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Deep Learning News
Hackernoon
1w
205
Image Credit: Hackernoon
A Simplified State Space Model Architecture
- The paper discusses a simplified architecture for selective state space models (SSMs).
- Selective SSMs are standalone sequence transformations that can be incorporated into neural networks.
- The architecture combines the linear attention block and MLP block into one homogeneous stack.
- This simplified architecture is inspired by the gated attention unit (GAU) approach.
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Hackernoon
1w
33
Image Credit: Hackernoon
The HackerNoon Newsletter: Understanding the Twitter API So You Can Design Your Own (12/16/2024)
- Understanding the Twitter API So You Can Design Your Own - Explore how the X (Twitter) home timeline (x.com/home) API is designed and what approaches they use to solve multiple challenges.
- New Tool Promises Faster Websites with Streamlined Server-Rendered UI - The HMPL project is a small template language for displaying UI from server to client.
- iPhones Could Become More Expensive Under the Trump Presidency - Trump has proposed to place tariffs on imported goods from China, Mexico, and Canada.
- Quickly Bulk Load Image to E-commerce Sites With This Guide - Learn how to efficiently upload bulk images to e-commerce sites instead of manually updating them one by one.
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Medium
1w
352
Image Credit: Medium
The Transformative Power of Artificial Intelligence
- AI plays a crucial role in early disease detection and personalized treatment.
- Adaptive learning platforms powered by AI provide tailored education to students.
- AI enables automation and efficient decision making in various processes.
- AI has the potential to revolutionize sectors like transportation and improve road safety.
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Medium
1w
117
Image Credit: Medium
How I Evolved My Voiceover Skills Overnight
- Voice cloning technology, such as SoundWaves AI, is revolutionizing the world of voiceovers.
- With SoundWaves AI, users can create ultra-realistic, human-like voices in multiple languages.
- The process of voice cloning with SoundWaves AI is simple and user-friendly.
- SoundWaves AI offers a free commercial license, allowing users to start their own voice cloning agency.
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Medium
1w
336
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Predictive Machine Failure Using Multiclass Classification with Deep Learning
- The dataset consists of various features related to machine performance and failure types.
- Class distribution is checked to handle class imbalance.
- Data is preprocessed by normalizing features and splitting into training, validation, and test sets.
- A neural network model is defined and trained using Keras.
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Hackernoon
1w
269
Image Credit: Hackernoon
Cutting-Edge Techniques That Speed Up AI Without Extra Costs
- The authors present a new technique called Selective State Space Models which aims to speed up AI without extra costs.
- The authors provide an overview of a model (SSMs) with larger hidden state dimension being more effective but slower, and identify that the recurrent mode is more flexible than the convolution mode, but the latter is more efficient.
- The authors propose to leverage properties of modern accelerators (GPUs) to materialize the state h only in more efficient levels of the memory hierarchy. In particular, they attempt to not actually materialize the full state h.
- Selective scan layer is illustrated in Figure 1: it is a memory-efficient layer which uses an efficient parallel scan algorithm to avoid sequential recurrence.
- The intermediate states, which are necessary for backpropagation, are not stored but recomputed in the backward pass when the inputs are loaded from HBM to SRAM.
- The authors state that fused selective scan layer has the same memory requirements as an optimized transformer implementation with FlashAttention.
- The authors describe synthetic tasks and state space models, including language, DNA, and audio modeling and generation as examples of their implementation.
- To make selective SSMs efficient on modern hardware (GPU) as well, the selection mechanism is designed to overcome the limitations of LTI models.
- The authors recognize that one of the core limitations of the usage of SSMs is their computational efficiency, which was why all derivatives used LTI (non-selective) models, most commonly in the form of global convolutions such as S4.
- The authors rely on three classical techniques: kernel fusion, parallel scan, and recomputation to address the sequential nature of recurrence, and the large memory usage in SSMs.
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Hackernoon
1w
79
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How Selection Mechanisms Transform State Space Models
- The paper discusses the transformation of State Space Models (SSMs) through selection mechanisms.
- The authors propose incorporating selection mechanisms into models by making parameters input-dependent.
