The 2 links are given below:
Genetic Algorithms and Evolutionary Computation framework is implemented with a difference – one level of the algorithm is based on quantum computing. Quantum Genetic Algorithms have been around since 1998 but I hope to make a significant breakthrough in this one.
When I first started learning TensorFlow and Keras I was advised to do a few projects to improve my abilities as an ML engineer. This portfolio consists of Python, Sonar Classification, MNIST and even a Julia project.
My undergraduate final semester project. Parallelized a neural network over 8 separate computers and did Sonar Classification with 81% accuracy. Also did some high-performance computation like calculating 8 million digits of pi. Used Java, MPI and VB.NET.
The world’s most famous and easiest-to-learn machine learning toolbox in Python. Used in 5 libraries called the scikit-stack, NumPy, SciPy, Pandas, Scikit-Learn, and Matplotlib.
The open source implementation of the easiest language in the world written basically in C and some higher level libraries running on Python that act as C wrappers.
The data scientists dream – a language with the ease of Python and the performance of C++. As Julia’s its open source contributions increase, it can only grow in popularity.
A language that has great features, great industry support, and is starting to gain prominence in the job market. This is my most coveted coding language to add to my skill set.