Introduction to Transformers | Transformers Part 1

Transformers are a powerful class of models in natural language processing and machine learning, revolutionizing various tasks. From attention mechanisms to self-attention, transformers have reshaped the landscape of deep learning.

Introduced by Vaswani et al., transformers use self-attention mechanisms to process input data in parallel, making them highly efficient for tasks like language translation, summarization, and various other sequence-based tasks.

A Comprehensive Survey on Applications of Transformers for Deep Learning Tasks:

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⌚Time Stamps⌚

00:00 - Intro
01:01 - What is Transformer? / Overview
05:12 - History of Transformer / Research Paper
07:55 - Impact of Transformers in NLP
10:29 - Democratizing AI
13:08 - Multimodal Capability of Transformers
16:28 - Acceleration of Gen AI
19:07 - Unification of Deep Learning
21:09 - Why transformers were created? / Seq-to-Seq Learning with Neural Networks
25:25 - Neural Machine Translation by Jointly Learning to Align and Translate
33:16 - Attention is all you need
39:10 - The Timeline of Transformers
41:42 - The Advantages of Transformers
46:30 - Real World Application of Transformers
47:30 - DALL*E 2
48:20 - AlphaFold by Google Deepmind
49:08 - OpenAI Codex
49:41 - A Comprehensive Survey on Applications of Transformers for Deep Learning Tasks
50:30 - Disadvantages of Transformers
54:40 - The Future of Transformers
59:20 - Outro

[deepanshuverma]

Sorry Bae can't talk right now, nitish sir dropped a much awaited master piece on transformers had to watch it first :-)

[gauravbabbar]

One of the best Indian speaker I have come across in Data science Field. It took me 3 days to go through so many videos and finally land on this video to realise what communication skills can bring to a video! Kudos mentor!

[prodigy]

Thousands of youtube channels and this is the only place which sticks to my mind. Thank you so much for such quality Nitish! Cheers

[ariousvinx]

Finally the most awaited Transformers! Learned a lot from this lecture! Your videos are always so detailed, you never rush for sake of completing. Thanks a lot sir ! ❤

[Samurai-hfun]

Sir can’t wait for those 4-5 videos please release ASAP.. I know you’re busy persons

I have no words to explain what impact you’re creating in students life….
Thank you very much for everything ❤🙏

[deependraverma]

You are not only a teacher, you are a magician. You can generate a spark in a non-tech guy even. Kya padhate ho sir!! Hats off, Lots of respect.

[souvik]

Just Wow !!!
Nitish .. Everyone can't deliver things the way you do... Excellent.
Request to you is to never leave doing this.🙏

[rashidnadeem]

Thank you so much for giving references with paper, now I am pursuing a PhD in deep reinforcement learning using transformer this video will help me more, thank u for giving great survey paper as a reference.
Thanks for ur wonderful contents. ❤❤

[rutvikkapuriya]

Thank you so much for giving references with paper, now I am pursuing a PhD in deep reinforcement learning using transformer this video will help me more, thank u for giving great survey paper as a reference.

[shreyanshgaurkar]

I WANT TO SAY SOMETHING ABOUT THESE MAN, THESE MAN IS JUST INCREDIBLE IN TEACHING NOT ONLY HE IS AMAZING TUTOR BUT ALSO HE IS GREAT LEADER FOR ALL AI/ML/DL STUDENTS, BEFORE I WAS VERY TENSED ABOUT TRANSFORMERS GPT HOW TO LEARN BUT NOW I FEEL LIKE GOD IS THERE TO TEACH SUCH A MASTER PIECE LIKE TRANSFORMER , NITISH SIR U R A GEM , AND I REALLY WANT TO MEET YOU PERSONALLY TO TOUCH YOUR FEET FIRST. THANX ALOT

[yashashree]

sir please create a playlist on finetuning LLMs also. it is currently the most demanded skill in Data science and AI

