Built a custom adam scheduler using gradient clipping, LR scheduling, momentum updates, with two different loss functions
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Updated
Jan 20, 2024 - Python
Built a custom adam scheduler using gradient clipping, LR scheduling, momentum updates, with two different loss functions
Design and train a network that combines supervised and unsupervised architecture in one model to achieve a classification task
Retrieval based biomedical chatbot to answer questions related to diseases
My solutions for Stanford's CS231n: Convolutional Neural Networks for Visual Recognition.
Applying Transfer Learning on Stanford Dogs Dataset
Effect of Optimizer Selection and Hyperparameter Tuning on Training Efficiency and LLM Performance
Activity recognition aims to recognize the actions and goals of one or more agents from a series of observations on the agents' actions and the environmental conditions.
🧠Implementation of a Neural Network from scratch in Python for the Machine Learning Course.
Detect and classify toxic behavior in social media comments using a bidirectional LSTM-based neural network. Achieved precision of 0.932 and recall of 0.733. Applications include customer service, reputation management, and market research. Real-time predictions available via a Gradio app. Future scope includes multi-lingual sentiment analysis.
Objective here is to explore the concepts of deep learning and implementing it from scratch in a structured way.
A simple study on the use of Keras framework (with Tensorflow background) for a simple handwritten number image classification task with Deep Neural Networks.
A deep learning classification program to detect the CT-scan results using python
Logistic regression and stochastic gradient descent approaches used to predict a binomial variable
Analyze the performance of 7 optimizers by varying their learning rates
Deep Learning Course | Home Works | Spring 2021 | Dr. MohammadReza Mohammadi
Using Keras to build a deep neural network for bladder cancer progression
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