{"id":22277321,"url":"https://github.com/zohebabai/deep-learning-projects","last_synced_at":"2025-07-03T02:37:58.506Z","repository":{"id":37876635,"uuid":"151380121","full_name":"ZohebAbai/Deep-Learning-Projects","owner":"ZohebAbai","description":"Best Deep Learning Projects for Advanced Learners","archived":false,"fork":false,"pushed_at":"2022-11-03T13:42:45.000Z","size":257450,"stargazers_count":23,"open_issues_count":0,"forks_count":10,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-04-15T12:29:04.351Z","etag":null,"topics":["cnn","computer-vision","deep-learning","gans","jax","keras","machine-learning","neural-network","nlp","pytorch","tensorflow","transfer-learning","transformers","tutorial","vae"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ZohebAbai.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2018-10-03T08:05:51.000Z","updated_at":"2025-03-05T07:22:02.000Z","dependencies_parsed_at":"2022-08-08T22:15:40.449Z","dependency_job_id":null,"html_url":"https://github.com/ZohebAbai/Deep-Learning-Projects","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/ZohebAbai/Deep-Learning-Projects","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ZohebAbai%2FDeep-Learning-Projects","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ZohebAbai%2FDeep-Learning-Projects/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ZohebAbai%2FDeep-Learning-Projects/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ZohebAbai%2FDeep-Learning-Projects/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ZohebAbai","download_url":"https://codeload.github.com/ZohebAbai/Deep-Learning-Projects/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ZohebAbai%2FDeep-Learning-Projects/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":263248815,"owners_count":23437095,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["cnn","computer-vision","deep-learning","gans","jax","keras","machine-learning","neural-network","nlp","pytorch","tensorflow","transfer-learning","transformers","tutorial","vae"],"created_at":"2024-12-03T14:22:17.360Z","updated_at":"2025-07-03T02:37:58.482Z","avatar_url":"https://github.com/ZohebAbai.png","language":"Jupyter Notebook","readme":"# Best Deep Learning Projects for Advanced Learners [2022 Updated]\n\n[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/ZohebAbai/Deep-Learning-Projects/master)\n\n![welcome](https://media0.giphy.com/media/xUPGGDNsLvqsBOhuU0/giphy.gif?cid=ecf05e47mxzkfopuw507aun32t74ggidrxflwrvb779i1874\u0026rid=giphy.gif)\n\n#### Using both Tensorflow and PyTorch Libraries\n\n**Get a glimpse of how similar/different these libraries are:**\n[Pytorch vs Tensorflow on MNIST dataset](https://nbviewer.org/github/ZohebAbai/Deep-Learning-Projects/blob/master/Pytorch_vs_Tensorflow.ipynb)\n\n**In each notebook, we shall train using free Google Colab resources and eventually deploy them as gradio/streamlit app (depending on projects).**\n\n## Notebooks:\n\n### Fundamentals\n* **Tensorflow Fundamentals** [TF Tensors Basics](https://nbviewer.org/github/ZohebAbai/Deep-Learning-Projects/blob/master/00_Tensorflow_Fundamentals.ipynb)\n\t- Constants and Variables\n\t- Compatibility with Numpy\n\t- Random Generators\n\t- Basic Operations\n* **Pytorch Fundamentals** [PT Tensors Basics](https://nbviewer.org/github/ZohebAbai/Deep-Learning-Projects/blob/master/00_Pytorch_Fundamentals.ipynb)\n\t- Tensor Basic\n\t- Interoperability with Numpy\n\t- Basic Operations\n\t- Regression Model Training with Custom Data on GPU\n\n### Structured Data\t\n* **Regression** - [Custom TF Model on Medical Insurance Dataset](https://nbviewer.org/github/ZohebAbai/Deep-Learning-Projects/blob/master/01_TF_Regression.ipynb)\n\t- Minimal EDA\n\t- k-Fold Cross Validation\n\t- L1 Regularizers\n\t- Gradio App\n\n### Computer Vision\n* **Image Classification** - [Custom TF Model on Cifar10 Dataset](https://nbviewer.org/github/ZohebAbai/Deep-Learning-Projects/blob/master/02_TF_Image_Classification.ipynb)\n\t- Image Augmentation\n\t- LR Finder\n\t- One-Cycle LR Scheduler\n\t- GradCAM visualisation\n\t- Gradio App\n* **Multi-Label Image Classification** - [TF Transfer Learning on Custom Dataset](https://nbviewer.org/github/ZohebAbai/Deep-Learning-Projects/blob/master/03_TF_Multilabel_Image_Classification.ipynb)\n\t- Custom Dataset \n\t- TF Record with Image Augmentation\n\t- Custom Loss Function\n\t- Transfer Learning\n\t- Performance Profiling\n\t- Gradio App\n* **Image Generation** - [TF VAE Image Generation on Celeb Faces](https://nbviewer.org/github/ZohebAbai/Deep-Learning-Projects/blob/master/04_TF_Image_Generation.ipynb)\n\t- Custom Architecture using Probabilistic Layers\n\t- Reduce LR on Plateau Scheduler\n\t- New Generated Faces\n\t- Reconstructing Faces\n\t- Feature Manipulation\n\t- Face Morphing\n\t- Visualize clusters on UMAP-reduced 1D latent vector\n* **Metric