#course #video #ml
This series is all about neural network programming and PyTorch! We will learn how to build neural networks with PyTorch, and we’ll find that we are super close to programming neural networks from scratch, as the experience of using PyTorch is as close as it gets to the real thing! After programming neural networks with PyTorch, it’s pretty easy to see how the process works from scratch. This will lead us to a much deeper understanding of neural networks and deep learning.

@machinelearning_tuts

https://www.youtube.com/playlist?list=PLZbbT5o_s2xrfNyHZsM6ufI0iZENK9xgG

📚Roadmaps and Important links.📚
#roadmap
#ml

@machinelearning_tuts

To learn languages based on projects.
Github: https://github.com/tuvtran/project-based-learning

Python Machine Learning Book
Github: https://github.com/rasbt/python-machine-learning-book

Coding Practice and Algorithms
Github: https://github.com/jwasham/coding-interview-university

What every programmer should know
Github: https://github.com/mtdvio/every-programmer-should-know

Awesome public datasets
Github: https://github.com/awesomedata/awesome-public-datasets

Awesome Machine Learning
Github: https://github.com/josephmisiti/awesome-machine-learning

Awesome Deep Vision
Github: https://github.com/kjw0612/awesome-deep-vision

Awesome tensorflow
Github: https://github.com/jtoy/awesome-tensorflow

Awesome Project Ideas
Github: https://github.com/NirantK/awesome-project-ideas

Awesome NLP
Github: https://github.com/keon/awesome-nlp

Best of Jupyter
Github: https://github.com/NirantK/best-of-jupyter

Deep Learning paper reading roadmap
Github: https://github.com/floodsung/Deep-Learning-Papers-Reading-Roadmap

Paper to Code
Github: https://github.com/zziz/pwc

Reinforcement Learning
Github: https://github.com/dennybritz/reinforcement-learning

Google dataset search
Link: https://t.co/iXFwNCDaUN

Best Practices for ML Engineering
Link: http://martin.zinkevich.org/rules_of_ml/rules_of_ml.pdf

List of Tutorials - Medium Article
Link: https://medium.com/machine-learning-in-practice/over-200-of-the-best-machine-learning-nlp-and-python-tutorials-2018-edition-dd8cf53cb7dc

Awesome list of people and blogs to follow to keep yourself updated in the field
Link: https://medium.com/@alexrachnog/ultimate-following-list-to-keep-updated-in-artificial-intelligence-32776ffcd079

Google's guide to Machine Learning
Link: https://techdevguide.withgoogle.com/paths/machine-learning/

Introduction to Machine Learning for Coders!

#ml
#course
#jeremy_howard
#video


New machine learning course by Jeremy Howard.
These videos was made in San Francisco University.

Headlines:
1—Introduction to Random Forests
2—Random Forest Deep Dive
3—Performance, Validation and Model Interpretation
4—Feature Importance, Tree Interpreter
5—Extrapolation and RF from Scratch
6—Data Products and Live Coding
7—RF from Scratch and Gradient Descent
8—Gradient Descent and Logistic Regression
9—Regularization, Learning Rates and NLP
10— More NLP and Columnar Data
11—Embeddings
12— Complete Rossmann, Ethical Issues

@machinelearning_tuts

Course URL:
http://course.fast.ai/ml

Read more:
http://www.fast.ai/2018/09/26/ml-launch/

#ml
Technology is becoming more sophisticated than ever these days, particularly when it comes to artificial intelligence (AI). The most advanced systems are now able to do things that were once only possible for humans to achieve, and they are helping organizations make better business decisions than ever before...

@machinelearning_tuts

Read More:
https://www.linkedin.com/pulse/levels-machine-learning-e-commerce-product-search-vanessa-meyer/

You're on a journey to learn Data Science, Randy Lao is here to help you along the way!
watch free courses, download free books and learn more about machine learning every day...

#ml
#course
#resource

@machinelearning_tuts

http://www.claoudml.co/

You're on a journey to learn Data Science, Randy Lao is here to help you along the way!
watch free courses, download free books and learn more about machine learning every day...

#ml
#course
#resource

@machinelearning_tuts

http://www.claoudml.co/

Nice article by Dat Tran about some mathematicians trying to make sense of neural networks. Some of the findings are quite obvious to machine learning practitioners/researchers like deeper network with many layers and fewer neurons aka ResNet are better than shallow networks with few layers but many neurons per layer. It's still interesting though to see that there's an effort in trying to build a "general theory" of neural networks which one usually obtains from experiences and a lot of trial and error. Maybe this will help in the future to do less trial and error.

Dat Tran (https://www.linkedin.com/in/dat-tran-a1602320/)

#deeplearning
#machinelearning
#ml
#article

@machinelearning_tuts

image

https://www.quantamagazine.org/foundations-built-for-a-general-theory-of-neural-networks-20190131/

📕 Building Machine Learning Powered Applications

Going from Idea to Product Emmanuel Ameisen

📌@cedeeplearning

#book #ML #deeplearning #free #machinelearning