LLMs Explained - Aggregate Intellect - AI.SCIENCE

LLMs Explained - Aggregate Intellect - AI.SCIENCE

Views:
841,275
Subscribers:
22,300
Videos:
702
Duration:
18:20:50:03
Canada
Canada

LLMs Explained - Aggregate Intellect - AI.SCIENCE is a Canadian YouTube content creator with roughly 22.3 thousand subscribers. He published 702 videos which altogether total roughly 841.28 thousand views.

Created on ● Channel Link: https://www.youtube.com/channel/UCfk3pS8cCPxOgoleriIufyg



Top 100 Most Liked Videos by LLMs Explained - Aggregate Intellect - AI.SCIENCE


Video TitleRatingCategoryGame
1.[BERT] Pretranied Deep Bidirectional Transformers for Language Understanding (algorithm) | TDLS1,000
2.[Transformer] Attention Is All You Need | AISC Foundational687
3.[Variational Autoencoder] Auto-Encoding Variational Bayes | AISC Foundational458
4.[Original ResNet paper] Deep Residual Learning for Image Recognition | AISC334
5.Classification of sentiment reviews using n-gram machine learning approach271Review
6.TGN: Temporal Graph Networks for Deep Learning on Dynamic Graphs [Paper Explained by the Author]245
7.Code Review: Transformer - Attention Is All You Need | AISC244Review
8.Principles of Riemannian Geometry in Neural Networks | TDLS229
9.(Original Paper) Latent Dirichlet Allocation (algorithm) | AISC Foundational221
10.Combining Satellite Imagery and machine learning to predict poverty213
11.[ELMo] Deep Contextualized Word Representations | AISC213
12.Introduction to the Conditional GAN - A General Framework for Pixel2Pixel Translation198
13.[GAT] Graph Attention Networks | AISC Foundational181
14.[StyleGAN] A Style-Based Generator Architecture for GANs, part 1 (algorithm review) | TDLS180Review
15.Deep InfoMax: Learning deep representations by mutual information estimation and maximization | AISC146Guide
16.AlphaStar explained: Grandmaster level in StarCraft II with multi-agent RL145Let's PlayStarCraft II
17.A Literature Review on Graph Neural Networks139Review
18.Neural Ordinary Differential Equations - part 1 (algorithm review) | AISC138Review
19.Mathematics of Deep Learning Overview | AISC Lunch & Learn130
20.Learning Mesh-Based Simulation with Graph Networks - Tobias Pfaff (DeepMind)127
21.Convolutional Neural Networks for processing EEG signals126
22.Battery Modelling using Data-Driven Machine Learning | AISC122
23.Can deep learning AI help with detecting COVID-19/coronavirus?122
24.Lagrangian Neural Networks | AISC117
25.Leaf Doctor: Plant Disease Detection Using Image Classification | Deep Learning Workshop Capstone116
26.Tobias Pfaff (DeepMind): Learning to Simulate Complex Physics with Graph Networks112
27.Steve Brunton: Machine Learning for Fluid Dynamics111
28.All-optical machine learning using diffractive deep neural networks | TDLS109
29.Connectionist Temporal Classification, Labelling Unsegmented Sequence Data with RNN | TDLS105
30.Deep Neural Networks for YouTube Recommendation | AISC Foundational102
31.[AlphaGo Zero] Mastering the game of Go without human knowledge | TDLS98Let's Play
32.Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond | TDLS97
33.PGGAN | Progressive Growing of GANs for Improved Quality, Stability, and Variation (part 1) | AISC95
34.[DDQN] Deep Reinforcement Learning with Double Q-learning | TDLS Foundational88
35.[GQN] Neural Scene Representation and Rendering | AISC86
36.Revolutionary Deep Learning Method to Denoise EEG Brainwaves84
37.Explainable AI for Time Series - Literature Review | AISC84Review
38.Introduction to NVIDIA NeMo - A Toolkit for Conversational AI | AISC82
39.Transformer XL | AISC Trending Papers80
40.Explainable AI with Layer-wise Relevance Propagation (LRP)79
41.[OpenAI GPT2] Language Models are Unsupervised Multitask Learners | TDLS Trending Paper78
42.Neural Image Caption Generation with Visual Attention (algorithm) | AISC76
43.AlphaFold 2, Is Protein Folding Solved? | AISC75Let's Play
44.Plug and Play Language Models: A Simple Approach to Controlled Text Generation | AISC74
