#Jupyter Notebook

Discover extensive deep learning resources featuring expert-led video lectures, comprehensive notes, and practical datasets. Perfect for both beginners and advanced learners aiming to enhance AI skills and employability, including in top firms like United Imaging. Join collaborative groups for valuable insights in AI applications.

BertViz is a tool for visualizing attention mechanisms in Transformer models such as BERT, GPT-2, and T5. It supports Jupyter and Colab environments via a Python API, compatible with Huggingface models. By enhancing the Tensor2Tensor framework, BertViz provides unique insights through head, model, and neuron views, aiding researchers and developers in exploring attention layers.

TensorWatch offers flexible debugging and visualization for ML, integrating with Jupyter Notebook, and supporting tools like PyTorch and TensorFlow. Features include custom visuals, lazy logging, and diverse plots, aiding model training and prediction explanations. Requires Python 3.x and Graphviz.

This guide offers an in-depth look at prompt engineering with ChatGPT, tailored for developers. It explains the workings of language models and shares practical strategies for their effective use. Featuring hands-on examples with Jupyter Notebook code and OpenAI's API, the course facilitates the rapid creation of impactful applications. Covered topics include summarization, inference, text transformation, and chatbot development. Created by experts Isa Fulford and Andrew Ng, this resource supports prompt skill enhancement with bilingual course content and CLI command scripts to foster practical experience.

Access NYU's Spring 2020 deep learning course featuring comprehensive video and text materials on a dedicated website. Learn how to set up a development environment using Miniconda and Jupyter for interactive data exploration. This multilingual resource serves a global audience interested in mastering deep learning techniques.

Inspectus is a tool designed for visualizing machine learning processes, integrating seamlessly into Jupyter notebooks with an easy Python API. It offers tools for visualizing attention mechanisms, analyzing token metrics, and plotting data distributions, aiding in comprehending deep learning models. Features such as heatmaps for query and key tokens and customizable attention maps improve model insight. Additionally, its ability to plot data distributions assists in outlier detection and model behavior analysis. Suitable for researchers and developers, Inspectus supports Huggingface models, providing a detailed view of deep learning frameworks.

This repository offers Jupyter Notebooks in alignment with Andrej Karpathy's 'Neural Networks: Zero to Hero' tutorial series. It delivers detailed insights into neural network fundamentals and advanced techniques, including GPT and GPT-2. The materials support learners in expanding their practical understanding of topics such as Micrograd, WaveNet, and GPT tokenization. Contributions to improve the repository are encouraged, fostering collaboration and continuous learning.