A naukri.com initiative
Neural Networks News
Analyticsinsight
4w
57
Image Credit: Analyticsinsight
Why is Reading Deep-Learning Books Essential?
- Reading deep learning books is essential for unlocking the power of deep learning and understanding its fundamentals, structure, and mathematical concepts.
- It provides in-depth knowledge of various deep learning algorithms such as CNNs, RNNs, and GANs, along with insights into their workings and limitations.
- Books often contain practical examples, code snippets, and exercises to enhance hands-on skills in applying deep learning to solve real-world tasks.
- By staying updated with the latest developments and best practices, reading deep learning books can contribute to career development in fields like data science, machine learning, and artificial intelligence.
Read Full Article
3 Likes
Medium
4w
381
Image Credit: Medium
DeepMind’s RecurrentGemma Pioneering Efficiency for Open Small Language Models
- DeepMind introduces RecurrentGemma, an open language model built on Google's Griffin architecture.
- RecurrentGemma reduces memory usage and enables efficient inference on lengthy sequences.
- The architecture of RecurrentGemma incorporates linear recurrences and local attention instead of global attention.
- RecurrentGemma-2B achieves comparable performance to Gemma while improving throughput during inference.
Read Full Article
22 Likes
Medium
4w
0
Image Credit: Medium
Recurrent memory removed restrictions on the length of input sequences for transformers
- Researchers have found a way to remove restrictions on the length of input sequences for transformers.
- By adding memory and recurrence to standard transformer architectures, the researchers created a Recurrent Memory Transformer (RMT).
- RMT allows for processing long input sequences with satisfactory neural network performance accuracy.
- In experiments, the researchers extended the input sequence length to 2 million tokens.
Read Full Article
Like
Medium
4w
98
Image Credit: Medium
Get Started With Neural Networks Practically Using TensorFlow;
- The first chapter of the book 'AI and ML for Coders' by Laurence Moroney provides a beginner-level understanding of neural networks using TensorFlow.
- Using TensorFlow in Google Colab, the article demonstrates the creation of a neural network with one neuron to find a specific pattern.
- The article explains the concept of Sequential and Dense layers in neural networks, and how they are connected.
- An optimizer (Stochastic Gradient Descent) and a loss function (mean squared error) are used to train the model and optimize the pattern.
Read Full Article
5 Likes
Medium
4w
98
Image Credit: Medium
Unveiling the Black Box: Meta’s LM Transparency Tool Deciphers Transformer Language Models
- Meta, University College London, and Universitat Politècnica de Catalunya have unveiled the LM Transparency Tool (LM-TT).
- LM-TT is an open-source interactive toolkit designed for analyzing Transformer-based language models.
- The tool offers granular examination and identifies relevant model components for a given prediction.
- LM-TT provides visualizations, attribution of changes, and interpretation of attention heads and feed-forward neurons.
Read Full Article
5 Likes
Ibm
4w
200
Image Credit: Ibm
IBM and TechD partner to securely share data and power insights with gen AI
- IBM and TechD have partnered to securely share data and power insights with generative AI (gen AI).
- The partnership aims to deliver scalable solutions for data management, conversational interface, and natural language processing.
- The comprehensive solution combines IBM Db2, IBM watsonx Assistant, and NeuralSeek for efficient data management and enhanced accessibility.
- The integration of these technologies enables organizations to develop intelligent conversational interfaces that understand and respond to user inquiries.
Read Full Article
12 Likes
Semiengineering
4w
272
Image Credit: Semiengineering
In-Memory Computing: Techniques for Error Detection and Correction
- A new technical paper titled “Error Detection and Correction Codes for Safe In-Memory Computations” was published.
- The paper investigates an architectural-level mitigation technique based on multiple checksum codes to detect and correct errors at run-time in In-Memory Computing (IMC).
- The implementation demonstrates higher efficiency in recovering accuracy compared to traditional methods such as Triple Modular Redundancy (TMR).
- The results show that the implementation recovers more than 91% of the original accuracy with less area and latency overhead.
