CS231n Convolutional Neural Networks (Deep Learning)

CS231n: Convolutional Neural Networks (Deep Learning)
English | Size: 4.75 GB
Category: tUTORIAL

Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Recent developments in neural network (aka “deep learning”) approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This course is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. During the 10-week course, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision. The final assignment will involve training a multi-million parameter convolutional neural network and applying it on the largest image classification dataset (ImageNet). We will focus on teaching how to set up the problem of image recognition, the learning algorithms (e.g. backpropagation), practical engineering tricks for training and fine-tuning the networks and guide the students through hands-on assignments and a final course project. Much of the background and materials of this course will be drawn from the ImageNet Challenge. [Read more…]

Data Science, Deep Learning, & Machine Learning with Python

Data Science, Deep Learning, & Machine Learning with Python
English | Size: 3.07 GB
Category: Career

What Will I Learn?

Develop using iPython notebooks
Understand statistical measures such as standard deviation
Visualize data distributions, probability mass functions, and probability density functions
Visualize data with matplotlib
Use covariance and correlation metrics
Apply conditional probability for finding correlated features
Use Bayes’ Theorem to identify false positives
Make predictions using linear regression, polynomial regression, and multivariate regression
Understand complex multi-level models
Use train/test and K-Fold cross validation to choose the right model [Read more…]

Packt Publishing – Applied Machine Learning and Deep Learning with R

Packt Publishing – Applied Machine Learning and Deep Learning with R
English | Size: 575.56 MB
Category: CBTs

A step-by-step real world guide on machine learning and deep learning that takes you through the core aspects for building powerful data science applications with the help of the R programming language

In this course, we will examine in detail the R software, which is the most popular statistical programming language of recent years.
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O’Reilly – Avoiding the Pitfalls of Deep Learning

O’Reilly – Avoiding the Pitfalls of Deep Learning
English | Size: 435.73 MB
Category: Tutorial

Understanding how to create a deep learning neural network is an essential component of any data scientist’s knowledge base. This video continues the explanation of how to build neural networks using Python and MXNet (a flexible and efficient deep learning library) described in "Introduction to Deep Learning with MXNet." This course covers some of the challenges that arise when training neural networks. It focuses on the problem of overfitting and its potential remedy: regularization. Learners should have a basic understanding of Python, linear algebra, and calculus.
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Data Science Deep Learning in Python

Data Science Deep Learning in Python
English | Size: 722.7 MB
Category: Programming | Cloud-Comp | E-learning | others

A guide for writing your own neural network in Python and Numpy, and how to do it in Google’s TensorFlow.
This course will get you started in building your FIRST artificial neural network using deep learning techniques. Following my previous course on logistic regression, we take this basic building block, and build full-on non-linear neural networks right out of the gate using Python and Numpy. All the materials for this course are FREE. [Read more…]