Towards AI

MAY 9, 2024

What’s AI Weekly Since I’ve been involved in building many LLM and RAG-based applications and courses, I wanted to find the best, cheapest, and easiest way possible to build an RAG chatbot hosted on Discord, which I use daily. Check out the video here to build your own chatbot! Meme of the week! It’s fast and accurate.

Convolutional Neural Networks 96

Convolutional Neural Networks Neural Network Large Language Models AI 96

Pickl AI

AUGUST 14, 2024

Summary: Neural networks are a key technique in Machine Learning, inspired by the human brain. Different types of neural networks, such as feedforward, convolutional, and recurrent networks, are designed for specific tasks like image recognition, Natural Language Processing, and sequence modelling.

Neural Network 52

Neural Network Machine Learning Convolutional Neural Networks Natural Language Processing 52

Mlearning.ai

MAY 30, 2023

Recurrent Neural Networks (RNNs) have become a potent tool for analysing sequential data in the large subject of artificial intelligence and machine learning. As we know that Convolutional Neural Network (CNN) is used for structured arrays of data such as image data. RNN is used for sequential data.

Neural Network 52

Neural Network Convolutional Neural Networks Natural Language Processing Machine Learning 52

Marktechpost

JULY 31, 2024

Learning TensorFlow enables you to create sophisticated neural networks for tasks like image recognition, natural language processing, and predictive analytics. The course includes videos, readings, quizzes, and programming assignments to deepen your understanding of NLP techniques and neural networks.

Neural Network 106

Neural Network Auto-classification Natural Language Processing Deep Learning 106

Pickl AI

NOVEMBER 13, 2024

Summary: Backpropagation in neural network optimises models by adjusting weights to reduce errors. Despite challenges like vanishing gradients, innovations like advanced optimisers and batch normalisation have improved their efficiency, enabling neural networks to solve complex problems.

Neural Network 52

Neural Network Algorithm Convolutional Neural Networks Natural Language Processing 52

Marktechpost

JUNE 27, 2023

Hallucination is the word used to describe the situation when AI algorithms and deep learning neural networks create results that are not real, do not match any data the algorithm has been trained on, or do not follow any other discernible pattern. They rely on massive visual training data in convolutional neural networks.

Artificial Intelligence 98

Artificial Intelligence Artificial Intelligence AI Chatbots Chatbots 98

Marktechpost

JANUARY 15, 2025

It employs artificial neural networks with multiple layershence the term deepto model intricate patterns in data. Each layer in a neural network extracts progressively abstract features from the data, enabling these models to understand and process complex patterns.

Deep Learning 85

Deep Learning Neural Network Convolutional Neural Networks Natural Language Processing 85

Marktechpost

MAY 28, 2024

This parallel processing capability allows Transformers to handle long-range dependencies more effectively than recurrent neural networks (RNNs) or convolutional neural networks (CNNs). Scaling Laws One of the key insights driving the development of the GPT series is understanding scaling laws in neural networks.

Neural Network 113

Neural Network Convolutional Neural Networks NLP Data Analysis 113

Chatbots Life

MAY 16, 2024

Famous Deep Learning Networks. Artificial Neural Network Convolutional Neural Network Artificial Neural Network It has an input layer, a hidden layer, and an output layer. In deep neural network input layers act as dendrites i.e Must Watch. What is Deep Learning?

Deep Learning 52

Deep Learning Convolutional Neural Networks Neural Network Machine Learning 52

AssemblyAI

SEPTEMBER 22, 2023

As for any diffusion model , Stable Audio adds noise to the audio vector, which a U-Net Convolutional Neural Network learns to remove, guided by the text and timing embeddings. A pre-trained CLAP transformer also generates text embeddings to represent musical characteristics like style, instrumentation, tempo, and mood.

Convolutional Neural Networks 246

Convolutional Neural Networks AI AI Data Scarcity 246

IBM Journey to AI blog

AUGUST 28, 2023

Machine learning algorithms like Naïve Bayes and support vector machines (SVM), and deep learning models like convolutional neural networks (CNN) are frequently used for text classification.

Data Mining 170

Data Mining Convolutional Neural Networks Categorization Machine Learning 170

Chatbots Life

APRIL 16, 2024

Variational Autoencoders (VAEs) : VAEs are neural networks that learn the underlying distribution of the input data and generate new data points. Generative Adversarial Networks (GANs) : GANs employ two neural networks : a generator that creates data and a discriminator that checks if it’s real.

Neural Network 59

Neural Network Convolutional Neural Networks Natural Language Processing Machine Learning 59

Heartbeat

MAY 30, 2023

What are Generative Adversarial Networks (GANs)? GANs are a type of neural network design used to develop new data samples that are similar to the training data. This machine learning subset uses artificially generated neural networks to model complex data relationships. Assume you’re teaching your pet new tricks.

