​Наш телеграм канал - tglink.me/ai_machinelearning_big_data

https://towardsdatascience.com/6-bits-of-advice-for-data-scientists-6e5758c52fb2?source=collection_home---4------3---------------------

🔗 6 bits of advice for Data Scientists
Syndromes, Hypotheses, Fallacies, Lies, Awareness, and Probabilities

​Optimal Control: LQR

🔗 Optimal Control: LQR
Intuitive ground-up explanation to LQR, a fundamental concept in optimal control.

​Trick Out Your Terminal in 10 Minutes or Less

🔗 Trick Out Your Terminal in 10 Minutes or Less
How to make a better, faster, stronger, and sexier terminal in mere minutes

🎥 Unsupervised Learning in NLP
👁 1 раз ⏳ 2051 сек.
In this video we learn how to perform topic modeling using unsupervised learning in natural language processing.

Our goal is to train a model that generates topics from a given document/collection of text, without us telling it what the topics are/may be.

LinkedIn: https://www.linkedin.com/in/carlos-lara-1055a16b/
Email: info@poincaregroup.com
Website: https://www.poincaregroup.com

​https://arxiv.org/abs/1903.10176

🔗 DeepRED: Deep Image Prior Powered by RED
Inverse problems in imaging are extensively studied, with a variety of strategies, tools, and theory that have been accumulated over the years. Recently, this field has been immensely influenced by the emergence of deep-learning techniques. One such contribution, which is the focus of this paper, is the Deep Image Prior (DIP) work by Ulyanov, Vedaldi, and Lempitsky (2018). DIP offers a new approach towards the regularization of inverse problems, obtained by forcing the recovered image to be synthesized from a given deep architecture. While DIP has been shown to be effective, its results fall short when compared to state-of-the-art alternatives. In this work, we aim to boost DIP by adding an explicit prior, which enriches the overall regularization effect in order to lead to better-recovered images. More specifically, we propose to bring-in the concept of Regularization by Denoising (RED), which leverages existing denoisers for regularizing inverse problems. Our work shows how the two (DeepRED) can be merged to a highly effective recovery process while avoiding the need to differentiate the chosen denoiser, and leading to very effective results, demonstrated for several tested inverse problems.

🎥 Python Neural Networks - Tensorflow 2.0 Tutorial - Creating a Model
👁 1 раз ⏳ 1068 сек.
This python neural network tutorial covers how to create a model using tensorflow 2.0 and keras. We will then train the model on our dataset and have it predict the classification of our test data.

Text-Based Tutorial: Coming soon..

Tensorflow Website: https://www.tensorflow.org/alpha/tutorials/keras/basic_classification

Want a sneak peak into my life? Follow my Instagram @tech_with_tim where I'm going to be filming a video each morning sharing my goals for the day and what I have planned:
https://www.in

​Light on Math ML: Intuitive Guide to Understanding GloVe Embeddings

🔗 Light on Math ML: Intuitive Guide to Understanding GloVe Embeddings
Understanding theory behind GloVe and Keras implementation!

​Markov Chains and HMMs

🔗 Markov Chains and HMMs
In this article, we’ll focus on Markov Models, where an when they should be used, and Hidden Markov Models. This article will focus on the…

🎥 Machine Learning: Dimensionality Reduction With Principal Component Analysis
👁 2 раз ⏳ 848 сек.
In this video, we cover how to reduce the number of features using principal component analysis.

Video explaining PCA in depth:
https://www.youtube.com/watch?v=g-Hb26agBFg&t=1421s

CONNECT
Site: https://coryjmaklin.com/
Medium: https://medium.com/@corymaklin
GitHub: https://github.com/corymaklin
Twitter: https://twitter.com/CoryMaklin
Linkedin: https://www.linkedin.com/in/cory-maklin-a51732b7/
Facebook: https://www.facebook.com/cory.maklin
Patreon: https://www.patreon.com/corymaklin

🎥 How to Code Deep Q Learning in Tensorflow (Tutorial)
👁 1 раз ⏳ 2604 сек.
Deep Q Learning w/ Pytorch: https://youtu.be/RfNxXlO6BiA
Where to find data for Deep Learning: https://youtu.be/9oW3WfKk6d4

#Tensorflow #DeepQLearning #OpenAIGym

In today's tutorial we are going to code a Deep Q Network, in the Tensorflow framework, to play the game Breakout, from the OpenAI Gym's Atari library.

