uc-berkeley-engineering
There are 6 repositories under uc-berkeley-engineering topic.
ehcastroh/intro_DATAVIZ
DATA-X: m130 - Introduction to Visual Principles Using Matplotlib and Seaborn. Provides users with the necessary foundations for building and understanding current state of the art visualizations. An additional aim is to provide users with an understanding of both the theory and techniques of various visualization paradigms. Finally, this series of notebooks seeks to provide sufficient knowledge to users so that they may build & evaluate various visualization systems, read & discuss visualization literature, and successfully convey visual information.
ehcastroh/intro_TENSORFLOW
DATA-X: m410 - TensorFlow - Shallow Neural Networks; An Introduction to TensorFlow V.2. Tensorflow (TF) is an open-source library used for dataflow, differentiable programming, symbolic math, and machine learning applications such as deep learning neural networks. TF's flexible architecture allows for easy deployment across varied processing platforms. This notebook covers advanced topics in machine learning. However, it does not require any prior knowledge in machine learning. The goal of this notebook is to teach a user how to deploy a TF model, as well as to provide the user guidance on how to tackle the more nuanced topics.
ehcastroh/intro_FLASK
DATA-X: m320 - Flask - Easy Web Development for Rapid Deployment. Provides a quick overview of how to set up a barebones Flask environment. This material can then be used to learn how to productionize ML models, build dynamic dashboard, and build complete websites -- quickly and easily.
ehcastroh/intro_NUMPY
DATA-X: m110 - Numpy - Introduction to Numerical Analysis Using NumPy. These materials introduce developers and data scientists to numerical analysis and data manipulation using NumPy. NumPy is the numerical analysis backbone to several popular open source analysis and machine learning packages.
ehcastroh/intro_PANDAS
DATA-X: m120 - Pandas - Introduction to Data Analysis Using Pandas. Pandas is a commonly used, yet powerful, software library written for Python that is built for expedient data manipulation and analysis. This notebook aims to introduce the syntax, data structures, and manipulation operations commonly seen in Pandas.
ehcastroh/reg_clas_TF_LUDWIG
DATA-X: m420 - Bread & Butter Deep Learning: Regression and Classification using TensorFlow v2 and Ludwig. This notebook covers advanced topics in machine learning. However, it does not require any prior knowledge in machine learning. The goal of this notebook is to teach a user how to deploy deep learning regression and classification models, using structured data. This is task is so common to machine learning, that it is pretty much the bread and butter of ML engineers.