Ibrahim Sobh

llms. Large Language Models: In this repository Language models are introduced covering both theoretical and practical aspects.

Transformers. Transformers and related deep network architectures are summarized and implemented here.

imageclassification. Deep Learning: Image classification, feature visualization and transfer learning with Keras

Practical-DRL. This is a practical resource that makes it easier to learn about and apply Practical Deep Reinforcement Learning (DRL) https://ibrahimsobh.github.io/Practical-DRL/

see-inside-cnn. Going deeper into Deep CNNs through visualization methods: Saliency maps, optimize a random input image and deep dreaming with Keras

Segmentation. In this tutorial, you will perform inference across 10 well-known pre-trained semantic segmentors and fine-tune on a custom dataset. Design and train your own segmentor.

Object-Detection. In this tutorial, you will perform inference across 10 well-known pre-trained object detectors and fine-tune on a custom dataset. Design and train your own object detector.

kaggle-COVID19-Classification. COVID-19 is an infectious disease. The current outbreak was officially recognized as a pandemic by the World Health Organization (WHO) on 11 March 2020. X-ray machines are widely available and provide images for diagnosis quickly so chest X-ray images can be very useful in early diagnosis of COVID-19. In this classification project, there are three classes: COVID19, PNEUMONIA, and NORMAL

kaggle-Flower-Classification-TPUs. TPUs are powerful hardware accelerators specialized in deep learning tasks, now available on Kaggle. https://ibrahimsobh.github.io/kaggle-Flower-Classification-TPUs/

askdoc. In this tutorial we will see 💡 How to get answers from documents using Chroma vector database, PaLM LLM by Google, and a question answering chain from LangChain. And finally, use Streamlit to develop and host the web application. You will need to use your google_api_key (you can get one from Google).

deep-learning-with-python-notebooks. Jupyter notebooks for the code samples of the book "Deep Learning with Python"

AskYouTube. In this tutorial we will see 💡 How to get answers from a YouTube video using Chroma vector database, PaLM LLM by Google, and a question answering chain from LangChain. And finally, use Streamlit to develop and host the web application. You will need to use your google_api_key (you can get one from Google).

DeepRL-Agents. A set of Deep Reinforcement Learning Agents implemented in Tensorflow.

askpdf. In this tutorial we will see 💡 How to get answers from a PDF file using Chroma vector database, PaLM LLM by Google, and a question answering chain from LangChain. And finally, use Streamlit to develop and host the web application. You will need to use your google_api_key (you can get one from Google).

reinforcement-learning. Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.

introduction_to_ml_with_python. Notebooks and code for the book "Introduction to Machine Learning with Python"

streamlit-example. Example Streamlit app that you can fork to test out share.streamlit.io

dlaicourse. Notebooks for learning deep learning