ex_ - inference qwen-distilled-scout-1.5b-instruct-gen2 (private).ipynb
• ex_ - inference qwen-distilled-scout-1.5b-instruct-gen2 (public).ipynb
• ex_ - introduction of basics of meta llama api client.ipynb
• ex_ - introduction to together api.ipynb
• ex_ - invoke huggingface gemma family tutorial (march 2025).ipynb
• ex_ - invoke open webui api endpoint.ipynb
• ex_ - kmeans from scratch.ipynb
• ex_ - landing ai visual doc scan tutorial.ipynb
• ex_ - langchain basic tool calling framework.ipynb
• ex_ - langchain tool calling with structured output framework and langfuse tracing.ipynb
• ex_ - langchain tool calling with structured output framework.ipynb
• ex_ - langgraph tutorial.ipynb
• ex_ - llama4 inference endpoint (in sloth + bitsandbytes) from huggingface.ipynb
• ex_ - make inference and invoke runpod api endpoint with an finetuned model.ipynb
• ex_ - Manifold learning.ipynb
• ex_ - mcp anthropic tutorial.ipynb
• ex_ - measure co2 using codecarbon when training tensorflow models.ipynb
• ex_ - meta llama tutorial part 1.ipynb
• ex_ - meta llama tutorial part 2.ipynb
• ex_ - microsoft phi4.ipynb
• ex_ - mistral ai ocr test.ipynb
• ex_ - naive bayes tutorial.ipynb
• ex_ - network mapping visualization using networkx.ipynb
• ex_ - nvidia llama3-1 nemotron model.ipynb
• ex_ - ocr from images with transformers huggingface.ipynb
• ex_ - olmocr tutorial basic.ipynb
• ex_ - openai new api.ipynb
• ex_ - pca.ipynb
• ex_ - predict customer churn using xgb.ipynb
• ex_ - pull csv from github repo tutorial.ipynb
• ex_ - push local csv file to huggingface hub.ipynb
• ex_ - pyspark tutorial.ipynb
• ex_ - quick tutorial of sqlite3 and run wyn agent against the database.ipynb
• ex_ - quick tutorial of sqlite3.ipynb
• ex_ - rag on openFDA label data.ipynb
• ex_ - roboflow api from custom projects.ipynb
• ex_ - rocauc.ipynb
• ex_ - run apply function on pandas using parallel computing demo.ipynb
• ex_ - run apply function on pandas using parallel computing.ipynb
• ex_ - runpod endpoint example.ipynb
• ex_ - searchbot api template.ipynb
• ex_ - send emails using python.ipynb
• ex_ - send text message using python using twilio.ipynb
• ex_ - serpapi literature review assistant.ipynb
• ex_ - serpapi template.ipynb
• ex_ - simulate grading rubrics with and without max function.ipynb
• ex_ - simulation of path with shades.ipynb
• ex_ - simulation of solar eclipse.ipynb
• ex_ - take hf dataset and generate jsonl format.ipynb
• ex_ - tensorflow save and load model.ipynb
• ex_ - tensorflow with gpu.ipynb
• ex_ - together ai deepseek r1.ipynb
• ex_ - together ai llama 3.3 70b.ipynb
• ex_ - together ai tutorial.ipynb
• ex_ - tutorial wyn-agent-s.ipynb
• ex_ - tutorials of yfinance.ipynb
• ex_ - Unrar, Unzip, Untar Rar, Zip, Tar in GDrive.ipynb
• ex_ - use deepseek on together ai.ipynb
• ex_ - use huggingface hub to call inference client qwq 32b example (public).ipynb
• ex_ - use huggingface hub to call inference client qwq 32b example.ipynb
• ex_ - use poetry to create and publish custom python library for pip install xyz purpose.ipynb
• ex_ - use python to process pdf into pngs.ipynb
• ex_ - use together ai deepseek to enhance reasoning of qa data (v2).ipynb
• ex_ - use together ai deepseek to enhance reasoning of qa data (v3).ipynb
• ex_ - use together ai deepseek to enhance reasoning of qa data.ipynb
• ex_ - use yfinance to do research and send to your own email
• ex_ - voice command basics.ipynb
• ex_ - write and save python script using python.ipynb
• ex_ - wyn agent x yfinance tutorial.ipynb
• ex_ - wyn agent x.ipynb
• ex_ - wyn voice tutorials.ipynb
• ex_ - wyn-pm tutorial.ipynb
• ex_ - wyn-transformers tutorial - part 1-5.ipynb
• ex_ codecarbon measure training versus inference.ipynb
• ex_ wyn keras tutorials.ipynb
• ex0 - intro to python - creating pi.ipynb
• ex0 - intro to python.ipynb
• ex1 - numpy, pandas, matplotlib.ipynb
• ex10 - dcgan on masked mnist.ipynb
• ex10 - dcgan.ipynb