[rahulkumawat]

sir, i don't have word to tell you how great your lecture are, I watched lots of videos on transformer but no one gives me such detail insight about attention mechanism, can't wait to watch next videos

[jituranjandakua]

One of the best detailed video on transformers

[jbvmuou]

3 saal se padh rha hu AI, MSc bhi kar liya, attention par project v kar liya... but samjha nahi tha honestly... Abhi pakad me aarha hai Nitish Bhai... bhale manus ho tum... acha kaam jari rakho, hum tumhare sath he... :=)

[sowmyachinthapally]

Learned a lot from this lecture. Thank you very much sir for patiently explaining everything. Please continue this series. It would be very helpful.

[myself]

Here are the grouped key takeaways for the first two timestamps based on the original summary:

01:01 - *What is Transformer? / Overview*
- Transformers are neural network architectures designed to handle sequence-to-sequence tasks, similar to previous architectures like RNNs.
- Transformers excel in tasks like machine translation, question answering, and text summarization by transforming one sequence into another.
- The architecture of transformers includes an encoder and decoder, utilizing self-attention for parallel processing, making them scalable and efficient.

05:12 - *History of Transformer / Research Paper*
- The first impactful paper, "Sequence to Sequence Learning with Neural Networks" (2014-15), proposed using an encoder-decoder architecture with LSTMs for sequence-to-sequence tasks like machine translation.
- This architecture struggled with long input sentences because summarizing the entire sentence into a single context vector was insufficient, leading to poor translation quality.
- The second paper, "Neural Machine Translation by Jointly Learning to Align and Translate, " introduced the concept of attention to address the limitations of context vectors in handling long sentences.
- Attention-based encoder-decoder models improve by maintaining a hidden state at each step, allowing better handling of long input sequences.
- Despite the improvements with attention mechanism, LSTM-based sequential training is slow, preventing training on large datasets and hindering transfer learning.
- Lack of transfer learning means models must be trained from scratch for every new task, requiring significant time, effort, and data.
- The fundamental problem with LSTM-based encoder-decoder architecture is its inability to parallelize training, limiting scalability.
- The landmark paper "Attention Is All You Need" (2017) introduced the transformer architecture, solving the sequential training problem of previous models.
- The paper introduced a fully attention-based architecture, using self-attention instead of LSTMs or RNNs.

07:55 - Impact of Transformers in NLP
- The impact of transformers is profound, having created a significant AI revolution and transforming various industries.
- Transformers have significantly advanced NLP problems efficiently, outperforming previous methods and models, such as LSTM and RNN.
- AI applications like ChatGPT have changed how people interact with machines

10:29 - Democratizing AI
- Transformers democratized AI, making it accessible for small companies and researchers by providing pre-trained models that can be fine-tuned for specific tasks.
- Pre-trained transformers like BERT and GPT, trained on large datasets, are available for public use, enabling efficient fine-tuning for specific applications.
- Transfer learning allows pre-trained transformers to be fine-tuned on small datasets, making state-of-the-art NLP accessible to small companies and individual researchers.
- Libraries like Hugging Face simplify the fine-tuning process, allowing state-of-the-art sentiment analysis and other NLP tasks to be implemented with minimal code.

13:08 - Multimodal Capability of Transformers
- Transformers are highly flexible, capable of handling different data modalities like text, images, and speech.
- Researchers have created representations for different modalities, enabling transformers to work with images and speech similar to text.
- Multi-modal applications like ChatGPT now support visual search and audio input, demonstrating transformers' versatility.

16:28 - Acceleration of Gen AI
- Transformers have accelerated the development of generative AI, making tasks like text, image, and video generation more feasible and efficient.
- Generative AI has become a crucial field, with companies increasingly expecting knowledge of generative AI tools and applications.

19:07 - Unification of Deep Learning
- There has been a paradigm shift in the last few years where transformers are used for various deep learning problems, including NLP, generative AI, computer vision, and reinforcement learning.
- This unification of deep learning through transformers is significant, reducing the need for different architectures for different problems.
- Despite some drawbacks, transformers have greatly impacted the deep learning field by unifying various applications under a single architecture.