Learning** - [TF Similarity Models on Dog-Cat Breed Dataset](https://nbviewer.org/github/ZohebAbai/Deep-Learning-Projects/blob/master/05_TF_Metric_Learning.ipynb)\n\t- Tensorflow Similarity\n\t- Transfer Learning with an embedding layer and Multisimilarity loss\n\t- ANN Search: Indexing, Calibration, Querying \n\t- Precision-Recall Curve\n\t- UMAP-reduced clustering with interactive visualization\n\n* **Image Translation** - [TF Pix2Pix on Edges-to-Handbags Dataset](https://nbviewer.org/github/ZohebAbai/Deep-Learning-Projects/blob/master/08_TF_Pix2Pix_on_Edges2Handbags.ipynb)\n\t- Understanding Pix2Pix Architecture\n\t- Training it from scratch with additional loss fucntion\n\t- Focal Frequency Loss\n\t- Using Tensorboard during model training\n\t- Image Generation\n\n* **Image Segmentation** - [HF SegFormer on Road-Sidewalk Dataset](https://nbviewer.org/github/ZohebAbai/Deep-Learning-Projects/blob/master/09_HF_Image_Segmentation_using_Transformers.ipynb)\n\t- Understanding Semantic Segmentation using Transformers\n\t- Fine-Tuning it using Huggingface Modules\n\t- Mean IOU metric\n\t- [Publishing as HF Model](https://huggingface.co/zoheb/mit-b5-finetuned-sidewalk-semantic)\n\t- [Live Inference Model](https://huggingface.co/spaces/zoheb/segformer_demo) \n\n* **Object Detection** - [PT YOLOS on Matterport Balloons Dataset](https://nbviewer.org/github/ZohebAbai/Deep-Learning-Projects/blob/master/10_PT_Object_Detection_using_Transformers.ipynb)\n\t- Understanding Object Detection using Transformers\n\t- Fine-Tuning YOLOS using Pytorch Lightning\n\t- Detecting object on a video \n\t- Viewing Attention Layers\n\t- [Publishing as HF Model](https://huggingface.co/zoheb/yolos-small-balloon)\n\t- [Live Inference Model](https://huggingface.co/spaces/zoheb/yolos_demo) \n\n### Natural Language Processing\n* **Pre-Neural NLP** - [Heuristics-based \u0026 Statistical Methods in NLP](https://nbviewer.org/github/ZohebAbai/Deep-Learning-Projects/blob/master/00_Pre_Neural_NLP.ipynb)\n\t- Basics of Sentiment Analysis\n\t- Valence Aware Dictionary and Sentiment Reasoner (VADER)\n\t- Support Vector Machines (SVM)\n\t- Grid Search for Hyperparameters\n\t- ROC Curve\n* **Understanding Vanilla Transformers** - [Vanilla Transformers](https://nbviewer.org/github/ZohebAbai/Deep-Learning-Projects/blob/master/06_Understanding_Vanilla_Transformers.ipynb)\n\t- Understanding Seq2Seq Models\n\t- Understanding Attention Mechanism\n\t- Understanding Transformer Architecture\n* **Vanilla Transformer Comment to Code** - [PT Train Vanilla Transformer (Sequence to Sequence)](https://nbviewer.org/github/ZohebAbai/Deep-Learning-Projects/blob/master/07_Vanilla_Transformer_Comment_to_Code.ipynb)\n\t- Dataset Augmentation\n\t- Custom Tokenizer\n\t- Build Complete Transformer Architecture \n\t- Custom Loss\n\t- Display Attention\n\t- Gradio App\n\n### Joint CV \u0026 NLP\n* **Stable Diffusion** - [HF Stable Diffusion Text to Image](https://nbviewer.org/github/ZohebAbai/Deep-Learning-Projects/blob/master/11_HF_Stable_Diffusion_Text_to_Image.ipynb)\n\t- Understanding Diffusion Models (Stable diffusion in particular)\n\t- Exploring Diffusers Library\n\t- Writing an inference pipeline\n\t- Understanding the complete generative process during inference\n  \n### Experimental (Excellent ML Applications of few yet not stable libraries)\n* **JAX Basics** - [JAX Basics](https://nbviewer.org/github/ZohebAbai/Deep-Learning-Projects/blob/master/JAX_Basics.ipynb)\n\t- Why JAX?\n\t- How randomness is handled\n\t- Speed Comparison\n\t- Asynchronous Dispatch\n\t- JIT Compilation\n\t- Auto-differentiation with grad\n\t- Auto-vectorization with Vmap\n\t- SPMD Programming with Pmap on TPU\n\t- Device Memory Profiler\n    \n* **PySyft - Secure and Privacy AI**\n\t- [Data Owner](https://nbviewer.org/github/ZohebAbai/Deep-Learning-Projects/blob/master/Data_Owner.ipynb)\n\t- [Data Scientist](https://nbviewer.org/github/ZohebAbai/Deep-Learning-Projects/blob/master/Data_Scientist.ipynb)\n\t- Differential Privacy\n\t- Remote Data Science\n\t- Covid-19 trends prediction\n\n* **TenSeal - Homomorphic Encryption on Tensors**\n\t- [Homomorphic Encryption Basic and Encrypted Inference](https://nbviewer.org/github/ZohebAbai/Deep-Learning-Projects/blob/master/HE_Basics_n_Inference.ipynb)\n\t- [Homomorphic Encryption NN Training](https://nbviewer.org/github/ZohebAbai/Deep-Learning-Projects/blob/master/HE_Training.ipynb)\n\t- Tenseal Context\n\t- Basic Mathematical Operations on Encrypted Tensors\n\t- Encrypted Evaluation on Encrypted Test Data\n\t- Training Encrypted NN on Encrypted Data \n\n\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fzohebabai%2Fdeep-learning-projects","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fzohebabai%2Fdeep-learning-projects","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fzohebabai%2Fdeep-learning-projects/lists"}