Short Courses by DeepLearning.AI
Take your generative AI skills to the next level with short courses from DeepLearning.AI.
Their short courses help you learn new skills, tools, and concepts efficiently.
Available for free for a limited time:
- Understanding and Applying Text Embeddings
- ChatGPT Prompt Engineering for Developers
- Building Systems with the ChatGPT API
- LangChain for LLM Application Development
- LangChain: Chat with Your Data
- Finetuning Large Language Models
- Large Language Models with Semantic Search
- Building Generative AI Applications with Gradio
- Evaluating and Debugging Generative AI Models Using Weights and Biases
- How Diffusion Models Work
- How Business Thinkers Can Start Building AI Plugins With Semantic Kernel
- Pair Programming with a Large Language Model
Links:
- https://www.deeplearning.ai/short-courses/
- Linkedin version of this post
Navigational hashtags: #armknowledgesharing #armcourses
General hashtags: #deeplearning #deeplearningai #llm #transformers #embeddings #chatgpt #gradio #diffusion #semanticsearch #promptengineering #prompts
@data_science_weekly
Take your generative AI skills to the next level with short courses from DeepLearning.AI.
Their short courses help you learn new skills, tools, and concepts efficiently.
Available for free for a limited time:
- Understanding and Applying Text Embeddings
- ChatGPT Prompt Engineering for Developers
- Building Systems with the ChatGPT API
- LangChain for LLM Application Development
- LangChain: Chat with Your Data
- Finetuning Large Language Models
- Large Language Models with Semantic Search
- Building Generative AI Applications with Gradio
- Evaluating and Debugging Generative AI Models Using Weights and Biases
- How Diffusion Models Work
- How Business Thinkers Can Start Building AI Plugins With Semantic Kernel
- Pair Programming with a Large Language Model
Links:
- https://www.deeplearning.ai/short-courses/
- Linkedin version of this post
Navigational hashtags: #armknowledgesharing #armcourses
General hashtags: #deeplearning #deeplearningai #llm #transformers #embeddings #chatgpt #gradio #diffusion #semanticsearch #promptengineering #prompts
@data_science_weekly
TinyML and Efficient Deep Learning Computing
Large generative models (e.g., large language models, diffusion models) have shown remarkable performance, but they require a massive amount of computational resources. To make them more accessible, it is crucial to improve their efficiency.
This course will introduce efficient AI computing techniques that enable powerful deep learning applications on resource-constrained devices. Topics include model compression, pruning, quantization, neural architecture search, distributed training, data/model parallelism, gradient compression, and on-device fine-tuning. It also introduces application-specific acceleration techniques for large language models, diffusion models, video recognition, and point cloud. This course will also cover topics about quantum machine learning.
Students will get hands-on experience deploying large language models (e.g., LLaMA 2) on a laptop.
Link: https://hanlab.mit.edu/courses/2023-fall-65940
Navigational hashtags: #armknowledgesharing #armcourses
General hashtags: #deeplearning #llm #largelanguagemodels #diffusion #diffusionmodels #pruning #quantization
@data_science_weekly
Large generative models (e.g., large language models, diffusion models) have shown remarkable performance, but they require a massive amount of computational resources. To make them more accessible, it is crucial to improve their efficiency.
This course will introduce efficient AI computing techniques that enable powerful deep learning applications on resource-constrained devices. Topics include model compression, pruning, quantization, neural architecture search, distributed training, data/model parallelism, gradient compression, and on-device fine-tuning. It also introduces application-specific acceleration techniques for large language models, diffusion models, video recognition, and point cloud. This course will also cover topics about quantum machine learning.
Students will get hands-on experience deploying large language models (e.g., LLaMA 2) on a laptop.
Link: https://hanlab.mit.edu/courses/2023-fall-65940
Navigational hashtags: #armknowledgesharing #armcourses
General hashtags: #deeplearning #llm #largelanguagemodels #diffusion #diffusionmodels #pruning #quantization
@data_science_weekly
Understanding Deep Learning by Simon J.D. Prince
Deep learning is a fast-moving field with sweeping relevance in today’s increasingly digital world. Understanding Deep Learning provides an authoritative, accessible, and up-to-date treatment of the subject, covering all the key topics along with recent advances and cutting-edge concepts. Many deep learning texts are crowded with technical details that obscure fundamentals, but Simon Prince ruthlessly curates only the most important ideas to provide a high density of critical information in an intuitive and digestible form. From machine learning basics to advanced models, each concept is presented in lay terms and then detailed precisely in mathematical form and illustrated visually. The result is a lucid, self-contained textbook suitable for anyone with a basic background in applied mathematics.
- Up-to-date treatment of deep learning covers cutting-edge topics not found in existing texts, such as transformers and diffusion models
- Short, focused chapters progress in complexity, easing students into difficult concepts
- Pragmatic approach straddling theory and practice gives readers the level of detail required to implement naive versions of models
- Streamlined presentation separates critical ideas from background context and extraneous detail
- Minimal mathematical prerequisites, extensive illustrations, and practice problems make challenging material widely accessible
- Programming exercises offered in accompanying Python Notebooks
Link: https://udlbook.github.io/udlbook/
Navigational hashtags: #armknowledgesharing #armbooks
General hashtags: #ml #machinelearning #dl #deeplearning #transformers #diffusion
@data_science_weekly
Deep learning is a fast-moving field with sweeping relevance in today’s increasingly digital world. Understanding Deep Learning provides an authoritative, accessible, and up-to-date treatment of the subject, covering all the key topics along with recent advances and cutting-edge concepts. Many deep learning texts are crowded with technical details that obscure fundamentals, but Simon Prince ruthlessly curates only the most important ideas to provide a high density of critical information in an intuitive and digestible form. From machine learning basics to advanced models, each concept is presented in lay terms and then detailed precisely in mathematical form and illustrated visually. The result is a lucid, self-contained textbook suitable for anyone with a basic background in applied mathematics.
- Up-to-date treatment of deep learning covers cutting-edge topics not found in existing texts, such as transformers and diffusion models
- Short, focused chapters progress in complexity, easing students into difficult concepts
- Pragmatic approach straddling theory and practice gives readers the level of detail required to implement naive versions of models
- Streamlined presentation separates critical ideas from background context and extraneous detail
- Minimal mathematical prerequisites, extensive illustrations, and practice problems make challenging material widely accessible
- Programming exercises offered in accompanying Python Notebooks
Link: https://udlbook.github.io/udlbook/
Navigational hashtags: #armknowledgesharing #armbooks
General hashtags: #ml #machinelearning #dl #deeplearning #transformers #diffusion
@data_science_weekly