21:09 - Why transformers were created? / Seq-to-Seq Learning with Neural Networks
- The first impactful paper, "Sequence to Sequence Learning with Neural Networks" (2014-15), proposed using encoder-decoder architecture with LSTMs for sequence-to-sequence tasks like machine translation.
- This architecture struggled with long input sentences because summarizing the entire sentence into a single context vector was insufficient, leading to poor translation quality.

25:25 - Neural Machine Translation by Jointly Learning to Align and Translate
- The second paper, "Neural Machine Translation by Jointly Learning to Align and Translate, " introduced the concept of attention to address the limitations of context vectors in handling long sentences.
- Attention-based encoder-decoder models improve by maintaining a hidden state at each step, allowing better handling of long input sequences.
- Attention mechanism eliminates the need to send a single context vector all at once, focusing on relevant parts of the input sentence for each output word.
- The context vector dynamically calculated at each decoder timestep influences the output, improving translation quality for longer sentences.
- Despite the improvements with attention mechanism, LSTM-based sequential training is slow, preventing training on large datasets and hindering transfer learning.
- Lack of transfer learning means models must be trained from scratch for every new task, requiring significant time, effort, and data.
- The fundamental problem with LSTM-based encoder-decoder architecture is its inability to parallelize training, limiting scalability.

33:16 - Attention is all you need
- The landmark paper "Attention Is All You Need" (2017) introduced the transformer architecture, solving the sequential training problem of previous models.
- The paper introduced a fully attention-based architecture, using self-attention instead of LSTMs or RNNs.
- The architecture includes components like residual connections, layer normalization, and feed-forward neural networks, allowing parallel training and scalability.
- Introduction of transfer learning in NLP led to models like BERT and GPT, which can be fine-tuned easily.
- Self-attention replaces LSTM, enabling parallel training and speeding up the process.
- The architecture is stable and robust, with hyperparameters that remain effective over time.
- "Attention Is All You Need" paper is groundbreaking because it is not incremental; it introduced a completely new architecture from scratch.

39:10 - The Timeline of Transformers
- The transformer architecture includes many unique components, making it distinct from previous models.
- Overview of the evolution in NLP: RNNs and LSTMs dominated until 2014, attention mechanism introduced in 2014, transformers in 2017, and large-scale models like BERT and GPT emerged in 2018.
- Between 2018 and 2020, transformers expanded to other domains like vision transformers and models in structural biology (e.g., AlphaFold 2).
- From 2021 onwards, the era of generative AI began with tools like GPT-3, DALL-E, and Codex, leading to the current prominence of ChatGPT and other generative models.

41:42 - The Advantages of Transformers
- Transformers' key advantages include scalability, allowing fast training on large datasets.
- Transformers support transfer learning, enabling easy fine-tuning on custom tasks after pre-training on large datasets.
- Transformers can handle multimodal input and output, such as text, images, and speech.
- The architecture of transformers is flexible, allowing the creation of different types like encoder-only (BERT) or decoder-only (GPT) models.
- The AI community's curiosity about transformers has led to a vibrant ecosystem with many libraries, tools, videos, and blogs available.

46:30 - *Real World Application of Transformers*
- Transformers can be integrated with other AI techniques, such as GANs for image generation, reinforcement learning for game-playing agents, and CNNs for image captioning.
- The main applications of transformers include chatbots like ChatGPT, which generate text, and tools like DALL-E 2, which create images from text prompts.

[ppfeelm]

Sir I request you to please don't delay your new videos of deep learning. You are really an amazing Teacher .

[sagarbhagwani]

Finally we are getting closer and closer❤ thanks sir

[daudiomusic]

Literally one of the best video on youtube for understanding Transformers

[DataGenius_network]

Please upload more videos on transformers. And thank you so much sir for providing me such a best content i follows your video of data science from starting .I will be waiting for your next video in deep learning.