🎥 Data Science Tutorial - NO EXP REQUIRED | Python - #grindreel #lambdaschool
👁 1 раз ⏳ 858 сек.
🔥 Land the job! Get help with a resume and cover letter https://bit.ly/2CNoxTm
📚My Courses: https://grindreel.academy/
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Want to work at Google? Cheat Sheet: https://goo.gl/N56orD

Code Bootcamps I've worked with: 🏫
Lambda School: FREE until you get a job: https://lambda-school.sjv.io/josh

Support the channel! ❤️
https://www.patreon.com/joshuafluke
Donations: paypal.me

​bentoML: One Model to Rule Them All

🔗 bentoML: One Model to Rule Them All
The machine learning community focuses too much on predictive performance. But machine learning models are always a small part of a complex system. This post discusses our obsession with finding the best model and emphasizes what we should do instead: Take a step back and see the bigger picture in which the machine learning model is embedded.

🎥 AI in 2040
👁 18 раз ⏳ 781 сек.
What does the field of Artificial Intelligence look like in 2040? It's a really hard question to answer since there are still so many unanswered questions about the nature of reality and computing. In this episode, I'll make my best predictions about AI hardware, AI software, and the societal impact of AI in 2040. We'll cover quantum mechanics, neuromorphic computing, DNA storage, decentralized computing, basic income, and mind-body machines. Enjoy!

Code for this video:
https://github.com/llSourcell/quant

🎥 Deep neural networks step by step forward propagation #part 2
👁 1 раз ⏳ 1243 сек.
Now when we have initialized our parameters, we will do the forward propagation module. We will start by implementing some basic functions that we will use later when implementing the model. We will complete three functions in this order:
• LINEAR
• LINEAR - ACTIVATION where ACTIVATION will be either ReLU or Sigmoid.
• [LINEAR - RELU] × (L-1) - LINEAR - SIGMOID (whole model)

I could write all these functions in one block, but then it's harder to understand code, so I will leave it so for learning purposes.

🎥 Theoretical Deep Learning. The Information Bottleneck method. Part 2
👁 1 раз ⏳ 5876 сек.
In this class we continue discussing how can we use the information bottleneck framework for neural networks study. In particular, we learn how to prevent NNs from overfitting by introducing a specific penalty term into the loss function, and reveal an objective very similar to evidence lower bound from bayesian statistics.

Find out more: https://github.com/deepmipt/tdl

Our open-source framework to develop and deploy conversational assistants: https://deeppavlov.ai/

​http://www.cs.cornell.edu/courses/cs4780/2018fa/lectures/

🔗

​Liberty Mutual Insurance joins MIT's Quest for Intelligence

http://news.mit.edu/2019/liberty-mutual-insurance-establishes-artificial-intelligence-collaboration-mit-0430

🔗 Liberty Mutual Insurance joins MIT's Quest for Intelligence
Company announces $25 million, five-year collaboration.

🎥 Webinar: Question Answering and Virtual Assistants with Deep Learning
👁 1 раз ⏳ 3206 сек.
In this webinar, we’ll look at how Deep Learning can be used to create Question Answering (QA) and Virtual Assistant type systems.

You will learn about:
- Typical use cases of QA systems in finance, insurance, and ecommerce
- The power of neural search compared to traditional keyword search
- The challenges of large-scale neural search and how to overcome them

Presenters:
Sava Kalbachou, AI Research Engineer, Lucidworks
Andy Liu, Senior Data Scientist, Lucidworks
Justin Sears, VP of Product Market

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​Plotting business locations on maps using multiple Plotting libraries in Python

🔗 Plotting business locations on maps using multiple Plotting libraries in Python
Comparing Map Plotting libraries

​Activation Atlas

🔗 Activation Atlas
By using feature inversion to visualize millions of activations from an image classification network, we create an explorable activation atlas of features the network has learned and what concepts it typically represents.

​Deep Learning Book Series 3.1 to 3.3 Probability Mass and Density Functions

🔗 Deep Learning Book Series 3.1 to 3.3 Probability Mass and Density Functions
This content is part of a series about Chapter 3 on probability from the Deep Learning Book by Goodfellow, I., Bengio, Y., and Courville…

🎥 Improving TripAdvisor Photo Selection With Deep Learning
👁 3 раз ⏳ 914 сек.
#reworkDL

This presentation took place at the Deep Learning Summit, Boston 2018 and was given by Greg Amis, Principal Software Engineer at TripAdvisor.

For more presentations & interviews from the Deep Learning Summit, Boston 2018, head to the Video Hub here: http://videos.re-work.co/events/39-deep-learning-summit-boston-2018

🎥 Achieving Continuous Machine and Deep Learning with Apache Ignite and TensorFlow
👁 1 раз ⏳ 2794 сек.
To download the presentation slides, visit: https://www.gridgain.com/resources/technical-presentations/achieving-continuous-deep-and-machine-learning-apache-ignite-and

With most machine learning (ML) and deep learning (DL) frameworks, it can take hours to move data, and hours to train models. Learn how Apache Ignite eliminates runs model training and execution in near-real-time and makes continuous learning possible.

🎥 Deep Learning на пальцах 11 - Аудио и распознавание речи (Юрий Бабуров)
👁 2 раз ⏳ 0 сек.

​April Edition: Reinforcement Learning

🔗 April Edition: Reinforcement Learning
How to Build an Actual Artificial Intelligence Agent

​Support Vector Machines — Soft Margin Formulation and Kernel Trick

🔗 Support Vector Machines — Soft Margin Formulation and Kernel Trick
Learn some of the advanced concepts that make Support Vector Machine a powerful linear classifier

​Deep Learning on Ancient DNA

🔗 Deep Learning on Ancient DNA
Reconstructing the Human Past with Deep Learning

​Python for Finance: Robo Advisor Edition

🔗 Python for Finance: Robo Advisor Edition
Extending Stock Portfolio Analyses and Dash by Plotly to track Robo Advisor-like Portfolios.