- The selective SSMs improve model compression, efficiency, and performance on various tasks.
- The paper presents empirical evaluations and benchmarks to validate the effectiveness of selective SSMs.
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Hackernoon
1w
290
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Why Compressing Information Helps AI Work Better
- Compressing information helps AI work better.
- Sequence models with effective compression of context are more efficient.
- Efficiency and effectiveness tradeoff of sequence models is characterized by how well they compress their state.
- A fundamental principle for building sequence models is selectivity.
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Hackernoon
1w
395
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How State Space Models Improve AI Sequence Modeling Efficiency
- State Space Models (SSMs) have been widely used in AI sequence modeling for their ability to model temporal dependencies.
- This paper discusses how selective state space models can improve the efficiency of AI sequence modeling.
- The authors propose a selective mechanism that allows for better compression and more efficient implementation of SSMs.
- Empirical evaluations show that selective SSMs perform well in various tasks, including language modeling and DNA modeling.
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Hackernoon
1w
323
Image Credit: Hackernoon
Princeton and CMU Push AI Boundaries with the Mamba Sequence Model
- Princeton and CMU have developed a new class of selective state space models (SSMs) called Mamba sequence model that operates at subquadratic-time and is more efficient than earlier models for sequence modeling.
- Mamba is a fully recurrent model with properties that make it an appropriate backbone for general foundation models operating on sequences.
- The model is effective in language, audio, and genomics and can scale linearly in sequence length, offering fast training and inference, and allowing for long context.
- Mamba outperforms prior state-of-the-art models such as Transformers on modeling audio waveforms and DNA sequences, both in pretraining quality, and downstream metrics.
- Mamba is the first linear-time sequence model that truly achieves Transformer-quality performance, both in pretraining perplexity, and downstream evaluations.
- Performance improvements have been observed that reach up to 1 million-length sequences.
- The selective SSM's simplicity in filtering out irrelevant information allows the model to remember relevant information indefinitely.
- The hardware-aware algorithm that computes the model recurrently with a scan helps overcome the simple selection mechanism's technical challenge.
- Mamba language model has 5x generation throughput compared to Transformers of similar size and is designed to combine prior SSM architectures with MLP blocks of Transformers into a single homogenous architecture.
- The Mamba-3B model outperforms Transformers of similar size and matches Transformers twice its size, both in pretraining and downstream evaluation.
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Medium
1w
383
Image Credit: Medium
How to Use ChatGPT in Daily Life
- 1. Learning New Skills: ChatGPT can provide resources, tips, and learning paths for hobbies or skills.
- 2. Daily Planning: ChatGPT can help with time management, setting priorities, and creating to-do lists.
- 3. Personal Development: ChatGPT offers advice on mindset, goal-setting, and strategies for success.
- 4. Entertainment: ChatGPT can provide jokes, riddles, and short stories for light-hearted content.
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Medium
1w
30
Image Credit: Medium
Bridging Dimensions in Reinforcement Learning with Green’s, Stokes’, and Gauss’ Theorems
- The gap between local decisions and global consistency in current RL policies is unavoidable.
- Classical vector calculus, through Green’s, Stokes’, and Gauss’ Theorems, reveals the symmetries and constraints that govern fields across space and time.
- Green’s Theorem relates the circulation of a vector field to the divergence of the field, ensuring smooth and consistent flows.
- Gauss' Theorem provides a global consistency constraint, ensuring that the total outward flow of decisions is accounted for by the behavior within the enclosed volume.
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Marktechpost
1w
329
Meta AI Releases EvalGIM: A Machine Learning Library for Evaluating Generative Image Models
- Meta AI has developed a machine-based learning library to evaluate text-to-image generative models that includes support for various metrics, datasets and visualizations.
- The EvalGIM library also introduces a unique feature called “Evaluation Exercises,” which synthesizes performance insights to answer specific research questions.
- Researchers that collaborated on the project are based at Fair at Meta, Mila Quebec AI Institute, Univ. Grenoble Alpes Inria CNRS Grenoble INP, LJK France, McGill University, and Canada CIFAR AI chair.
