Data science/ML/AI

Accelerating Deep Learning with GPUs (Login Required) Training complex deep learning models with large datasets takes along time. In this course, you will learn how to use accelerated GPU hardware to overcome the scalability problem in deep learning. You can use accelerated hardware such as Google’s Tensor Processing Unit (TPU) or Nvidia GPU to accelerate your convolutional neural network computations time on the Cloud. These chips are specifically designed to support the training of neural networks, as well as the use of trained networks (inference). Accelerated hardware has recently been proven to significantly reduce training time. 🆓 Free Online Course Rating⭐️: 4.7 out 5 🎬 video lesson 🏃‍♂️ Self paced Duration ⏰: More than 7 hours worth of material Source: cognitiveclass 🔗 Course Link #deep_Learning ➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖ Join @datascience_bds for more cool data science materials. *This channel belongs to @bigdataspecialist group

Introduction to the Data Science Process

Data Science Ethics (Login Required) Utilize the framework provided in the course to analyze concerns related to data science ethics. Explore the broader impact of the data science field on modern society and the principles of fairness, accountability and transparency. Examine the need for voluntary disclosure when leveraging metadata to inform basic algorithms and/or complex artificial intelligence systems. Learn best practices for responsible data management. Gain an understanding of the significance of the Fair Information Practices Principles Act and the laws concerning the "right to be forgotten." 🎬 video lessons Rating⭐️: 4.1 out 5 🏃‍♂️ Self paced Source: University of Michigan 🔗 Course Link #data_science ➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖ Join @datascience_bds for more cool data science materials. *This channel belongs to @bigdataspecialist group

MIT 6.S191: Introduction to Deep Learning 2021 Created by MIT ⏰ 29 hours worth of material 🎬 43 Video lessons 👨‍🏫 Teacher: Alexander Amini 🔗 Course link #deeplearning #ai #MIT ➖➖➖➖➖➖➖➖➖➖➖➖➖➖ 👉Join @bigdataspecialist for more👈

Your Guide to Latent Dirichlet Allocation Latent Dirichlet Allocation (LDA) is a “generative probabilistic model” of a collection of composites made up of parts. Its uses include Natural Language Processing (NLP) and topic modelling, among others. In terms of topic modelling, the composites are documents and the parts are words and/or phrases (phrases n words in length are referred to as n-grams). But you could apply LDA to DNA and nucleotides, pizzas and toppings, molecules and atoms, employees and skills, or keyboards and crumbs. The probabilistic topic model estimated by LDA consists of two tables (matrices). The first table describes the probability or chance of selecting a particular part when sampling a particular topic (category). Link #ml #data_science ➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖ Join @datascience_bds for more cool data science materials. *This channel belongs to @bigdataspecialist group

Big and Sparse Data Sciences Integration with Theory, Experiment, Simulations, and Uncertainty Quantification

Data Science for Engineers, IIT Madras 🆓 Free Online Course 💻 50 Lecture Videos ⏰ 8 Module 🏃‍♂️ Self paced Teacher 👨‍🏫 : Prof. Shankar Narasimhan, Prof. Ragunathan Rengasamy 🔗 https://nptel.ac.in/courses/106106179 #Data_Science #IIT ➖➖➖➖➖➖➖➖➖➖➖➖➖➖ 👉Join @bigdataspecialist for more👈

Data Science Components

R for Data Science A weekly data project aimed at the R ecosystem. As this project was borne out of the R4DS Online Learning Community and the R for Data Science textbook, an emphasis was placed on understanding how to summarize and arrange data to make meaningful charts with ggplot2, tidyr, dplyr, and other tools in the tidyverse ecosystem. However, any code-based methodology is welcome - just please remember to share the code used to generate the results. Creator: rfordatascience Stars ⭐️: 5.6k Forked By: 2.3k https://github.com/rfordatascience/tidytuesday #R #data_science ➖➖➖➖➖➖➖➖➖➖➖➖➖➖ Join @datascience_bds for more cool repositories. *This channel belongs to @bigdataspecialist group