Data science consists of many algorithms, theories, components etc. Before detail study of data science, we need to understand them. Five basic components of data science are discussed here. Data is a collection of factual information based on numbers, words, observations, measurements which can be utilized for calculation, discussion and reasoning. The crude dataset is the basic foundation of data science and it may be of different kinds like Structured Data (Tabular structure), Unstructured Data (pictures, recordings, messages, PDF documents and so forth.)

In this notebook, we are going to take a baby step into the world of deep learning using PyTorch. There are already a ton of notebooks out there that teach you about deep learning and PyTorch. My goal here is to provide a foundation and introduction to deep learning using PyTorch. Therefore, this notebook is targeting beginners but it can also serve as a review for more experienced developers. After completion of this notebook, you are expected to know the basic components of training a basic neural network with PyTorch.

To drive, or not to drive, that is the question. In recent years, autonomous driving has emerged as a key mobility trend. For some, self-driving vehicles are the future -- a future in which drivers are freed from the burden of driving to make better use of their time while traveling. At Porsche, on the other hand, we regard human driving as a privilege: Porsche stands for performance, perfect handling and pure driving pleasure. That's why our customers want to drive their car themselves.

One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates. The course will also introduce a range of model based and algorithmic machine learning methods including regression, classification trees, Naive Bayes, and random forests. The course will cover the complete process of building prediction functions including data collection, feature creation, algorithms, and evaluation.

One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates. The course will also introduce a range of model based and algorithmic machine learning methods including regression, classification trees, Naive Bayes, and random forests. The course will cover the complete process of building prediction functions including data collection, feature creation, algorithms, and evaluation.

Hi! My name is Wick. I'm a neuroscientist from Portland, OR, and I study the neural circuits that move bodies (you can see my first published paper here). I believe that in order to eventually understand the impossibly complicated system that is the human brain, we need to start by first mastering its most basic components. There's so much that goes on beneath our awareness. My heart beats a steady rhythm, pumping blood through my billowing lungs down to my legs as I absentmindedly walk through the forest.

Session 1: Introduction to Tensorflow We'll cover the importance of data with machine and deep learning algorithms, the basics of creating a dataset, how to preprocess datasets, then jump into Tensorflow, a library for creating computational graphs built by Google Research. We'll learn the basic components of Tensorflow and see how to use it to filter images. Session 2: Training A Network W/ Tensorflow We'll see how neural networks work, how they are "trained", and see the basic components of training a neural network. We'll then build our first neural network and use it for a fun application of teaching a neural network how to paint an image. Session 3: Unsupervised And Supervised Learning This session goes deep.