ankurshukla03
I am a CS Masters student and I have a keen interest in Machine learning specially in the field of Deep learning.
Uppsala, Sweden
Pinned Repositories
AppliedML_Coursera
Programming tasks to the University of Michigan's "Applied Machine Learning in Python" course.
Assembler-master
This assembler is a 2 pass assembler in C language.The first pass seperates the jump statements into a file symbols.txt and the second pass converts the source code into machine language and stores the output in output.txt
awesome-datascience
:memo: An awesome Data Science repository to learn and apply for real world problems.
awesome-deep-learning
A curated list of awesome Deep Learning tutorials, projects and communities.
awesome-semantic-segmentation
:metal: awesome-semantic-segmentation
CAIA_Project
Capstone_ML_IBM
data-science-ipython-notebooks
Continually updated data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
deep-learning-v2-pytorch
Projects and exercises for the latest Deep Learning ND program https://www.udacity.com/course/deep-learning-nanodegree--nd101
DeepLearning_Coursera
Deep Learning Specialization
ankurshukla03's Repositories
ankurshukla03/awesome-datascience
:memo: An awesome Data Science repository to learn and apply for real world problems.
ankurshukla03/data-science-ipython-notebooks
Continually updated data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
ankurshukla03/ImageAnalysis2Labs
Image Analysis II Lab Assignments
ankurshukla03/Plinth_2
Lnmiit Technical Fest App
ankurshukla03/pyspider
A Powerful Spider(Web Crawler) System in Python.
ankurshukla03/UNet-in-Tensorflow
U-Net implementation in Tensorflow
ankurshukla03/unet-pytorch
U-Net implementation for PyTorch based on https://arxiv.org/abs/1505.04597