- The library supports real-image datasets, including MS-COCO and GeoDE, offering insights into performance across geographic regions.
- Prompt-only datasets PartiPrompts and T2I-Compbench are included to test models across diverse text input scenarios and EvalGIM is compatible with popular tools such as HuggingFace diffusers.
- Multiple exercises that are structured around the evaluation process, such as the Trade-offs Exercise, examine how models balance quality, diversity and consistency over time.
- Researchers found that consistency metrics showed steady improvement during early training stages, but plateaued after about 450,000 iterations.
- The Evaluation Exercises also assessed geographic performance disparities using the GeoDE dataset, showing Southeast Asia and Europe benefited most from advancements in latent diffusion models.
- A ranking robustness exercise demonstrated how performance rankings varied depending on the metric and dataset.
- Combining original and recaptioned training data improved model performance across datasets.
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Marktechpost
1w
240
DL4Proteins Notebook Series Bridging Machine Learning and Protein Engineering: A Practical Guide to Deep Learning Tools for Protein Design
- DL4Proteins Notebook Series provides practical and hands-on resources integrating foundational machine learning concepts with advanced protein engineering methods for predicting and designing protein structures lines.
- The series offers accessible learning tools, ranging from neural networks to graph models, that enable researchers, educators, and students to apply deep learning techniques to protein design tasks lines.
- The notebooks include introductions to tools like AlphaFold, RFDiffusion, and ProteinMPNN aimed at fostering innovation in synthetic biology and therapeutics lines.
- Notebook 1 and Notebook 2 introduce the foundational concepts of neural networks using NumPy and PyTorch, respectively lines.
- Notebook 3 explains the foundational concepts of CNNs and demonstrates their application in handling image like data lines.
- Notebook 4 explores the use of LMs in understanding sequences such as text and proteins lines.
- Notebook 5 delves into the application of language model embeddings in solving real-world problems by repurposing embeddings generated from pre-trained language models lines.
- Notebook 6 introduces the use of GNNs in protein research, emphasizing their ability to model the complex relationships between amino acids in protein structures lines.
- Notebook 7 explores the application of diffusion models in protein structure prediction and design lines.
- Notebook 8 combines advanced tools like RFdiffusion, ProteinMPNN, and AlphaFold to guide users through the complete protein design process lines.
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Medium
1w
288
Image Credit: Medium
5 Best Artificial Intelligence Courses to Take on Udemy in 2025
- Artificial Intelligence is now not the next big thing but the current big thing and its most impacting programming and software development field.
- There are two main sets of programmers, first responsible for AI development and machine learning and the second category includes all other programmers who needs to know AI for making the best use of AI tools and technologies.
- This article lists some of the best AI courses on Udemy to learn AI from scratch. The first course, 'Artificial Intelligence A-Z : Build 7 AI + LLM & ChatGPT' is one of the best AI courses online.
- The course 'The Complete Artificial Intelligence and ChatGPT Course' teaches how to use Python and R for data science. It is suitable for both beginners and advanced learners and has an overall rating of 4.5 stars.
- 'Machine Learning A-Z™: Hands-On Python & R In Data Science', a course inclusive of over 40 hours of on-demand video and 178 downloadable resources, is suitable for both beginners and advanced learners and has an overall rating of 4.5 stars.
- 'Deep Learning A-Z™: Hands-On Artificial Neural Networks' a course suitable for both beginners and advanced learners and has an overall rating of 4.5 stars.
- 'Applied Machine Learning in Python' is another good course that teaches how to use machine learning for data analysis and prediction, and has an overall rating of 4.6 stars.
- Learning about AI is important not just to grow your career but also to survive in your current job. You can also use this opportunity to get yourself promoted or change your career.
- Udemy gives you options and its courses are very affordable. You can either buy courses individually which will cost around $10 each or get a Udemy Persona Plan which gives access of 10, 000 Udemy courses for just $30 a month.
- This list of best AI courses on Udemy has been created by AI experts who have worked in the field of AI, Machine Learning, and Deep Learning, and have experience teaching those technologies.
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