• ex10 - masked image model.ipynb
• ex10 - reconstruct mnist fashion image from ae to vapaad.ipynb
• ex10 - reconstruct mnist image from ae to vapaad.ipynb
• ex10 - vapad test v1.ipynb
• ex10 - vapad test v2.ipynb
• ex11 - huggingface on IMDB.ipynb
• ex11 - huggingface on names.ipynb
• ex11 - lstm on IMDB.ipynb
• ex11 - next frame prediction convolutional lstm + attention.ipynb
• ex11 - next frame prediction convolutional lstm.ipynb
• ex11 - next frame prediction instruct-vapaad class (updated) with stop gradient.ipynb
• ex11 - next frame prediction instruct-vapaad class with stop gradient.ipynb
• ex11 - next frame prediction instruct-vapaad with stop gradient.ipynb
• ex11 - next frame prediction instruct-vapaad.ipynb
• ex11 - next frame prediction patch encoder + transformer.ipynb
• ex11 - next frame prediction vapaad.ipynb
• ex11 - rnn on sine function but for time-series forecast.ipynb
• ex11 - rnn on sine function.ipynb
• ex11b - transformers.ipynb
• ex11c - attention layer sample.ipynb
• ex11e - text encoder using transformers.ipynb
• ex11k - next frame ecoli data instruct-vapaad class (updated) with stop grad.ipynb
• ex12 - bertviz tutorial.ipynb
• ex13 - bert on IMDB.ipynb
• ex13b - build and train a fake news embeddings classifier.ipynb
• ex13b - use llm agents to rewrite news to be more neutral.ipynb
• ex14 - music generation.ipynb
• ex15 - functional api and siamise network.ipynb
• ex16 - dynamic time warping.ipynb
• ex16 - neuralprophet tutorials.ipynb
• ex16 - use finviz to get basic stock data.ipynb
• ex16 - use lstm to forecast any data.ipynb
• ex16 - use lstm to forecast stock price.ipynb
• ex16 - use neural network to forecast stock price or any other data.ipynb
• ex16 - use neuralprophet to forecast stock price.ipynb
• ex17 - introduction to modeling gcl.ipynb
• ex18 - transformer.ipynb
• ex18b - distances between two sentences.ipynb
• ex18c - attention.ipynb
• ex18d - transformers and multi-head attention.ipynb
• ex19a - text generation with GPT
• ex19b - chatGPT.ipynb
• ex19c - chat completion.ipynb
• ex19d - transformers can do anything
• ex2 - gradient descent in neural networks.ipynb
• ex2 - ann and cnn.ipynb
• ex2 - mnist tutorial with gradio experiment.ipynb
• ex2 - train nn using tpu.ipynb
• ex2 - vision + patch + self attention.ipynb
• ex2 - vit classifier on mnist data.ipynb
• ex2 - vit for medmnist.ipynb
• ex20 - stable diffusion application.ipynb
• ex20b - generate ai photo by leap ai.ipynb
• ex20b - random walks with stable diffusion 3.ipynb
• ex21 - video classification with 3d cnn and residual design.ipynb
• ex21a - image_classification_with_vision_transformer
• ex21b - image_classification_with_vision_transformer_brain_tumor
• ex21b - fine tune detr for object detection on balloons data and push to hf.ipynb
• ex21b - image segmentation.ipynb
• ex21b - object detection using vision transformer
• ex21b - object detection using vision transformer.ipynb
• ex21b - shiftvit on cifar10
• ex21c - face recognition.ipynb
• ex21d - neural style transfer
• ex21e - 3d image classification.ipynb
• ex21f - object detection inference from huggingface.ipynb
• ex21f - object detection inference.ipynb
• ex21f - zero shot object detection with yolo world.ipynb
• ex22a - actor-critic intro with toy data.ipynb
• ex22a - actor-critic intro.ipynb
• ex22b - actor-critic with ppo.ipynb
• ex22c - actor-critic with ppo from chatgpt.ipynb
• ex22c - use ppo rl to train mnist classifier.ipynb
• ex23 - monte carlo policy gradient.ipynb
• ex24a - basic langchain tutorial.ipynb
• ex24a - fine tune bert using hugginface transformer.ipynb
• ex24a - fine tune customized qa model.ipynb
• ex24a - fine tune falcon on qlora.ipynb
• ex24a - fine tune llm tf-t5.ipynb
• ex24a - learning and comparing gemini and chatgpt.ipynb
• ex24a - transformer from scratch - version 1.ipynb
• ex24a - transformer from scratch - version 2.ipynb
• ex24a - transformer from scratch - version 3.ipynb