Read Full Article
16 Likes
Towards Data Science
4w
71
Image Credit: Towards Data Science
AI Mapping: Using Neural Networks to Identify House Numbers
- This article discusses the use of Artificial Neural Networks and Convolutional Neural Networks to predict house numbers.
- The author provides examples of models using ANN and CNN and compares their performance on the SVHN dataset.
- The dataset consists of images from Google Street View, containing partial digits next to the identified digit.
- The images are preprocessed by flattening them into 1D arrays and normalizing them.
Read Full Article
4 Likes
Medium
4w
263
Image Credit: Medium
OPPO AI’s Transformer-Lite Delivers 10x+ Prefill and 2~3x Decoding Boost on Mobile Phone GPUs
- Researchers from OPPO AI Center introduce Transformer-Lite, a mobile inference engine for large language models.
- Four optimization techniques are proposed to streamline LLM deployment on device GPUs.
- Transformer-Lite achieves over 10x acceleration for prefill speed and 2~3x for decoding speed.
- The engine supports ONNX models and demonstrates superior performance compared to CPU and GPU-based alternatives.
Read Full Article
15 Likes
Medium
4w
138
Image Credit: Medium
Bike Curious — Neural Network Regression with TensorFlow
- The author uses Google's Colab notebook to perform neural network regression with TensorFlow.
- The author loads and inspects the first 3 rows of raw data from Kaggle using the pandas python library.
- The author preprocesses the data, drops irrelevant columns, and defines the output data as the total count of rented bikes per day.
- The author applies one-hot encoding to categorical data, splits the data into training and testing sets, and creates a deep learning model to predict bike rentals.
Read Full Article
8 Likes
Medium
4w
241
Image Credit: Medium
Exploring Essential Data Science Algorithms: From Regression to Neural Networks
- Decision Trees: Hierarchical tree-like structures that partition the feature space, versatile for classification, regression, and ensemble methods.
- Support Vector Machines (SVM): Powerful models for classification and regression tasks, maximizing margin between classes.
- K-Nearest Neighbors (KNN): Simple yet effective algorithm based on proximity for classification and regression tasks.
- Neural Networks: Revolutionizing AI and ML, learning complex patterns through interconnected layers of neurons.
Read Full Article
14 Likes
Medium
4w
31
Image Credit: Medium
Machine & Deep Learning
- The transfer of information in the human brain and artificial neural networks is crucial for behavior and learning.
- Hebbian Learning is an algorithm used in neural networks that strengthens synapses when activated together, helping create memories and pattern recognition.
- Competitive Learning involves neurons competing to become active, aiding in feature detection and decision making.
- Human neural networks and artificial neural networks share similarities in functionality and serve as the foundation for various applications.
Read Full Article
1 Like
Medium
1M
103
Image Credit: Medium
Deep Learning is Regression
- Neural networks are considered the cornerstone of machine learning and AI.
- Underneath the terminology, neural networks are built upon regression models.
- Linear regression is the basis for simple neural network models.
- Neural networks are complex grids of regression models that accurately represent complex problems.
Read Full Article
6 Likes
Hackernoon
1M
44
Image Credit: Hackernoon
The Noonification: UX Considerations for Better Multi-Factor Authentication (4/12/2024)
- 100 Days of AI, Day 20: Using Midjourney for Stock Photos in Your Side Project
- Unveiling the Limits of Learned Local Search Heuristics: Are You the Mightiest of the Meek?
- A Dozen (or so) Learnings From 15 Years of Software Incident Management
- How to Implement the Idempotency-Key Specification on Apache APISIX
- UX Considerations for Better Multi-Factor Authentication
Read Full Article
2 Likes
Hackernoon
1M
287
Image Credit: Hackernoon
Comparative Analysis: Learned Heuristics vs. WalkSAT in SAT Problem Solving
- This research paper presents a comparative analysis of learned heuristics and the WalkSAT algorithm in solving SAT problems.
- The authors use publicly available implementations of ECO-DQN and GNNSAT for their experiments.
- They analyze the generalization and performance of agents trained on different graph sizes and structures.
- The paper also evaluates the performance of learned heuristics and WalkSAT using various metrics.
Read Full Article
17 Likes
For uninterrupted reading, download the app