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... 1:45:37
Build an LLM from Scratch 4: Implementing a GPT model from Scratch To Generate Text

Sebastian Raschka

1+ months ago 2K views

In this segment, the implementation of the GPT model architecture is detailed, focusing on its various components, such as embedding layers, masked multi-head attention modules, and transformer blocks. The architecture also employs layer normalization and feed-forward networks with GELU activations. Shortcuts enhance training efficiency, addressing challenges like vanishing gradients. The output, consisting of logits, maps input tokens to predictions, with an emphasis on generating new tokens iteratively. Future chapters will delve into pre-training, optimizing the model, and generating coherent text, linking these components to practical applications in large language models.
02:53 - 03:00

Transformer blocks consist of multiple components, including attention.

Natural Language Processing (NLP) Education
... 27:30
E13: NVIDIA'S HUGE AI Announcements Will Change Everything

Ticker Symbol: YOU

1+ months ago 69K views

Nvidia's Dion Harris discusses the evolution of AI technology and its applications beyond training, emphasizing the importance of large-scale inference for AI models. He highlights Nvidia's advancements in creating efficient platforms for deploying AI, particularly with the Blackwell architecture, which assists in improving performance and energy efficiency. The interplay between pre-training, post-training, and inference signifies an evolving, continuous learning cycle that will benefit various sectors, including healthcare and robotics. Upcoming sessions at GTC will further explore these innovations and their implications for AI deployment across industries.
08:33 - 08:50

Applications of low-precision hardware significantly enhance AI performance across fields.

Nvidia AI hardware Tech & Hardware
... 10:40
How AI Actually Works in 10 Minutes

Re: Design

1+ months ago 3K views

AI converts inputs like images into numerical matrices, which undergo mathematical transformations through neural networks mimicking human brain functions. Each node performs operations on incoming data, utilizing activation functions to learn complex patterns. The overview discusses gradient descent for error minimization, introducing techniques like batch normalization for training efficiency. Another focus is on transfer learning, which allows specialized AI tasks using smaller datasets. Generative adversarial networks enhance quality through competitive training, while neural architecture search optimizes designs. Advanced techniques like quantization and federated learning address computational efficiency and privacy, essential for modern AI applications.
02:35 - 02:45

Backpropagation adjusts model weights to minimize errors during learning.

Machine Learning Education
... 15:1
TensorFlow for Beginners - Lesson 2 | AI That Reads Handwriting! (MNIST Neural Network)

The Digital Tutor

1+ months ago 1K views

Teaching computers to recognize handwritten numbers enhances AI skills significantly. This lesson builds on previous learnings where AI was trained to recognize number patterns. Computers perceive images as numerical grids, differentiating brightness levels, which is vital for recognizing patterns. Neural networks, inspired by human brain functionality, learn through analyzing thousands of examples. The lesson details the process of importing datasets from TensorFlow, preparing data for neural networks, scaling pixel values to enhance training efficiency, and defining a model to train and predict handwritten digit recognition, culminating in showcasing the AI's capabilities.
07:05 - 07:30

Demonstrates the impact of scaling pixel values on training efficiency.

Google Neural Network Architectures Education
... 10:53
Titans by Google: The Era of AI After Transformers?

AI Papers Academy

1+ months ago 4K views

Transformers, introduced in Google's 2017 paper "Attention is All You Need," revolutionized AI with their attention mechanism, enabling efficient processing of token sequences. However, their quadratic computational costs limit scalability for longer sequences. In contrast, recurrent models offer linear dependency but lack the performance of Transformers. Google Research's new architecture, Titans, mitigates Transformers' cost issues by implementing a deep neural long-term memory module inspired by human memory. This model updates weights based on surprising inputs, learns associations through a memory module, and shows promising results against various benchmarks, outperforming traditional models in long sequence tasks.
02:31 - 04:27

The neural long-term memory module learns through surprising inputs and incorporates forgetting mechanisms.

Google Machine Learning Research & Innovations
... 14:6
From Worm to AI: How Control Theory Unlocks Neural Networks

CompuFlair

1+ months ago 2K views

Unlocking the secrets of large language models involves grappling with the blackbox problem of AI systems. By studying simpler models, such as the fully mapped neural network of C. elegans, we can better understand the flow of information and the dynamics of neural networks. Control theory provides a mathematical framework that reveals how neural structures dictate their function and behavior. Experimental findings within the C. elegans neural network not only validate predictions about known neurons but also uncover the roles of previously unstudied neurons, thereby suggesting pathways for future research in artificial neural networks.
10:47 - 10:51

Control theory predicts roles of previously unstudied neurons in C. elegans.

Machine Learning Education
... 21:13
why ai neural networks will change trading forever and how to build yours in minutes!

QuantProgram

1+ months ago 3K views

Machine learning and AI influence trading and investment decisions, particularly using neural networks to generate advanced strategies. Neural networks function similarly to human brains, identifying patterns through errors and adjustments in weight. Methods such as feedforward and recurrent neural networks are explained, with a focus on their applications in stock price prediction and strategy optimization. The discussion also covers the importance of hyperparameter tuning, challenges in computational demand, and the potential for improved returns compared to traditional S&P 500 investments. Applications of AI extend to portfolio rebalancing and enhancing existing strategies.
06:09 - 07:52

Machine learning models help optimize and rebalance investment portfolios for better returns.

Neural Network Architectures Finance
... 39:
Day 09 - Dive into Deep learning frameworks | Jetson AI #pantechelearning

pantechelearning

1+ months ago 1K views

Deep learning frameworks, particularly TensorFlow, streamline model training and development. Efficient coding reduces the chance of errors and increases scalability by enabling code execution across CPUs and GPUs. TensorFlow employs a graph-based architecture for program execution, where nodes represent operations, enhancing computational efficiency. The session also introduced libraries like Keras, which simplifies building neural networks, and PyTorch, known for its extensive use in AI research. Additionally, challenges such as installation processes and remote connection setups for practical sessions were discussed, providing practical guidance for completing AI projects.
12:21 - 12:58

Building computation graphs is fundamental to working with TensorFlow.

Jetson Deep Learning Education
... 39:
Day 09 - Dive into Deep learning frameworks | Jetson AI #pantechelearning

pantechelearning

1+ months ago 1K views

Deep learning frameworks, particularly TensorFlow, streamline model training and development. Efficient coding reduces the chance of errors and increases scalability by enabling code execution across CPUs and GPUs. TensorFlow employs a graph-based architecture for program execution, where nodes represent operations, enhancing computational efficiency. The session also introduced libraries like Keras, which simplifies building neural networks, and PyTorch, known for its extensive use in AI research. Additionally, challenges such as installation processes and remote connection setups for practical sessions were discussed, providing practical guidance for completing AI projects.
12:21 - 12:58

Building computation graphs is fundamental to working with TensorFlow.

Jetson Deep Learning Education