• ex24b - character level text generation
• ex24b - custom agent with plugin retrieval using langchain.ipynb
• ex24b - fast bert embedding.ipynb
• ex24b - internet search by key words.ipynb
• ex24b - pandasAI demo.ipynb
• ex24b - scrape any PDF for QA pairs.ipynb
• ex24b - scrape internet with public URL.ipynb
• ex24b - self refinement prompt engineering.ipynb
• ex24b - semantic similarity with keras nlp
• ex24b - semantic_similarity_with_bert
• ex24b - serpapi-openai.ipynb
• ex24b - test QA+search performance boost pipeline.ipynb
• ex24c - google gemini rest api.ipynb
• ex24c - palm api.ipynb
• ex24d - finetuning gpt2 using tensorflow keras and custom data.ipynb
• ex24d - finetuning gpt2 using tensorflow keras.ipynb
• ex24d - langchain integrations of vector stores.ipynb
• ex24d - language agent tree search basic tutorial.ipynb
• ex24d - performance evaluation of finetuned model, chatgpt, langchain, and rag.ipynb
• ex24d - use aws bedrock with REST API with immediate access (billed by inference).ipynb
• ex24d - use aws textract with REST API (2025).ipynb
• ex24d - use textract to conduct ocr and use genAI to create features.ipynb
• ex24d - use textract to conduct ocr for operator.ipynb
• ex24d - use textract to conduct ocr.ipynb
• ex24d - working with langchain agents.ipynb
• ex24e - llama 65b exercise.ipynb
• ex24f - ludwig efficient fine tune Llama2-7b.ipynb
• ex24f - 4bit llm quantization with gptq.ipynb
• ex24f - alpaca + gemma 7b finetune + push to hf.ipynb
• ex24f - api call to aws lambda with llama2 deployed.ipynb
• ex24f - autogen coding with huggify data.ipynb
• ex24f - call textract ocr (by yin) and chat.ipynb
• ex24f - deepspeed tutorial.ipynb
• ex24f - fine tune deepseek r1 0528 qwen3 8b grpo.ipynb
• ex24f - fine tune deepseek-r1 model and push to hf.ipynb
• ex24f - fine tune deepseek-r1 model using grpo and push to hf.ipynb
• ex24f - fine tune deepseek-r1 model using scripts.ipynb
• ex24f - fine tune deepseek-r1 model using unsloth.ipynb
• ex24f - fine tune gemma3 1b using grpo and unsloth.ipynb
• ex24f - fine tune gemma3 4b using unsloth.ipynb
• ex24f - fine tune Llama 2 using guanaco in Google Colab.ipynb
• ex24f - fine tune Llama 2 using ysa data in Google Colab (only code).ipynb
• ex24f - fine tune Llama 2 using ysa data in Google Colab.ipynb
• ex24f - fine tune Llama 3 using warren buffett letters in colab.ipynb
• ex24f - fine tune llama 3.1b with unsloth.ipynb
• ex24f - fine tune llama financial sentiment inference.ipynb
• ex24f - fine tune llama financial sentiment.ipynb
• ex24f - fine tune llama3 with orpo v2.ipynb
• ex24f - fine tune llama3 with orpo.ipynb
• ex24f - fine tune mistral small 22b using unsloth 2x faster.ipynb
• ex24f - fine tune mistral-7b with dpo.ipynb
• ex24f - fine tune qwen 2.5 3b using grpo and unsloth with data enhancement (public).ipynb
• ex24f - fine tune qwen 2.5 3b using grpo and unsloth with data enhancement.ipynb
• ex24f - fine tune qwen 2.5 3b using grpo and unsloth without data enhancement.ipynb
• ex24f - fine tune qwen 2.5 3b using grpo and unsloth.ipynb
• ex24f - fine tune qwen 3 to support tool calling.ipynb
• ex24f - fine tune qwen model using transformer and lora only.ipynb
• ex24f - fine tune qwen use bnb to load and trl to train (for aws setup).ipynb
• ex24f - fine-tune bert using mrpc dataset and push to huggingface hub
• ex24f - finetuning using grpo tutorial.ipynb
• ex24f - gemini 1.5 trial.ipynb
• ex24f - hqq 1bit.ipynb
• ex24f - huggify data basic tutorial.ipynb
• ex24f - huggify data fine tune llama2.ipynb
• ex24f - huggify data inference using finetuned llama2.ipynb
• ex24f - huggify data pdf qa generator.ipynb
• ex24f - huggify data using google generative ai tool calling.ipynb
• ex24f - huggify-data tutorial on storyseed data (main tutorial).ipynb
• ex24f - huggify-data tutorial on storyseed data.ipynb
• ex24f - inference endpoing interaction from huggingface.ipynb
• ex24f - inference from llama-2-7b-miniguanaco.ipynb
