• Numpy was first released in 1995 Exploding head
• For context PyTorch was released in 2016, TensorFlow in 2015 and Sklearn in 2007.

1. Google books and online course

2. Laurence Moroney - Machine Learning Foundations is a free training course where you’ll learn the fundamentals of building machine learned models using TensorFlow

3. Introduction in Colab format

4. This roadmap aims to give a complete picture of the modern data engineering landscape and serve as a study guide for aspiring data engineers.

5. Most influence papers

6. Top 9 Free Beginner Tutorials for Machine Learning (ML)

7. 6-machine-learning-algorithms-anyone-learning-data-science-should-know

8. Fast AI Course

9. top 8 algorithms machine learning

10. Introduction to Machine Learning
11. Neural Networks

Python for Data Analysis

List of Data Science Cheatsheets to rule the world.

Dicas de Programação Python, Pandas, Matplotlib, NumPy

Scientific Computing in Python: Introduction to NumPy and Matplotlib

7 Advanced Python Features You Should Know About

DataCamp: online interactive Python Tutorials for Data Science

Introdução a Python no Colab (Português)

Udacity - 5 weeks - free course

140 Python Projects with Source Code

130 Python Projects with Source Code

60 Python Projects with Source Code

190 Python Projects with Source Code

1. Machine Learning
2. Unsupervised Learning
3. Deep Learning and Neural Networks
4. Pandas and ML - Course Youtube plus Lectures
5. End-to-End Machine Learning Library
6. Dicas de Pandas

1. A friendly introduction to Convolutional Neural Networks and Image Recognition
2. A friendly introduction to Recurrent Neural Networks

1. How Convolutional Neural Networks Work (CNNs Explained & Visualized) a video on visualizing convolutional networks, would mean a lot if you could let me know what you think about it....
2. Neural Network that Changes Everything - Computerphile
3. Deep Learning - Computerphile
4. Convolutional Neural Networks (CNNs) explained
5. An Intuitive Explanation of Convolutional Neural Networks
6. Intuitively Understanding Convolutions for Deep Learning
7. Gentle Dive into Math Behind Convolutional Neural Networks
8. Simple 4 min - How Convolutional Neural Networks Work | CNN's #1

1. Roadmap Machine Learning - Daniel (2h45min) - Interactive map, github, slides Get starting with TensorFlow

2. Timeline for Data Science Competence

3. Machine Learning Algorithms Full Course | Machine Learning Algorithms Explained | Simplilearn

4. Deep Learning's Most Important Ideas - A Brief Historical Review

5. Deep Learning: A Crash Course 3h33 - ACMSIGGRAPH

6. Join 20K+ developers in learning how to responsibly deliver value with applied ML.

7. Roots of Data Science

8. Introduction to Machine Learning

9. #51 Francois Chollet - Intelligence and Generalisation

10. 01 – History and resources 2021 - Lecun

11. The future of deep learning, according to its pioneers

12. ML articles

1. freeCodeCamp.org Keras with TensorFlow Course - Python Deep Learning and Neural Networks for Beginners Tutorial 2:47:55

2. CS231n Computer Vision Stanford - Labs Solved 2017/18

3. Five years of TF

4. Machine Learning from Scratch Youtube playlist by Python Engineer: kmeans, knn, random forest, ....

5. New TensorFlow Course 2021 - Daniel Bourke- 1h and Learn TensorFlow and Deep Learning fundamentals with Python (code-first introduction) Part 2 - 4h

6. Auto Keras Tutorial

7. Keras resource examples

8. his is the repo supporting Intro to Data Science (DS-GA-1001) for the NYU Center for Data Science. For homework assignments, quizzes, exams and course grades, please log in to NYU Classes.

9. Welcome to the Zero to Mastery TensorFlow for Deep Learning Book

Deep Learning 2024 = Bishop

Understanding Deep Learning by Simon J.D. Princ To be published by MIT Press Dec 5th 2023.

Brief Deep Learning + complete course

Understanding Deep Learning - by Simon J.D. Prince - To be published by MIT Press.

Github, Author Aurélien Géron,Released September 2019 - Book - Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

Python Data Science Handbook by Jake VanderPlas

Deep Learning - Ian Goodfellow and Yoshua Bengio and Aaron Courville

Dive into Deep Learning, Interactive deep learning book with code, math, and discussions, Implemented with NumPy/MXNet, PyTorch, and TensorFlow - Github

The Hundred-Page Machine Learning Book

Machine Learning Systems Design Chip Huyen

Deep Learning book ONLINE in portuguese

• Deep learning CMU 2022 playlist

• Artificial Intelligence for Beginners - A Curriculum

• Machine Learning for Beginners - A Curriculum

• Machine Learning Main Algorithms

• Zero to Mastery Machine Learning- Daniel Bourke

• Machine Learning Engineering in 10 Weeks curriculum v1 and Jason Benn - learn machine learning

• Cs231n - Stanford

• Make your own Machine Learning and Deep Learning degree, September 21, 2020 - Aurélien Peden

• Collection of the resources & project from Niklas Muennighoff

• Udacity - Introduction to Machine Learning Course

STAT 157 at UC Berkeley

• Ciência de Dados e Aprendizado de Máquina

• Deep Computer Vision + schedule

• Introduction to Machine Learning

• Deep Learning Course François Fleuret's deep-learning courses 14x050 of the University of Geneva, Switzerland

0. CS229 Andrew NG - Machine Learning - plus python version

1. Machine Learning & Data Science - Kaggle

2. Comparing images for similarity using siamese networks, Keras, and TensorFlow

3. Top list papers, libraries...of 2020

4. Group Equivariant Convolutional Networks

1. For instance, to train the large-scale language model GPT-3, OpenAI in partnership with Microsoft may have consumed compute resources worth $5 million to $10 million, according to one analysis. No U.S. university has ready access to this scale of computation.

• Fei-Fei Li Stanford Professor: The year 2020 brought renewed federal support for universities and colleges. But more needs to be done. At the Stanford Institute for Human-Centered Artificial Intelligence (HAI), which I co-direct with John Etchemendy, we have proposed a National Research Cloud. This initiative would devote $1 billion to $10 billion per year over 10 years to recharge the partnership between academia, government, and industry. It would give U.S. academic researchers the compute and data they need to stay on the cutting edge, which in turn would attract and retain new crops of faculty and students, potentially reversing the current exodus of researchers from academia to industry.

2. Deep Learning's Diminishing Returns: The Cost of Improvement is Becoming Unsustainable Neil C. Thompson; Kristjan Greenewald; Keeheon Lee; Gabriel F. Manso

3. Thinking Outside the Die: Architecting the ML Accelerator of the Future

4. Forgets GPT~3. Real #AI news of 2020 is that @ylecun & Y Bengio have converged w @yudapearl, @emilybender, me, etc in realizing large language models are not enough - to Next Decade in AI = deeper understanding & richer cognitive models, discussed here

5. Lecun Videos - Play List of Keynotes

Mini-curso Ciência de Dados - SBRC2019 - Portuguese

• Neural network built from scratch using Python and numpy

• Machine learning algorithms from scratch - Knn and Learn Regression

• We Don’t Need Data Scientists, We Need Data Engineers

• TensorFlow Hub has 1000's of models for you to take a look at! You can start here

• "Ok, can you guide me through a full example?" Yes, of course

• Where can I find more sample code?" The official repo has great samples

• "Where can I find samples from the community?" This repo is awesome and has a lot of great resources

• E-book Redes neurais artificiais: teoria e aplicações