Designing, Visualizing, and Understanding Deep Neural Networks
UC Berkeley CS L182
John Canny A theoretical course focusing on design principles and best practices for designing deep neural networks. https://
bcourses.berkeley.edu/courses/1487769
AI
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Deep Neural Networks Design Principles UC Berkeley Course
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NYU Deep Learning Course by LeCun and Canziani
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Deep Learning
NYU DS-GA 1008
Yann LeCun and Alfredo Canziani This course covers the latest techniques in deep learning and representation learning with applications to computer vision, natural language understanding, and speech recognition. https://
atcold.github.io/pytorch-Deep-L
earning/
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MIT Deep Learning Course with TensorFlow and Practical Applications
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Introduction to Deep Learning
MIT Course 6.S191
Alexander Amini and Ava Soleimany Introductory course on deep learning methods and practical experience using TensorFlow. Covers applications for computer vision, natural language processing, and more. http://
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Language Beyond Token Prediction: Intent and Purpose
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“Chiang believes that language without the intention, emotion and purpose that humans bring to it becomes meaningless. “Language is a way of facilitating interactions with other beings. That is entirely different than the sort of next-token prediction, which is what we have
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SQL-PaLM: LLM-based Text-to-SQL Achieves State-of-the-Art
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9/ SQL-PaLM – an LLM-based Text-to-SQL adopted from PaLM-2; achieves SoTA in both in-context learning and fine-tuning settings; the few-shot model outperforms the previous fine-tuned SoTA by 3.8% on the Spider benchmark.
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CodeTF: Open-Source Transformer Library for Code LLMs
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10/ CodeTF – an open-source Transformer library for state-of-the-art code LLMs; supports pretrained code LLMs and popular code benchmarks, including standard methods to train and serve code LLMs efficiently.
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MERT: Self-Supervised Acoustic Music Understanding Model
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6/ MERT – an acoustic music understanding model with large-scale self-supervised training; it incorporates a superior combination of teacher models to outperform conventional speech and audio approaches.
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Bytes Are All You Need: Direct File Classification with Transformers
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7/ Bytes Are All You Need – investigates performing classification directly on file bytes, without needing to decode files at inference time; achieves ImageNet Top-1 accuracy of 77.33% using a transformer backbone;
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Direct Preference Optimization: Training LLMs Without RLHF
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8/ Direct Preference Optimization – while helpful to train safe & useful LLMs, RLHF can be complex and often unstable; this work proposes an approach to finetune LMs by solving a classification problem on the human preferences data, with no RL required.https://t.co/DZ0GSarfuT
— DAIR.AI (@dair_ai) 4 juin 20238/ Direct Preference Optimization – while helpful to train safe & useful LLMs, RLHF can be complex and often unstable; this work proposes an approach to finetune LMs by solving a classification problem on the human preferences data, with no RL required.
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Thought Cloning: Learning to Think While Acting
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4/ Thought Cloning – introduces an imitation learning framework to learn to think while acting; the idea is not only to clone the behaviors of human demonstrators but also the thoughts humans have when performing behaviors.