• ex24f - invoke finetuned warren buffett letter model llama-3.2-1B-Instruct 2024 and deepseek-r1-distilled (public notebook).ipynb
• ex24f - invoke finetuned warren buffett letter model llama-3.2-1B-Instruct 2024.ipynb
• ex24f - jax gemma on colab tpu.ipynb
• ex24f - llm classifier.ipynb
• ex24f - load and save models from transformers package locally.ipynb
• ex24f - load sciq formatted dataset from huggingface into chroma.ipynb
• ex24f - load ysa formatted dataset from huggingface into chroma.ipynb
• ex24f - mistral agent exercise part 1-4.ipynb
• ex24f - parameter efficient finetuning of gpt2 with lora.ipynb
• ex24f - pdf qa generator locally.ipynb
• ex24f - process any custom data from pdf to create qa pairs for rag system and push to huggingface.ipynb
• ex24f - process custom data from pdf and push to huggingface to prep for fine tune task of llama 2 using lora.ipynb
• ex24f - prompt tuning using peft.ipynb
• ex24f - qwen 2.5 conversational + unsloth 2x faster finetuning.ipynb
• ex24f - qwen3-14b reasoning-conversation.ipynb
• ex24f - training language models with grpo trainer tutorial.ipynb
• ex24f - tutorial of docling.ipynb
• ex24f - unsloth finetune llama-3-1-8b grpo.ipynb
• ex24f - unsloth finetune phi4 conversational.ipynb
• ex24f - using grpo trainer from tel package to fine tune qwq model.ipynb
• ex24f - what to do when rag system hallucinates.ipynb
• ex24f - wyn agent using mistral agent test.ipynb
• ex24g - text generation gpt
• ex24h - llamaindex hybrid search with query fusion retriever.ipynb
• ex25 - gpt2_text_generation_with_kerasnlp
• ex26 - aws textract api call via post method.ipynb
• ex27a - image captioning vit-to-gpt2 on coco2014 data.ipynb
• ex27b - image captioning on keras (without build).ipynb
• ex27c - keras integration with huggingface tutorial.ipynb
• ex27d - collect stock chart data as image data (by yin).ipynb
• ex27d - stock chart captioning (from data cleanup to push to HF).ipynb
• ex27d - stock chart classification using seq-vit.ipynb
• ex27d - stock chart image (by yin) classifier using vit.ipynb
• ex27d - stock chart image classification using vit part 1+2.ipynb
• ex27d - stock chart image on gan.ipynb
• ex27e - keras greedy image captioning (inference).ipynb
• ex27e - keras greedy image captioning (training).ipynb
• ex28a - quantized influence versus cosine similarity.ipynb
• ex28b - quantized influence versus cosine similarity.ipynb
• ex28c - quantized influence versus cosine similarity.ipynb
• ex29a - dna generation alphafold2 v1.ipynb
• ex29a - dna generation alphafold2 v2.ipynb
• ex29a - dna generation to protein folding part 2.ipynb
• ex29a - dna generation to protein folding.ipynb
• ex3 - run an installed neuralnet.ipynb
• ex30a - v-jepa (ish) on mnist data.ipynb
• ex30a - v-jepa2 finetuning notebook.ipynb
• ex30b - medmnist classification task using custom model vapaad like model.ipynb
• ex30b - mnist custom model vapaad like model for classification.ipynb
• ex30b - moving mnist create moving images from scratch.ipynb
• ex30b - moving mnist custom model vapaad like model img2vid mode.ipynb
• ex30b - moving mnist custom model vapaad like model on ucf101.ipynb
• ex30b - moving mnist custom model vapaad like model.ipynb
• ex30b - moving mnist instruct-vapaad class (success).ipynb
• ex30b - universe custom model vapaad like model prep simulation data.ipynb
• ex30c - ecoli mutation instruct-vapaad class (in progress).ipynb
• ex30d - liver sequence instruct-vapaad class (in progress).ipynb
• ex30e - moving stock returns instruct-vapaad class (success).ipynb
• ex31a - redo rag from scratch using openai embed and qim.ipynb
• ex31b - redo rag from scratch using openai embed + qim + llama3.ipynb
• ex31c - redo rag with auto question generation.ipynb
• ex32a - text-to-video initial attempt.ipynb
• ex33a - autogen introduction.ipynb
• ex33a - build agent with customized persona using instructor library.ipynb