45.Similarity of neural network representations revisited73
46.Reinforcement Learning in Economics and Finance | AISC73
47.Junction Tree Variational Autoencoder for Molecular Graph Generation | TDLS72
48.Graph Normalizing Flows72
49.[Original attention] Neural Machine Translation by Jointly Learning to Align and Translate | AISC70
50.Deep Q-Learning paper explained: Human-level control through deep reinforcement learning (algorithm)69
51.Top-K Off-Policy Correction for a REINFORCE Recommender System | AISC62
52.Topological Deep Learning61Vlog
53.[SAGAN] Self-Attention Generative Adversarial Networks | TDLS60
54.[StackGAN++] Realistic Image Synthesis with Stacked Generative Adversarial Networks | AISC56
55.Logeo: Automatically Transform 2D Designs to 3D55Vlog
56.Ernie 2.0: A Continual Pre-Training Framework for Language Understanding | AISC53
57.An overview of task-oriented dialog systems | AISC53Vlog
58.TMLS2017: Transitioning to Data Science, Panel Discussion52Discussion
59.Machine Learning in Environmental Science and Prediction: An Overview | AISC51
60.Supercharging AI with high performance distributed computing51
61.A Literature Review on Machine Learning in Materials Science | AISC50Review
62.Single Headed Attention RNN: Stop Thinking With Your Head | AISC50
63.[hgraph2graph] Hierarchical Generation of Molecular Graphs using Structural Motifs | AISC Spotlight49
64.[Original Style Transfer] A Neural Algorithm of Artistic Style | TDLS Foundational49
65.Applications of Blockchain to IoT Security | AISC48
66.Overview of Machine Learning for Knowledge Graphs | AISC48
67.Principal Neighbourhood Aggregation for Graph Nets | AISC47
68.Learning the Graphical Structure of Electronic Health Records with Graph Convolutional Transformer47
69.Automated Vulnerability Detection in Source Code Using Deep Learning (algorithm) | AISC46
70.Deep Learning for Symbolic Mathematics | AISC45
71.Why do large batch sized trainings perform poorly in SGD? - Generalization Gap Explained | AISC45
72.The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words & Sentences From Natural Supervision45
73.[Gated-SCNN] Gated Shape CNNs for Semantic Segmentation44
74.Genomics with Deep Learning: A Concise Overview | AISC44
75.Lookahead Optimizer: k steps forward, 1 step back44
76.A Framework for Developing Deep Learning Classification Models44
77.BERT & NLP Explained44Let's Play
78.Paper Explained : PEGASUS, a SOTA abstractive summarization model by Google | AISC44
79.Interpretable Neural Networks for Panel Data Analysis in Economics | AISC43
80.Deep learning enables rapid identification of potent DDR1 kinase inhibitors | AISC43
81.Da Xu (Walmart Labs): Inductive Representation Learning on Temporal Graphs | AISC42
82.XLNet: Generalized Autoregressive Pretraining for Language Understanding | AISC41
83.Neural Ordinary Differential Equations - part 2 (results & discussion) | AISC39Discussion
84.TF-Encrypted: Private machine learning in tensorflow with secure computing | AISC Lunch & Learn39
85.New Directions in Computer Vision38
86.BERTology Meets Biology: Interpreting Attention in Protein Language Models | AISC38Vlog
87.State of Natural Language Processing in 2019 | AISC38
88.Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods | AISC38
89.Understanding the Origins of Bias in Word Embeddings37
90.GrowNet: Gradient Boosting Neural Networks | AISC36
91.[RoBERT & ToBERT] Hierarchical Transformers for Long Document Classification | AISC36
92.Beyond Accuracy: Behavioral Testing of NLP Models with CheckList | AISC36Let's Play
93.Unsupervised Data Augmentation | AISC36
94.Survival regression with AFT model in XGBoost | AISC34
95.'Less Than One'-Shot Learning (author speaking)34
96.Explainable AI to Automate Loan Underwriting Decisions | AISC34
97.Overview of Unsupervised & Semi-supervised learning | AISC34
98.Visualizing Data using t-SNE (algorithm) | AISC Foundational34
99.Few-Shot Learning Through an Information Retrieval Lens | TDLS34Guide
100.Tensor Field Networks | AISC34