Neural Network 72

Neural Network Convolutional Neural Networks Deep Learning Machine Learning 72

Marktechpost

JUNE 27, 2023

The system’s adaptability makes it useful in many contexts, including but not limited to customer care, virtual agents, and chatbots. Deep Convolutional Neural Networks (DCNN) trained on millions of photos power VanceAI’s A.I. ChatGPT can provide customers with a conversational A.I.

AI Tools 102

AI Tools Convolutional Neural Networks Artificial Intelligence Artificial Intelligence 102

Mlearning.ai

MAY 11, 2023

Indeed, this AI is a powerful natural language processing tool that can be used to generate human-like language, making it an ideal tool for creating chatbots, virtual assistants, and other applications that require natural language interactions. What lies behind GPT is a type of artificial intelligence (AI) called a neural network.

Neural Network 52

Neural Network Explainability Convolutional Neural Networks Natural Language Processing 52

Pickl AI

JANUARY 15, 2023

A subset of Machine Learning makes use of artificial neural networks and computer algorithms to imitate human learning. Deep Learning performs tasks to solve business problems by using neural networks that learn from various levels. Deep Learning uses neural networks which has multiple layers or nodes.

Deep Learning 52

Deep Learning Neural Network Convolutional Neural Networks Machine Learning 52

Topbots

OCTOBER 4, 2023

Image processing : Predictive image processing models, such as convolutional neural networks (CNNs), can classify images into predefined labels (e.g., These models are built by first adding noise to the image and then training the neural network to remove noise. a social media post or product description).

Generative AI 119

Generative AI Natural Language Processing Machine Learning Convolutional Neural Networks 119

Chatbots Life

OCTOBER 9, 2023

Researchers are using microwave imaging and convolutional neural networks for breast cancer screening with high accuracy in classifying profiles as healthy or diseased. ? was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Convolutional Neural Networks 52

Convolutional Neural Networks Neural Network Deep Learning Generative AI 52

Heartbeat

NOVEMBER 21, 2023

One of the most significant breakthroughs in this field is the convolutional neural network (CNN). In stark contrast, deep learning algorithms take a radically different approach, particularly convolutional neural networks (CNNs).

Computer Vision 52

Computer Vision Deep Learning NLP Neural Network 52

Viso.ai

JANUARY 24, 2025

Narrow AI chatbots, for instance, are very good at responding to pre-formulated queries, but they have trouble with intricate, open-ended discussions. Architecture of LeNet5 – Convolutional Neural Network – Source The capacity of AGI to generalize and adapt across a broad range of tasks and domains is one of its primary features.

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Convolutional Neural Networks Neural Network Deep Learning Large Language Models 59

Heartbeat

DECEMBER 8, 2023

Agents can be used for applications such as personal assistants, question answering, chatbots, querying tabular data, interacting with APIs, extraction, summarization, and evaluation. For example, an agent may need to interact with a chatbot platform, a customer relationship management (CRM) system, or a knowledge base.

Convolutional Neural Networks 52

Convolutional Neural Networks Neural Network Deep Learning LLM 52

How to Learn Machine Learning

AUGUST 15, 2024

Projects to Tackle for Your Machine Learning Portfolio To build a comprehensive portfolio, consider including projects from various domains and complexity levels: Image Classification : Develop a model to classify images using convolutional neural networks (CNNs).

Machine Learning 64

Machine Learning Convolutional Neural Networks Neural Network Artificial Intelligence 64

Viso.ai

FEBRUARY 25, 2023

Vision Transformer (ViT) have recently emerged as a competitive alternative to Convolutional Neural Networks (CNNs) that are currently state-of-the-art in different image recognition computer vision tasks. For example, the popular ChatGPT AI chatbot is a transformer-based language model.

Convolutional Neural Networks 52

Convolutional Neural Networks Computer Vision Neural Network Natural Language Processing 52

Pickl AI

AUGUST 1, 2024

Summary : Deep Learning engineers specialise in designing, developing, and implementing neural networks to solve complex problems. They work on complex problems that require advanced neural networks to analyse vast amounts of data. Hyperparameter Tuning: Adjusting model parameters to improve performance and accuracy.

Deep Learning 52

Deep Learning Neural Network Machine Learning Natural Language Processing 52

Pickl AI

SEPTEMBER 23, 2024

Gain insights into neural networks, optimisation methods, and troubleshooting tips to excel in Deep Learning interviews and showcase your expertise. Deep Learning is a subset of Machine Learning that focuses on using Artificial Neural Networks with multiple layers to model complex patterns in data. What is Deep Learning?