• ex33b - autogen introduction.ipynb
• ex33c - tool use of conversational chess.ipynb
• ex33d - autogen code executor template in py script ready to run in terminal.ipynb
• ex33d - autogen coding and custom function.ipynb
• ex33d - autogen coding with just custom function and run in terminal.ipynb
• ex33d - autogen financial ai agent tutorial.ipynb
• ex33d - autogen template script to run in terminal.ipynb
• ex33e - agentchat webscraping with apify.ipynb
• ex33f - agent based calculator using llm.ipynb
• ex33f - agent persona sim using tinytroupe.ipynb
• ex33f - agentchat retrievechat.ipynb
• ex33f - intelligent agent writes and executes python scripts by human question.ipynb
• ex33f - intelligent code executor (personal use).ipynb
• ex33f - push any custom data to create qa pairs for rag system and push to huggingface.ipynb
• ex33f - using autogen to write python script.ipynb
• ex33g - agent chat retrieve chat with custom raw markdown file.ipynb
• ex34a - fine tune paligemma using huggingface vqa v2 dataset and custom data.ipynb
• ex34a - fine tune paligemma using huggingface vqa v2 dataset.ipynb
• ex34a - use fine tuned paligemma from huggingface to do inference.ipynb
• ex34b - code executor using google genai.ipynb
• ex34b - how to fine tune florence 2 on poker data.ipynb
• ex34c - visual question answering or vqa by torch.ipynb
• ex34c - vqa flava finetuning tutorial.ipynb
• ex34d - image captioning with ensemble methods and data augmentation.ipynb
• ex34d - image captioning with ensemble methods.ipynb
• ex35a - stable audio trial from huggingface.ipynb
• ex36a - wave2lip model_8_3 tutorial.ipynb
• ex37a - langchain basic tool calling framework.ipynb
• ex37b - langchain tool calling with structured output framework and langfuse tracing.ipynb
• ex37d - working with langchain agents.ipynb
• ex37e - langchain functions tools agents together.ipynb
• ex38a - anthropic claude api basic tutorial.ipynb
• ex38b - anthropic claude api custom tool calling.ipynb
• ex38c - anthropic claude api custom tool calling with pydantic basemodel for structured output.ipynb
• ex38d - anthropic claude api custom tool calling and multi-tool chat manager.ipynb
• ex38e - anthropic claude api client with multiple custom tool calling.ipynb
• ex38f - anthropic claude api client with multiple custom tool calling and with pydantic structured output.ipynb
• ex38g - anthropic claude api client with model context protocol or mcp.ipynb
• ex38h - anthropic claude api client with model context protocol and perplexity sec search as server.ipynb
• ex39a - openai api tutorial basics.ipynb
• ex39b - openai api tutorial tool calling.ipynb
• ex39c - openai api tutorial structured output.ipynb
• ex40a - perplexity sona client basics.ipynb
• ex40b - perplexity sona client structured output.ipynb
• ex40c - perplexity sona client with image input.ipynb
• ex40d - perplexity sona client with sec data ingest.ipynb
• ex40e - perplexity sona client with academic research mode.ipynb
• ex40f - perplexity sona client youtube demo.ipynb
• ex4a - more in cnn.ipynb
• ex4a - popular cnn walkthrough using wyn-keras.ipynb
• ex4a - popular cnn walkthrough.ipynb
• ex4b - 3d cnn using captcha ocr.ipynb
• ex4c - chestxray classification.ipynb
• ex4d - train cnn using hindi digits.ipynb
• ex5 - fine tuning neural network.ipynb
• ex6 - autoencoder.ipynb
• ex6 - image denoising.ipynb
• ex7 - neural network regressor + bayesian last layer.ipynb
• ex7 - vae.ipynb
• ex8 - inference of autoencoder.ipynb
• ex9 - image segmentation unet attention style.ipynb
• ex9 - image segmentation unet dense style.ipynb
• ex9 - image segmentation unet.ipynb
• jarvis - test run v1.ipynb
• jarvis - test run v2.ipynb
• jarvis - test run v3.ipynb
• jarvis - wyn voice tutorial.ipynb
• olympics-1-collect-data.ipynb
• olympics-2-create-qa.ipynb
• olympics-3-train-qa.ipynb
• qci ex1 - machine learning with qboost.ipynb
• qci ex2 - deep learning with qboost.ipynb