After a brief meeting with the other interns in the morning (and receiving our prizes from the team building exercise yesterday), I started working in the Machine Vision Lab to establish a solid foundation of the math and programming packages that will be essential for this internship. After finishing setting up my workplace and resolving a few operating system issues, we successfully installed all the necessary software, such as anaconda, Spyder, Numpy, and Pytorch, for me to begin experimenting and utilizing the learning resources.
I started off reading a few articles and completing an edX course titled "Essential Math for Machine Learning- Python Edition". These materials reviewed the fundamentals of linear algebra (vectors, matrices, tensors, and their operations), calculus (multivariate differentiation, integration, etc.), and statistics/probability (measures of central tendency and variance, confidence intervals, sampling distributions, and hypothesis testing).
After becoming confident in the math upon which deep learning is founded, I transitioned into a Microsoft course titled "Introduction to Python for Data Science". This course was a great review of python programming practices which i haven't used in awhile and an easy introduction to the packages useful in python as well. These include Numpy (numeric python), Matplotlib (for data visualization), and Pandas (for dataframes storing data of different type).
Finally, I finished the day with a few more articles on the python packages Numpy and Pytorch. These tutorials gave me more in-depth knowledge around the uses of the packages and allowed me to start experimenting on my own in the Spyder IDE.
Overall, my second day was very informative, and I learned/reviewed a lot of material which I believe will be very useful in the future.
I started off reading a few articles and completing an edX course titled "Essential Math for Machine Learning- Python Edition". These materials reviewed the fundamentals of linear algebra (vectors, matrices, tensors, and their operations), calculus (multivariate differentiation, integration, etc.), and statistics/probability (measures of central tendency and variance, confidence intervals, sampling distributions, and hypothesis testing).
After becoming confident in the math upon which deep learning is founded, I transitioned into a Microsoft course titled "Introduction to Python for Data Science". This course was a great review of python programming practices which i haven't used in awhile and an easy introduction to the packages useful in python as well. These include Numpy (numeric python), Matplotlib (for data visualization), and Pandas (for dataframes storing data of different type).
Finally, I finished the day with a few more articles on the python packages Numpy and Pytorch. These tutorials gave me more in-depth knowledge around the uses of the packages and allowed me to start experimenting on my own in the Spyder IDE.
Overall, my second day was very informative, and I learned/reviewed a lot of material which I believe will be very useful in the future.
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