Deep Learning 52

Deep Learning Neural Network Natural Language Processing Convolutional Neural Networks 52

Viso.ai

JULY 9, 2024

As the name suggests, this technique involves transferring the learnings of one trained machine learning model to another, in the form of neural network weights. To understand how transfer learning works, it is essential to understand the architecture of Deep Neural Networks. Book a demo to learn more.

Neural Network 52

Neural Network BERT Deep Learning Computer Vision 52

Mlearning.ai

DECEMBER 13, 2023

Vanishing Gradient: When training a neural network, sometimes the updates to the weights get too small. What are generative adversarial networks (GANs), and how do they differ from traditional neural networks? GANs: These are two neural networks working against each other. How can they be mitigated?

Deep Learning 52

Deep Learning Neural Network Explainability Convolutional Neural Networks 52

Pickl AI

JULY 14, 2023

Machine Learning and Neural Networks (1990s-2000s): Machine Learning (ML) became a focal point, enabling systems to learn from data and improve performance without explicit programming. Techniques such as decision trees, support vector machines, and neural networks gained popularity.

Neural Network 52

Neural Network Software Engineer Artificial Intelligence Artificial Intelligence 52

Bugra Akyildiz

AUGUST 17, 2024

It often employs convolutional neural networks (CNNs) or vision transformers to capture spatial and semantic information from images. With HALVA, businesses can deploy chatbots and virtual assistants that deliver precise information, enhancing customer trust and engagement.

Convolutional Neural Networks 59

Convolutional Neural Networks Large Language Models Neural Network Machine Learning 59

Viso.ai

MARCH 26, 2024

Arguably, one of the most pivotal breakthroughs is the application of Convolutional Neural Networks (CNNs) to financial processes. Institutions widely use machine learning models like Random Forest, neural networks, and anomaly detection algorithms. 1: Fraud Detection and Prevention No.2:

Computer Vision 52

Computer Vision Neural Network Machine Learning Convolutional Neural Networks 52

Pickl AI

SEPTEMBER 18, 2023

Generative Adversarial Networks (GANs): GANs consist of two neural networks, a generator, and a discriminator, which compete with each other during training. Boltzmann Machines: Boltzmann Machines are a type of stochastic neural network with both visible and hidden units.

Generative AI 52

Generative AI Neural Network Artificial Intelligence Artificial Intelligence 52

Viso.ai

JULY 9, 2024

Discriminative models include a wide range of models, like Convolutional Neural Networks (CNNs), Deep Neural Networks (DNNs), Support Vector Machines (SVMs), or even simpler models like random forests. Autoencoders are a type of neural network that simply copies the input to the output.

Generative AI 52

Generative AI Neural Network Convolutional Neural Networks AI 52

Snorkel AI

MARCH 1, 2023

Model architectures that qualify as “supervised learning”—from traditional regression models to random forests to most neural networks—require labeled data for training. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks Radford et al. What are some examples of Foundation Models?

BERT 83

BERT Natural Language Processing Large Language Models NLP 83

Pragnakalp

JANUARY 1, 2024

Transformer models are a type of neural network architecture designed to process sequential material, such as sentences or time-series data. "} ] Response: The Transformer, a neural network architecture introduced in a 2017 paper by Ashish Vaswani et al.,

Neural Network 52

Neural Network Natural Language Processing Convolutional Neural Networks NLP 52

ML Review

JUNE 4, 2018

The first paper, to the best of our knowledge, to apply neural networks to the image captioning problem was Kiros et al. These new approaches generally; Feed the image into a Convolutional Neural Network (CNN) for encoding, and run this encoding into a decoder Recurrent Neural Network (RNN) to generate an output sentence.

Neural Network 52

Neural Network Convolutional Neural Networks Computer Vision Deep Learning 52

Pickl AI

SEPTEMBER 25, 2024

Chatbots and Virtual Assistants These AI-driven tools utilise Deep Learning to provide customer support through natural language conversations. Customer Support Automation Telecom companies implement chatbots powered by Deep Learning to handle customer inquiries efficiently without requiring human intervention for routine questions or issues.

Deep Learning 52

Deep Learning Algorithm Convolutional Neural Networks Neural Network 52

Mlearning.ai

JUNE 8, 2023

Images can be embedded using models such as convolutional neural networks (CNNs) , Examples of CNNs include VGG , and Inception. The Unreasonable Effectiveness Of Neural Network Embeddings An embedding is a low-dimensional vector representation that captures relationships in higher dimensional input data.

Machine Learning 52

Machine Learning BERT Neural Network Convolutional Neural Networks 52