Umar Jamil

Subscribers 84.6K

Videos 26

Views 2.6M

Channels Like Umar Jamil

Umar Jamil focuses on machine learning and deep learning concepts, offering tutorials and deep-dives such as attention mechanisms, multimodal models, reinforcement learning, interpretability, and PyTorch tutorials. Content appears to be long-form and technical, with occasional code explanations and math derivations, and the channel has been active since 2007 with an average of about 53.9K views per video.

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Similar Channels

We found 47 YouTube channels similar to Umar Jamil

IBM Technology YouTube channel thumbnail

#1

IBM Technology view on YouTube ▶

86% relevance

Subscribers 1.6M

Videos 1.5K

Views 108.6M

Appearances 20

SERP 100%

Similarity 76%

machine learning fundamentals multimodal models vision language model

Shares Umar Jamil’s focus on machine learning fundamentals and multimodal models, with high search overlap on terms like 'machine learning fundamentals' and 'multimodal models' (search 100%, content 76%), indicating a similar audience but slightly different delivery style.

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#2

Stanford Online

83% relevance

Subscribers 1.1M

Videos 2.9K

Views 77.1M

Appearances 34

SERP 93%

Similarity 76%

multimodal models interpretability in ML attention mechanisms tutorials

Appeals to the same ML audience with emphasis on multimodal models and interpretability in ML, reflected by strong search alignment (93%) and content similarity (76%), suggesting overlapping topics though often more formal academic framing.

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freeCodeCamp.org YouTube channel thumbnail

#3

freeCodeCamp.org view on YouTube ▶

67% relevance

Subscribers 11.5M

Videos 2.1K

Views 954.1M

Appearances 10

SERP 51%

Similarity 77%

machine learning fundamentals deep learning tutorials pytorch ML tutorials

Targets machine learning fundamentals and deep learning tutorials, sharing audience interest on core topics (search 51%, content 77%), but with broader coding and tutorial-focused style that diverges from Umar Jamil's niche framing.

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#4

3Blue1Brown view on YouTube ▶

62% relevance

Subscribers 8.2M

Videos 229

Views 727.0M

Appearances 10

SERP 40%

Similarity 77%

deep learning tutorials vision language model attention mechanisms tutorials

Converges on deep learning tutorials and attention mechanisms, evidenced by high content similarity (77%) and notable search overlap (40%), indicating similar topic interests but a distinct visual-mathematical teaching approach.

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StatQuest with Josh Starmer YouTube channel thumbnail

#5

StatQuest with Josh Starmer view on YouTube ▶

62% relevance

Subscribers 1.6M

Videos 293

Views 89.3M

Appearances 7

SERP 42%

Similarity 75%

machine learning fundamentals reinforcement learning fundamentals attention mechanisms tutorials

Aligns on machine learning fundamentals and attention mechanism tutorials, with moderate search overlap (42%) and strong content similarity (75%), signaling a shared audience but differing presentation.

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#6

Neural Breakdown with AVB

57% relevance

Subscribers 32.8K

Videos 67

Views 874.0K

Appearances 3

SERP 11%

Similarity 87%

multimodal models vision language model reinforcement learning fundamentals

Shares interest in multimodal models and vision-language models, plus reinforcement learning fundamentals (search 11%, content 87%), indicating a niche audience overlap with a distinct, high-detail content style.

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Content Landscape

Top competitors include IBM Technology (86% match) and Stanford Online (83% match), both aligning with Umar Jamil on machine learning fundamentals, multimodal models, and interpretability/attention topics. FreeCodeCamp.org (67% match) and 3Blue1Brown (62% match) also overlap on deep learning tutorials and vision-language or attention-focused content; StatQuest with Josh Starmer (62% match) shares emphasis on machine learning fundamentals and reinforcement learning. Umar Jamil has 84.6K subscribers, while IBM Technology and Stanford Online have substantially larger audiences (1.6M and 1.1M respectively), with freeCodeCamp.org at 11.5M and 3Blue1Brown at 8.2M subscribers, indicating Umar operates on a smaller scale but competes for the same search queries related to machine learning fundamentals, multimodal models, and attention mechanisms.

