Education & Training Info: Neural Networks, Machine Learning & AI
At Seeflection, we’re actively engaged in learning more about our core areas of research—as well as new tools, technologies and approaches. Also, given the heightened importance and utility that interdisciplinary knowledge holds for AI, we encourage all of our teams to stay abreast of key news and information across a broad range of AI-related areas.
To facilitate this, we have a team of writers, researchers and related staff that employ AI to help them comb tens of thousands of resources every day, to help find, distill, and summarize important news and information. We provide free access to this constantly growing compendium, as part of the industry-wide initiative of fostering open, collaborate sharing of knowledge and resources.
Check back periodically for updates, as we’re always adding to our growing base of knowledge.
YouTube & Other Video Resources:
CNNs for Visual Recognition
CognitiveClass.AI (YouTube Channel)
DeepLearning.TV: Neural Networks
NLPs for Deep Learning
Siraj Raval – YouTube
Online Courses (Free):
CognitiveClass.AI
Data Camp – (Beginner)
CS231n Convolutional Neural Networks for Visual Recognition
Deep Learning
Google Codelabs
Intro to Python
Intro to R
Intro to SQL
Learn to code by doing | Code School
Python Learning Path- Beginner to Advanced | Pluralsight
Other Educational Links & Resources:
19 Data Science Tools for non-programmers and interns
30 Essential Data Science, Machine Learning & Deep Learning Cheat Sheets
A Beginner’s Guide To Understanding Convolutional Neural Networks – Adit Deshpande – CS Undergrad at UCLA (’19)
A Neural Network Playground
A.I. Experiments
An Intuitive Explanation of Convolutional Neural Networks – The Data Science Blog
Building a Neural Network from Scratch in Python and in TensorFlow – Nick Becker
Deep Learning Cheat Sheet – Hacker Noon
Deeplearning.net
Essential Cheat Sheets for Machine Learning and Deep Learning Engineers
How I Made a Neural Network Web Application in an Hour
Machine Learning is Fun! – Adam Geitgey – Medium
MiaBella Neural Net
Neural network & its applications
Reading List For Data Scientists
The 9 Deep Learning Papers You Need To Know About (Understanding CNNs Part 3)
The Neural Network Zoo – The Asimov Institute
Top 50 Artificial Intelligence Websites And Blogs for AI Enthusiast | AI Websites
Visualizing Neural Networks in 3D
Which Supervised Learning Method Works Best for What? An Empirical Comparison of Learning Methods and Metrics – VideoLectures.NET
Data Repositories, Research, & Other Technical Resources:
7 Top Python GUI Frameworks for 2017 – Dice Insights
A Fast Learning Algorithm for Deep Belief Nets | Neural
Computation | MIT Press Journals
Artificial Intelligence A-Z™: Download Code Templates – SuperDataScience – Big Data |Analytics Careers | Mentors | Success
Clarifai | Image & Video Recognition API
Continuous online video classification with TensorFlow, Inception and a Raspberry Pi
crowdAI
CS231n Convolutional Neural Networks for Visual Recognition
Datasets | Kaggle
Distill — Latest articles about machine learning
GitHub – jtoy/awesome-tensorflow: TensorFlow – A curated list of dedicated resources http://tensorflow.org
GitHub – mprat/pascal-voc-python: Repository for reading Pascal VOC data in Python, rather than requiring MATLAB to read the XML files.
GitHub – zer0n/deepframeworks: Evaluation of Deep Learning Frameworks
Google Research Blog
BAIR Model Zoo for Caffe
DeepScale/SqueezeNet: SqueezeNet: AlexNet-level accuracy with 50x fewer parameters-GitHub
Google’s Deep Dream in PyCharm | PyCharm Blog
Google’s Entry to ImageNet
https://Hub.Docker.com
Jupyter Notebook Archives – Artificial Intelligence
PyPI – the Python Package Index : Python Package Index
Python Online: The 10 Best Tools to Edit and Compile Python
Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex
tensorflow/tensorflow – Docker Hub
The Comprehensive R Archive Network
The PASCAL Visual Object Classes Homepage
The PASCAL Visual Object Classes Homepage
Very Deep Convolutional Networks for Lare-Scale Visual Recognition – Visual Geometry Group, Department of Engineering Science, University of Oxford
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