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. In this video, I have explained what is meant by Deep Learning, Artificial Neural Networks and Applications of Deep Learning.

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Hi guys! I am Siddhardhan. I work in the field of Data Science and Machine Learning. It all started with my curiosity to learn about Artificial Intelligence and the ability of AI to solve several Real Life Problems. I worked on several Machine Learning & Deep Learning projects involving Computer Vision.
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I am making a "Hands-on Machine Learning Course with Python" in YouTube. I'll be posting 3 videos per week. 2 videos on Machine Learning basics (Monday & Wednesday Evening). 1 video on a Machine Learning project (Friday Evening).

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This is the 6th video in a series on using large language models (LLMs) in practice. Here, I review key aspects of developing a foundation LLM based on the development of models such as GPT-3, Llama, Falcon, and beyond.

More Resources:
▶️ Series Playlist: https://www.youtube.com/playli....st?list=PLz-ep5RbHos Read more: https://towardsdatascience.com..../how-to-build-an-llm

[1] BloombergGPT: https://arxiv.org/pdf/2303.17564.pdf
[2] Llama 2: https://ai.meta.com/research/p....ublications/llama-2-
[3] LLM Energy Costs: https://www.statista.com/stati....stics/1384401/energy
[4] arXiv:2005.14165 [cs.CL]
[5] Falcon 180b Blog: https://huggingface.co/blog/falcon-180b
[6] arXiv:2101.00027 [cs.CL]
[7] Alpaca Repo: https://github.com/gururise/AlpacaDataCleaned
[8] arXiv:2303.18223 [cs.CL]
[9] arXiv:2112.11446 [cs.CL]
[10] arXiv:1508.07909 [cs.CL]
[11] SentencePience: https://github.com/google/sent....encepiece/tree/maste
[12] Tokenizers Doc: https://huggingface.co/docs/tokenizers/quicktour
[13] arXiv:1706.03762 [cs.CL]
[14] Andrej Karpathy Lecture: https://www.youtube.com/watch?v=kCc8FmEb1nY&t=5307s
[15] Hugging Face NLP Course: https://huggingface.co/learn/n....lp-course/chapter1/7
[16] arXiv:1810.04805 [cs.CL]
[17] arXiv:1910.13461 [cs.CL]
[18] arXiv:1603.05027 [cs.CV]
[19] arXiv:1607.06450 [stat.ML]
[20] arXiv:1803.02155 [cs.CL]
[21] arXiv:2203.15556 [cs.CL]
[22] Trained with Mixed Precision Nvidia: https://docs.nvidia.com/deeple....arning/performance/m
[23] DeepSpeed Doc: https://www.deepspeed.ai/training/
[24] https://paperswithcode.com/method/weight-decay
[25] https://towardsdatascience.com..../what-is-gradient-cl
[26] arXiv:2001.08361 [cs.LG]
[27] arXiv:1803.05457 [cs.AI]
[28] arXiv:1905.07830 [cs.CL]
[29] arXiv:2009.03300 [cs.CY]
[30] arXiv:2109.07958 [cs.CL]
[31] https://huggingface.co/blog/ev....aluating-mmlu-leader
[32] https://www.cs.toronto.edu/~hi....nton/absps/JMLRdropo

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Intro - 0:00
How much does it cost? - 1:30
4 Key Steps - 3:55
Step 1: Data Curation - 4:19
1.1: Data Sources - 5:31
1.2: Data Diversity - 7:45
1.3: Data Preparation - 9:06
Step 2: Model Architecture (Transformers) - 13:17
2.1: 3 Types of Transformers - 15:13
2.2: Other Design Choices - 18:27
2.3: How big do I make it? - 22:45
Step 3: Training at Scale - 24:20
3.1: Training Stability - 26:52
3.2: Hyperparameters - 28:06
Step 4: Evaluation - 29:14
4.1: Multiple-choice Tasks - 30:22
4.2: Open-ended Tasks - 32:59
What's next? - 34:31

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Sometimes, a large language model is too much horse, so to speak. For tasks that are smaller in scope and more fine-tuned for a specific task, a small language model, or SLM, might be a better fit. SLMs are cheaper than LLMs, so they're great for smaller businesses or just automation tasks that don't need all the bells and whistles an LLM offers.

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Today we're diving deep into one of the hottest topics in AI right now. Building actual agents in Python. Not just chat bots to respond to your queries, but autonomous systems with memory goals and the ability to take actions in the world.

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MIT 14.41, Public Finance and Public Policy, Fall 2024
Instructor: Prof. Jonathan Gruber
View the complete course: https://ocw.mit.edu/courses/14....-41-public-finance-a
YouTube Playlist: https://www.youtube.com/playli....st?list=PLUl4u3cNGP6

In this lecture Prof. Gruber discusses the importance of studying public finance, which is the role of the government in the economy. When should the government intervene? How should the government intervene? What is the effect of those interventions?


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In this lecture, we highlight the course logistics and give a brief overview of the course.

In this video I explain how Einstein Summation (einsum) works and why it is amazing, at the end of the video you too will realize that einsum is all you need. It works similar in all libraries with the following function call: torch.einsum, tf.einsum, numpy.einsum (pytorch, tensorflow, numpy).

Video title was recklessly stolen from the following blogpost which I was inspired by (read it!):
https://rockt.github.io/2018/04/30/einsum

Also looked quite a bit at:
https://obilaniu6266h16.wordpr....ess.com/2016/02/04/e
https://ajcr.net/Basic-guide-to-einsum/

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REFERENCES
[1] The main Paper: https://arxiv.org/abs/1706.03762
[2] Tensor2Tensor has some code with a tutorial: https://www.tensorflow.org/tut....orials/text/transfor
[3] Transformer very intuitively explained - Amazing: http://jalammar.github.io/illustrated-transformer/
[4] Medium Blog on intuitive explanation: https://medium.com/inside-mach....ine-learning/what-is
[5] Pretrained word embeddings: https://nlp.stanford.edu/projects/glove/
[6] Intuitive explanation of Layer normalization: https://mlexplained.com/2018/1....1/30/an-overview-of-
[7] Paper that gives even better results than transformers (Pervasive Attention): https://arxiv.org/abs/1808.03867
[8] BERT uses transformers to pretrain neural nets for common NLP tasks. : https://ai.googleblog.com/2018..../11/open-sourcing-be
[9] Stanford Lecture on RNN: http://cs231n.stanford.edu/sli....des/2018/cs231n_2018
[10] Colah’s Blog: https://colah.github.io/posts/....2015-08-Understandin
[11] Wiki for timeseries of events: https://en.wikipedia.org/wiki/....Transformer_(machine