We'll split our code into 2 classes: One to handle the neural networks for deep Q learning, and another to handle the agent's functionality, like memory and learning.

My model is still training

🎥 Machine Learning with Scikit-Learn Python | K-fold Cross-validation
👁 1 раз ⏳ 705 сек.
#normalizednerd #python #scikitlearn

In this video, I've explained the concept of k-fold cross-validation and how to implement it in the popular library known as sci-kit learn. Stay tuned more sci-kit learn videos are coming!

Previous video on confusion matrix -
https://www.youtube.com/watch?v=Dr7lbdgzpWM

For more videos please subscribe -
http://bit.ly/normalizedNERD

Playlist Learn Scikit Learn -
https://www.youtube.com/watch?v=mmnLkKYvGG8&list=PLM8wYQRetTxDHDWU-YBPfKXV3G0TKXvpy

Data source -
https:

​Python’s One Liner graph creation library with animations Hans Rosling Style

https://towardsdatascience.com/pythons-one-liner-graph-creation-library-with-animations-hans-rosling-style-f2cb50490396?source=collection_home---4------2---------------------

🔗 Python’s One Liner graph creation library with animations Hans Rosling Style
Animation, One Line graphs. It has it all

​A Data Science Workflow Canvas to Kickstart Your Projects

🔗 A Data Science Workflow Canvas to Kickstart Your Projects
Use this guide to help you complete your data science projects.

​Free online r course
fasteR: Fast Lane to Learning R!
https://github.com/matloff/fasteR

🔗 matloff/fasteR
Fast Lane to Learning R! Contribute to matloff/fasteR development by creating an account on GitHub.

​https://sites.google.com/view/icml-reproducibility-workshop

🔗 learningtobereproducible

​Visualizing Loss Landscape of Deep Neural Networks…..but can we Trust them?

🔗 Visualizing Loss Landscape of Deep Neural Networks…..but can we Trust them?
Can we trust the visualization of loss landscape of deep neural networks?

🎥 Segmentation of Brain Tumors from MRI using Deep Learning
👁 1 раз ⏳ 2331 сек.
Segmentation of Brain Tumors from MRI using Deep Learning

Наш телеграм канал - tglink.me/ai_machinelearning_big_data
https://www.youtube.com/watch?v=fcD6YeEYKNg

🎥 Deep Learning With Python | Deep Learning And Neural Networks | Deep Learning Tutorial | Simplilearn
👁 1 раз ⏳ 3055 сек.
This video on Deep Learning with Python will help you understand what is deep learning, applications of deep learning, what is a neural network, biological versus artificial neural networks, introduction to TensorFlow, activation function, cost function, how neural networks work, and what gradient descent is. Deep learning is a technology that is used to achieve machine learning through neural networks. We will also look into how neural networks can help achieve the capability of a machine to mimic human be

​Comprehensive Introduction to Turing Learning and GANs: Part 1

🔗 Comprehensive Introduction to Turing Learning and GANs: Part 1
Want to turn horses into zebras? Make DIY anime characters or celebrities? Generative adversarial networks (GANs) are your new best friend.

🎥 Feature Ranking in Keras and Scikit-Learn: Perturbation Ranking
👁 1 раз ⏳ 1035 сек.
Perturbation Ranking will tell which imports are the most important for any machine learning model, such as a deep neural network. The provided code work with TensorFlow and Keras. Because Perturbation ranking uses no internal model information (only results from generated inputs), it can be used with any classification or regression model.

Code for this video: https://github.com/drcannady/pub/tree/master/ijcnn-2017

Follow Me/Subscribe:
https://www.youtube.com/user/HeatonResearch
https://github.com/jeff

🎥 TensorFlow In 10 Minutes | TensorFlow Tutorial For Beginners | TensorFlow Training | Edureka
👁 1 раз ⏳ 541 сек.
*** AI and Deep-Learning with TensorFlow - https://www.edureka.co/ai-deep-learning-with-tensorflow ***
This video provides you with a basic introduction to TensorFlow: The amazing deep learning framework by Google.


*** Complete Tensorflow Playlist: https://www.youtube.com/playlist?list=PL9ooVrP1hQOFJ8UZl86fYfmB1_P5yGzBT ***

Join our Meetup group and never miss any free live webinar: http://bit.ly/2DQO5PL

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