Deep Learning News #2, Feb 6 2021

Sebastian Raschka · Intermediate ·📐 ML Fundamentals ·5y ago

Skills: Reading ML Papers70%Research Methods60%

Sebastian's books: https://sebastianraschka.com/books/ Link to resources mentioned in the video: CMU Researchers Explore ‘Crazy Idea’ of Automating AI Paper Reviews: https://syncedreview.com/2021/02/04/cmu-researchers-explore-crazy-idea-of-automating-ai-paper-reviews/ Code: https://github.com/neulab/ReviewAdvisor Paper: https://arxiv.org/pdf/2102.00176.pdf --------------------------------------------------------------- DALL-E: a version of a 12-billion param GPT-3 https://www.wired.com/story/ai-go-art-steering-self-driving-car/ Original blog article (Jan 5): https://openai.com/blog/dall-e/ --------------------------------------------------------------- CLIP: Connecting Text and Images https://openai.com/blog/clip/ Paper: https://cdn.openai.com/papers/Learning_Transferable_Visual_Models_From_Natural_Language_Supervision.pdf Code: https://github.com/openai/CLIP --------------------------------------------------------------- GPT-Neo: https://www.eleuther.ai/projects/gpt-neo/ --------------------------------------------------------------- Where Programming, Ops, AI, and the Cloud are Headed in 2021 https://www.oreilly.com/radar/where-programming-ops-ai-and-the-cloud-are-headed-in-2021/ --------------------------------------------------------------- Teaching AI to manipulate objects using visual demos https://ai.facebook.com/blog/teaching-ai-to-manipulate-objects-using-visual-demos/ Code: https://github.com/facebookresearch/LearningToLearn --------------------------------------------------------------- Initialization is critical for preserving global data structure in both t-SNE and UMAP Article: https://www.nature.com/articles/s41587-020-00809-z https://github.com/scikit-learn/scikit-learn/issues/18018

Watch on YouTube ↗ (saves to browser)

Sign in to unlock AI tutor explanation · ⚡30

Playlist

Uploads from Sebastian Raschka · Sebastian Raschka · 0 of 60

← Previous Next →

Intro to Deep Learning -- L06.5 Cloud Computing [Stat453, SS20]

Intro to Deep Learning -- L06.5 Cloud Computing [Stat453, SS20]

Sebastian Raschka

Intro to Deep Learning -- L09 Regularization [Stat453, SS20]

Intro to Deep Learning -- L09 Regularization [Stat453, SS20]

Sebastian Raschka

Intro to Deep Learning -- L10 Input and Weight Normalization Part 1/2 [Stat453, SS20]

Intro to Deep Learning -- L10 Input and Weight Normalization Part 1/2 [Stat453, SS20]

Sebastian Raschka

Intro to Deep Learning -- L10 Input and Weight Normalization Part 2/2 [Stat453, SS20]

Intro to Deep Learning -- L10 Input and Weight Normalization Part 2/2 [Stat453, SS20]

Sebastian Raschka

Intro to Deep Learning -- L11 Common Optimization Algorithms [Stat453, SS20]

Intro to Deep Learning -- L11 Common Optimization Algorithms [Stat453, SS20]

Sebastian Raschka

Intro to Deep Learning -- L12 Intro to Convolutional Neural Networks (Part 1) [Stat453, SS20]

Intro to Deep Learning -- L12 Intro to Convolutional Neural Networks (Part 1) [Stat453, SS20]

Sebastian Raschka

Intro to Deep Learning -- L13 Intro to Convolutional Neural Networks (Part 2) 1/2 [Stat453, SS20]

Intro to Deep Learning -- L13 Intro to Convolutional Neural Networks (Part 2) 1/2 [Stat453, SS20]

Sebastian Raschka

Intro to Deep Learning -- L13 Intro to Convolutional Neural Networks (Part 2) 2/2 [Stat453, SS20]

Intro to Deep Learning -- L13 Intro to Convolutional Neural Networks (Part 2) 2/2 [Stat453, SS20]

Sebastian Raschka

Intro to Deep Learning -- L14 Intro to Recurrent Neural Networks [Stat453, SS20]

Intro to Deep Learning -- L14 Intro to Recurrent Neural Networks [Stat453, SS20]

Sebastian Raschka

Intro to Deep Learning -- L15 Autoencoders [Stat453, SS20]

Intro to Deep Learning -- L15 Autoencoders [Stat453, SS20]

Sebastian Raschka

Intro to Deep Learning -- L16 Generative Adversarial Networks [Stat453, SS20]

Intro to Deep Learning -- L16 Generative Adversarial Networks [Stat453, SS20]

Sebastian Raschka

Intro to Deep Learning -- Student Presentations, Day 1 [Stat453, SS20]

Intro to Deep Learning -- Student Presentations, Day 1 [Stat453, SS20]