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Video Highlights

Didn't Make the Cut

41 additional channels that were close

Rank	Relevance	Channel	Similarity	Subscribers	SERP	Appearances
#7	56%	Infinite Codes ▶	75%	128K	27%	5
#8	56%	Jia-Bin Huang	82%	45.7K	17%	3
#9	56%	PyTorch	81%	67.4K	17%	5
#10	55%	Tales Of Tensors	84%	1.6K	12%	1
#11	55%	MIT OpenCourseWare ▶	77%	6.2M	22%	7
#12	55%	A Data Odyssey	76%	10K	22%	4
#13	55%	DeepBean	78%	12.8K	19%	3
#14	54%	Simplilearn ▶	72%	6.1M	27%	4
#15	54%	Rob Mulla	82%	220K	12%	3
#16	54%	CodeEmporium	78%	155K	17%	5
#17	53%	Yannic Kilcher ▶	80%	320K	12%	5
#18	53%	Welch Labs ▶	80%	860K	12%	3
#19	53%	MIT HAN Lab	80%	21.3K	12%	6
#20	52%	Julia Turc	79%	69.9K	13%	3
#21	52%	PyData	75%	171K	18%	6
#22	52%	Shaw Talebi	81%	91.2K	9%	4
#23	52%	TwinEd Productions	79%	2.4K	11%	2
#24	51%	AssemblyAI	73%	180K	18%	2
#25	51%	Super Data Science: ML & AI Podcast with Jon Krohn	77%	265K	12%	2
#26	51%	Efficient NLP	74%	19.9K	15%	5
#27	50%	Computerphile ▶	75%	2.6M	13%	3
#28	50%	codebasics	73%	1.5M	16%	3
#29	50%	Statistical Learning and Data Science	77%	3.2K	9%	1
#30	50%	Harry Connor AI	75%	13.3K	12%	1
#31	50%	ComputerVisionFoundation Videos	74%	45.1K	13%	3
#32	49%	The Linux Foundation	76%	209K	9%	1
#33	49%	Tübingen Machine Learning	74%	51K	11%	3
#34	49%	Fireship ▶	73%	4.1M	12%	3
#35	48%	Lex Fridman	73%	5.0M	11%	2
#36	48%	Google Cloud Tech	70%	1.4M	14%	4
#37	48%	Boris Meinardus	74%	96.9K	9%	1
#38	48%	MATLAB	73%	595K	10%	4
#39	47%	doggo dot jl	73%	11.6K	9%	1
#40	46%	Agility	71%	65.9K	9%	1
#41	46%	Anthropic	70%	448K	10%	2
#42	46%	Matt Williams	69%	94.5K	10%	3
#43	45%	Mısra Turp	69%	49.4K	9%	1
#44	45%	Brady Neal - Causal Inference	67%	17.1K	13%	3
#45	44%	CrashCourse ▶	67%	16.9M	9%	2
#46	43%	Lex Clips	62%	1.6M	14%	5
#47	42%	Federal Reserve Bank of St. Louis	63%	31.2K	9%	1

Search Queries Used

machine learning fundamentals deep learning tutorials multimodal models vision language model reinforcement learning fundamentals interpretability in ML attention mechanisms tutorials quantization in deep learning distributed training tutorials sparse attention models mixture of experts recurrent convolution networks math in ML DL model optimization pytorch ML tutorials ml model explainability robust ML methods neural network pruning self supervised learning transformer optimization time series ML GAN fundamentals probabilistic ML basics edge AI deployment robot perception models meta learning intro causal ML basics probabilistic programming diffusion models overview neural architecture search privacy preserving ML fed learning basics online learning algorithms multitask learning Bayesian neural networks

Frequently Asked Questions

Which YouTube channels are most similar to Umar Jamil?

Umar Jamil's biggest YouTube competitors are IBM Technology (86% match, 1.6M subscribers), Stanford Online (83% match, 1.1M subscribers), and freeCodeCamp.org (67% match, 11.5M subscribers). They share a focus on technical education and programming/AI topics, delivering in-depth tutorials and explainers to a tech-minded audience.

What type of content does Umar Jamil make?

Umar Jamil produces technical and AI/machine learning content inferred from video titles such as Flash Attention, memorization techniques, reasoning in LLMs, building a multimodal model in PyTorch, and interpretability topics. He uploads multiple videos with an average around 53.9K views per video and various topics; recent videos include titles with 84.5K, 62K, 132K, and 16K views.

How do we determine which channels are similar to Umar Jamil?

We analyze Umar Jamil's recent videos, generate topic-relevant search queries, check YouTube search results, and compare the meaning of each channel's content to measure similarity. The result is a ranked list sorted by SERP overlap, semantic similarity, and search appearances.

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