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Latest Neural Network Architectures AI Videos

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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
... 4:49
From Zero to Autonomous: Essential Secrets of Enterprise Agentic AI in 5 Minutes | AI Insights

SiliconANGLE theCUBE

1+ months ago 3K views

Architecting agentic AI systems requires a focus on intelligence and autonomy, emphasizing the need for robust foundational architecture that integrates perception, reasoning, and action. Multi-agent collaboration is crucial to enhance efficiency and resource utilization, supported by effective communication and dynamic task allocation. Continuous performance optimization ensures that AI systems adapt to changing business environments, maintaining relevance in operations. Without these elements, AI deployments risk becoming misaligned with organizational goals, diminishing both efficiency and return on investment.
03:06 - 03:09

Continuous optimization is necessary to keep AI systems aligned with business needs.

Autonomous Systems AI Trends
... 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
... 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
... 52:26
Risto Miikkulainen: AI Research, Neural Networks, Evolutionary Computation|Hrvoje Kukina Podcast #25

Hrvoje Kukina

1+ months ago 3K views

AI research is advancing rapidly, drawing on insights from cognitive sciences and interdisciplinary fields. The limitations of current neural networks compared to biological systems are significant, such as spiking neurons and online learning capabilities. Evolving AI systems could lead to greater adaptability and assist in addressing AI alignment challenges. Insights into human cognition and emotions are critical for developing robust AI, which can navigate complex social environments and make better decisions. Emphasizing collaboration among AI disciplines and responsible AI development will enhance future research and practical applications in AI, ensuring beneficial innovations for society.
05:31 - 08:21

Neuroevolution might enhance AI creativity and adaptability.

Machine Learning Research & Innovations
... 10:
How Transformers Changed AI Forever

GAI-Observe.online

1+ months ago 2K views

The video discusses the transformative impact of the Transformer architecture on natural language processing (NLP). It highlights key innovations such as parallel processing, attention mechanisms, and architecture design that enhance scalability and efficiency. The self-attention mechanism enables deep contextual understanding while multi-head attention captures diverse relationships. The video explores position encoding's importance and outlines encoder-decoder architectures for tasks like text generation and translation. Practical considerations for successful training include data preparation and optimization strategies. The evolution of Transformers continues with new architectures, applications, and ethical considerations shaping future developments in AI.
04:10 - 04:35

Encoder processes text into rich representations for effective feature enhancement.

Natural Language Processing (NLP) Research & Innovations
... 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
... 15:44
Scaling Up and Out for AI - Six Five at Marvell Industry Analyst Day

Six Five Media

1+ months ago 3K views

AI's success in data centers hinges on components like GPUs, memory, and storage, with networking often overlooked despite being a critical bottleneck. The discussion emphasizes both scale-up and scale-out approaches to enhance performance, reduce power consumption, and lower latency. Scale-up networking involves consolidating resources to function like a supercomputer, while scale-out focuses on connecting multiple clusters to handle extensive AI workloads. Trends indicate a shift towards larger scale-up clusters and distributed data centers requiring high-bandwidth connections. Custom solutions will play a critical role as demand for efficient, high-performance AI systems grows.
04:51 - 05:17

Shifts to larger scale-up clusters necessitate improved interconnect solutions.

Marvell AI hardware Tech & Hardware
... 15:44
Scaling Up and Out for AI - Six Five at Marvell Industry Analyst Day

Six Five Media

1+ months ago 3K views

AI's success in data centers hinges on components like GPUs, memory, and storage, with networking often overlooked despite being a critical bottleneck. The discussion emphasizes both scale-up and scale-out approaches to enhance performance, reduce power consumption, and lower latency. Scale-up networking involves consolidating resources to function like a supercomputer, while scale-out focuses on connecting multiple clusters to handle extensive AI workloads. Trends indicate a shift towards larger scale-up clusters and distributed data centers requiring high-bandwidth connections. Custom solutions will play a critical role as demand for efficient, high-performance AI systems grows.
04:51 - 05:17

Shifts to larger scale-up clusters necessitate improved interconnect solutions.

Marvell AI hardware Tech & Hardware