Sebastian Raschka

1.2 What is Machine Learning (L01: What is Machine Learning)

1.2 What is Machine Learning (L01: What is Machine Learning)

Sebastian Raschka

1.3 Categories of Machine Learning (L01: What is Machine Learning)

1.3 Categories of Machine Learning (L01: What is Machine Learning)

Sebastian Raschka

1.4 Notation (L01: What is Machine Learning)

1.4 Notation (L01: What is Machine Learning)

Sebastian Raschka

1.1 Course overview (L01: What is Machine Learning)

1.1 Course overview (L01: What is Machine Learning)

Sebastian Raschka

1.5 ML application (L01: What is Machine Learning)

1.5 ML application (L01: What is Machine Learning)

Sebastian Raschka

1.6 ML motivation (L01: What is Machine Learning)

1.6 ML motivation (L01: What is Machine Learning)

Sebastian Raschka

2.1 Introduction to NN (L02: Nearest Neighbor Methods)

2.1 Introduction to NN (L02: Nearest Neighbor Methods)

Sebastian Raschka

2.2 Nearest neighbor decision boundary (L02: Nearest Neighbor Methods)

2.2 Nearest neighbor decision boundary (L02: Nearest Neighbor Methods)

Sebastian Raschka

2.3 K-nearest neighbors (L02: Nearest Neighbor Methods)

2.3 K-nearest neighbors (L02: Nearest Neighbor Methods)

Sebastian Raschka

2.4 Big O of K-nearest neighbors (L02: Nearest Neighbor Methods)

2.4 Big O of K-nearest neighbors (L02: Nearest Neighbor Methods)

Sebastian Raschka

2.5 Improving k-nearest neighbors (L02: Nearest Neighbor Methods)

2.5 Improving k-nearest neighbors (L02: Nearest Neighbor Methods)

Sebastian Raschka

2.6 K-nearest neighbors in Python (L02: Nearest Neighbor Methods)

2.6 K-nearest neighbors in Python (L02: Nearest Neighbor Methods)

Sebastian Raschka

3.1 (Optional) Python overview

3.1 (Optional) Python overview

Sebastian Raschka

3.2 (Optional) Python setup

3.2 (Optional) Python setup

Sebastian Raschka

3.3 (Optional) Running Python code

3.3 (Optional) Running Python code

Sebastian Raschka

4.1 Intro to NumPy (L04: Scientific Computing in Python)

4.1 Intro to NumPy (L04: Scientific Computing in Python)

Sebastian Raschka

4.2 NumPy Array Construction and Indexing (L04: Scientific Computing in Python)

4.2 NumPy Array Construction and Indexing (L04: Scientific Computing in Python)

Sebastian Raschka

4.4 NumPy Broadcasting (L04: Scientific Computing in Python)

4.4 NumPy Broadcasting (L04: Scientific Computing in Python)

Sebastian Raschka

4.5 NumPy Advanced Indexing -- Memory Views and Copies (L04: Scientific Computing in Python)

4.5 NumPy Advanced Indexing -- Memory Views and Copies (L04: Scientific Computing in Python)

Sebastian Raschka

4.3 NumPy Array Math and Universal Functions (L04: Scientific Computing in Python)

4.3 NumPy Array Math and Universal Functions (L04: Scientific Computing in Python)

Sebastian Raschka

4.7 Reshaping NumPy Arrays (L04: Scientific Computing in Python)

4.7 Reshaping NumPy Arrays (L04: Scientific Computing in Python)

Sebastian Raschka

4.6 NumPy Random Number Generators (L04: Scientific Computing in Python)

4.6 NumPy Random Number Generators (L04: Scientific Computing in Python)

Sebastian Raschka

4.8 NumPy Comparison Operators and Masks (L04: Scientific Computing in Python)

4.8 NumPy Comparison Operators and Masks (L04: Scientific Computing in Python)

Sebastian Raschka

4.9 NumPy Linear Algebra Basics (L04: Scientific Computing in Python)

4.9 NumPy Linear Algebra Basics (L04: Scientific Computing in Python)

Sebastian Raschka

4.10 Matplotlib (L04: Scientific Computing in Python)

4.10 Matplotlib (L04: Scientific Computing in Python)

Sebastian Raschka

5.1 Reading a Dataset from a Tabular Text File (L05: Machine Learning with Scikit-Learn)

5.1 Reading a Dataset from a Tabular Text File (L05: Machine Learning with Scikit-Learn)

Sebastian Raschka

5.2 Basic data handling (L05: Machine Learning with Scikit-Learn)

5.2 Basic data handling (L05: Machine Learning with Scikit-Learn)

Sebastian Raschka

5.3 Object Oriented Programming & Python Classes (L05: Machine Learning with Scikit-Learn)

5.3 Object Oriented Programming & Python Classes (L05: Machine Learning with Scikit-Learn)

Sebastian Raschka

5.4 Intro to Scikit-learn (L05: Machine Learning with Scikit-Learn)

5.4 Intro to Scikit-learn (L05: Machine Learning with Scikit-Learn)

Sebastian Raschka

5.5 Scikit-learn Transformer API (L05: Machine Learning with Scikit-Learn)

