🔅 AI for Beginners: Inside Large Language Models

⏲ 3 hours 📁 326 Lessons

📔 Understand how LLMs actually work under the hood from scratch with practical and fun lessons. No prior knowledge required!

🎙 Taught by: Scott Kerr

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🔰 Aiopandas is a lightweight patch for Pandas that adds native async support for the most popular data processing methods: map, apply, applymap, aggregate and transform.

Allows you to pass async functions to these methods without any problems. The library will automatically run them asynchronously, controlling the number of tasks executed simultaneously using the max_parallel parameter.

✨ Key features:

▪️ Easy integration: Use as a replacement for standard Pandas functions, but now with full support for async functions.
▪️ Controlled parallelism: Automatically execute your coroutines asynchronously, with the ability to limit the maximum number of parallel tasks (max_parallel). Ideal for managing the load on external services!
▪️ Flexible error handling: Built-in options for managing runtime errors: raise, ignore, or log.
▪️ Progress Indication: Built-in tqdm support for visually tracking the progress of long operations in real time.

🌐 Github : https://github.com/telekinesis-inc/aiopandas

🔅 Building Deep Learning Applications with Keras

📝 Get a thorough introduction to Keras, a versatile deep learning framework, and learn how to build, deploy, and monitor robust deep learning models.

🌐 Author: Isil Berkun
🔰 Level: Intermediate
⏰ Duration: 1h 50m

📋 Topics: Keras, Deep Learning, Application Development

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📱Building Deep Learning Applications with Keras

🔥 Voice mode + video chat mode is now available in chat.qwenlm.ai chat

Moreover, the Chinese have posted the code of their Qwen2.5-Omni-7B - a single omni-model that can understand text, audio, images and video.

They developed a "thinker-talker" architecture that enables a model to think and talk simultaneously.

They promise to release open source models for an even greater number of parameters soon.

Simply top-notch, run and test it.

🟢 Try it : https://chat.qwenlm.ai
🟢 Paper : https://github.com/QwenLM/Qwen2.5-Omni/blob/main/assets/Qwen2.5_Omni.pdf
🟢 Blog : https://qwenlm.github.io/blog/qwen2.5-omni
🟢 GitHub : https://github.com/QwenLM/Qwen2.5-Omni
🟢 Hugging Face : https://huggingface.co/Qwen/Qwen2.5-Omni-7B
🟢 ModelScope : https://modelscope.cn/models/Qwen/Qwen2.5-Omni-7B

🌟 ChatTTS — a generative text2speech model with an emphasis on realism

 import ChatTTS
from IPython.display import Audio

chat = ChatTTS.Chat()
chat.load_models()

texts = ["<PUT YOUR TEXT HERE>",]

wavs = chat.infer(texts, use_decoder=True)
Audio(wavs[0], rate=24_000, autoplay=True)

ChatTTS is a text-to-speech model designed specifically for conversational scenarios such as LLM assistant.
ChatTTS supports both English and Chinese (if this is relevant).

🖥 GitHub
🤗 Play Hugging Face
🟡 ChatTTS Page

🔅 Deep Learning with Python: Sequence Models and Transformers

📝 The course introduces sequence data, sequence data problems, and how to solve sequence data problems using sequence models.

🌐 Author: Frederick Nwanganga
🔰 Level: Intermediate
⏰ Duration: 1h 26m

📋 Topics: Deep Learning, Python

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📱Deep Learning with Python: Sequence Models and Transformers