5.5 Scikit-learn Transformer API (L05: Machine Learning with Scikit-Learn)

Sebastian Raschka

5.6 Scikit-learn Pipelines (L05: Machine Learning with Scikit-Learn)

5.6 Scikit-learn Pipelines (L05: Machine Learning with Scikit-Learn)

Sebastian Raschka

6.1 Intro to Decision Trees (L06: Decision Trees)

6.1 Intro to Decision Trees (L06: Decision Trees)

Sebastian Raschka

6.2 Recursive algorithms & Big-O (L06: Decision Trees)

6.2 Recursive algorithms & Big-O (L06: Decision Trees)

Sebastian Raschka

6.3 Types of decision trees (L06: Decision Trees)

6.3 Types of decision trees (L06: Decision Trees)

Sebastian Raschka

6.5 Gini & Entropy versus misclassification error (L06: Decision Trees)

6.5 Gini & Entropy versus misclassification error (L06: Decision Trees)

Sebastian Raschka

6.6 Improvements & dealing with overfitting (L06: Decision Trees)

6.6 Improvements & dealing with overfitting (L06: Decision Trees)

Sebastian Raschka

6.7 Code Example Implementing Decision Trees in Scikit-Learn (L06: Decision Trees)

6.7 Code Example Implementing Decision Trees in Scikit-Learn (L06: Decision Trees)

Sebastian Raschka

7.1 Intro to ensemble methods (L07: Ensemble Methods)

7.1 Intro to ensemble methods (L07: Ensemble Methods)

Sebastian Raschka

7.2 Majority Voting (L07: Ensemble Methods)

7.2 Majority Voting (L07: Ensemble Methods)

Sebastian Raschka

7.3 Bagging (L07: Ensemble Methods)

7.3 Bagging (L07: Ensemble Methods)

Sebastian Raschka

7.4 Boosting and AdaBoost (L07: Ensemble Methods)

7.4 Boosting and AdaBoost (L07: Ensemble Methods)

Sebastian Raschka

7.5 Gradient Boosting (L07: Ensemble Methods)

7.5 Gradient Boosting (L07: Ensemble Methods)

Sebastian Raschka

7.6 Random Forests (L07: Ensemble Methods)

7.6 Random Forests (L07: Ensemble Methods)

Sebastian Raschka

7.7 Stacking (L07: Ensemble Methods)

7.7 Stacking (L07: Ensemble Methods)

Sebastian Raschka

8.1 Intro to overfitting and underfitting (L08: Model Evaluation Part 1)

8.1 Intro to overfitting and underfitting (L08: Model Evaluation Part 1)

Sebastian Raschka

8.2 Intuition behind bias and variance (L08: Model Evaluation Part 1)

8.2 Intuition behind bias and variance (L08: Model Evaluation Part 1)

Sebastian Raschka

8.3 Bias-Variance Decomposition of the Squared Error (L08: Model Evaluation Part 1)

8.3 Bias-Variance Decomposition of the Squared Error (L08: Model Evaluation Part 1)

Sebastian Raschka

8.4 Bias and Variance vs Overfitting and Underfitting (L08: Model Evaluation Part 1)

8.4 Bias and Variance vs Overfitting and Underfitting (L08: Model Evaluation Part 1)

Sebastian Raschka

More on: Reading ML Papers

View skill →

Automatic Literature Review with GPT-3 - I embedded and indexed all of arXiv into a search engine!

Automatic Literature Review with GPT-3 - I embedded and indexed all of arXiv into a search engine!

Marcos Lopez Caniego - ESASky's JupyterLab widget| JupyterCon 2020

Marcos Lopez Caniego - ESASky's JupyterLab widget| JupyterCon 2020

Obsidian Zotero Integration Plugin | Streamline Your Research Paper Workflow 📝️

Obsidian Zotero Integration Plugin | Streamline Your Research Paper Workflow 📝️

This FULLY FREE Research Agent can BUILD Reports in Minutes!!!

This FULLY FREE Research Agent can BUILD Reports in Minutes!!!

Claude 3.7 Sonnet API | Build a Research Assistant

Claude 3.7 Sonnet API | Build a Research Assistant

I Built An Obsidian AI Research Assistant with Oz...

I Built An Obsidian AI Research Assistant with Oz...

Related AI Lessons

Cx Dev Log — 2026-05-07

Learn about JIT backend development with techniques like back-edge sealing and assert native lowering

Dev.to · COMMENTERTHE9

Andromeda Paradox through the lens of Ghidan Bloch Sphere

Explore the intersection of relativity, simultaneity, and quantum information through the Ghidan Bloch Sphere lens

Medium · Data Science

Why the Quantum Revolution Might Be a Civilizational Illusion

The quantum revolution's potential impact on civilization might be exaggerated, and its true effects need to be reevaluated

Medium · Deep Learning

A Practical Guide to scikit-learn: The Swiss Army Knife of Machine Learning in Python

Learn to use scikit-learn, a key Python library for machine learning, to simplify and improve your ML workflows

Medium · Machine Learning

AI Dev 26 x SF: Emma McGrattan: Engineering the Context Layer