Towards AI

OCTOBER 3, 2024

Liquid Neural Networks: Research focuses on developing networks that can adapt continuously to changing data environments without catastrophic forgetting. These networks excel at processing time series data, making them suitable for applications like financial forecasting and climate modeling.

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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.

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IBM Journey to AI blog

JANUARY 10, 2024

The category of AI algorithms includes ML algorithms, which learn and make predictions and decisions without explicit programming. AI systems, particularly complex models like deep neural networks, can be hard to control and interpret. This process can prove unmanageable, if not impossible, for many organizations.

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The MLOps Blog

FEBRUARY 22, 2024

TL;DR: In many machine-learning projects, the model has to frequently be retrained to adapt to changing data or to personalize it. Continual learning is a set of approaches to train machine learning models incrementally, using data samples only once as they arrive. What is continual learning?

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Flipboard

FEBRUARY 6, 2023

The tool, not yet generally available, can “communicate” in natural language and collaborate with users on code changes, Steinberger claims — operating like a pair programmer that’s able to understand and continuously learn more about the context of both coding projects and developers.

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Flipboard

APRIL 17, 2023

We will put everything we learned so far into gradually building a multilayer perceptron (MLP) with PyTrees. We hope this post will be a valuable resource as you continue learning and exploring the world of JAX. In the context of a neural network, a PyTree can be used to represent the weights and biases of the network.

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Pickl AI

SEPTEMBER 23, 2024

Summary: This guide covers the most important Deep Learning interview questions, including foundational concepts, advanced techniques, and scenario-based inquiries. Gain insights into neural networks, optimisation methods, and troubleshooting tips to excel in Deep Learning interviews and showcase your expertise.

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Marktechpost

SEPTEMBER 19, 2024

Hong Kong Polytechnic University researchers use the Universal Approximation Theorem (UAT) to explain memory in LLMs. The UAT forms the basis of deep learning and explains memory in Transformer-based LLMs. UAT shows that neural networks can approximate any continuous function.

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Marktechpost

MARCH 3, 2025

An agentic AI is designed to autonomously plan, execute multi-step tasks, and continuously learn from feedback. Some agents may update their policies over time using reinforcement learning, but this learning is often isolated from real-time operation. In contrast, agentic AI systems are built to be adaptive.

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Heartbeat

NOVEMBER 27, 2023

Deep learning automates and improves medical picture analysis. Convolutional neural networks (CNNs) can learn complicated patterns and features from enormous datasets, emulating the human visual system. Convolutional Neural Networks (CNNs) Deep learning in medical image analysis relies on CNNs.

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Viso.ai

APRIL 29, 2024

STNs are used to “teach” neural networks how to perform spatial transformations on input data to improve spatial invariance. Spatial Transformer Networks Explained The central component of the STN is the spatial transformer module. TensorFlow is well-known for its versatility in designing custom layers.

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Viso.ai

MARCH 26, 2024

Uniquely, this model did not rely on conventional neural network architectures like convolutional or recurrent layers. without conventional neural networks. This represented a significant departure in how machine learning models process sequential data. Vaswani et al.

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Pickl AI

NOVEMBER 28, 2024

Understanding various Machine Learning algorithms is crucial for effective problem-solving. Continuous learning is essential to keep pace with advancements in Machine Learning technologies. Linear Algebra Linear algebra is fundamental for Machine Learning, especially in understanding how models process data.

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Unite.AI

JULY 31, 2023

The ultimate goal of the SEER model is to help in developing strategies for the pre-training process that use uncurated data to deliver top-notch state of the art performance in transfer learning. An algorithm that can learn the patterns from a large amount of images without any labels, annotations, or metadata.

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How to Learn Machine Learning

JUNE 9, 2024

It enables machines to recognize patterns in training data and learn without human assistance. What makes them fantastic is their ability to learn from their past interactions. This continuous learning enables the ML systems to improve their outcomes and make better predictions on new data over time.

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Snorkel AI

OCTOBER 4, 2023

Instead of the rule-based decision-making of traditional credit scoring, AI can continually learn and adapt, improving accuracy and efficiency. These large-scale neural networks are trained on vast amounts of data to address a wide number of tasks (i.e. Improve auditing, model explainability, and iteration.

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Chatbots Life

SEPTEMBER 19, 2024

In this guide, we’ll explain what chatbot machine learning is and provide an easy-to-follow approach to building your own chatbot for business purposes. What is Chatbot Machine Learning? Before we dive into how to build a chatbot, it’s important to understand what “ machine learning ” means in this context.

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Snorkel AI

OCTOBER 4, 2023

Instead of the rule-based decision-making of traditional credit scoring, AI can continually learn and adapt, improving accuracy and efficiency. These large-scale neural networks are trained on vast amounts of data to address a wide number of tasks (i.e. Improve auditing, model explainability, and iteration.

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Pickl AI

JUNE 9, 2024

Overcoming challenges through practical applications, continuous learning, and resource utilisation is key to success. Neural Network Architectures: Grasping the mathematical principles behind the structure and functioning of neural networks, including activation functions and backpropagation.

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Pickl AI

DECEMBER 16, 2024

Foundational techniques like decision trees, linear regression , and neural networks lay the groundwork for solving various problems. These languages provide access to powerful libraries and frameworks such as TensorFlow, PyTorch, and Scikit-learn, simplifying the implementation of Machine Learning models.

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PyImageSearch

DECEMBER 11, 2023

ML Study Jams: These were intensive 4-week learning opportunities, using Kaggle Courses to deepen the understanding of ML among participants. ML Paper Reading and Writing Clubs: To foster a culture of continuous learning and research, these clubs were introduced in various ML communities. Let's start with a simple example.

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Pickl AI

AUGUST 21, 2023

Step 3: Dive into Machine Learning and Deep Learning Master the realm of machine learning algorithms, from linear regression to neural networks. Understanding supervised and unsupervised learning techniques equip you to develop predictive models and uncover hidden patterns.

Data Scientist 52

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Snorkel AI

OCTOBER 4, 2023

Instead of the rule-based decision-making of traditional credit scoring, AI can continually learn and adapt, improving accuracy and efficiency. These large-scale neural networks are trained on vast amounts of data to address a wide number of tasks (i.e. Improve auditing, model explainability, and iteration.

Data Scientist 52

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Pickl AI

NOVEMBER 5, 2024

Explainable AI (XAI) is crucial for building trust in automated systems. These trends indicate a rapidly evolving field where continuous learning will be essential for professionals. Explainable AI (XAI) There is a growing demand for transparency in AI decision-making processes.

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Bugra Akyildiz

MAY 25, 2024

Model Selection and Tuning: ChatGPT could guide users through the process of selecting appropriate machine learning algorithms, tuning hyperparameters, and evaluating model performance using techniques like cross-validation or holdout sets. Are the internal representations in these systems also converging?

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Pickl AI

AUGUST 2, 2023

This humongous dataset was used to form a deep learning neural network […] modeled after the human brain—which allowed ChatGPT to learn patterns and relationships in the text data […] predicting what text should come next in any given sentence. How ChatGPT Works? How to Use ChatGPT for Free?

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The MLOps Blog

APRIL 20, 2023

While convolutional neural networks (CNNs) are commonly used in smaller-scale facial recognition systems, scaling to a larger number of faces requires a more sophisticated approach. ONNX : when working with different deep learning frameworks like PyTorch or TensorFlow, I often choose the Open Neural Network Exchange (ONNX) format.

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Heartbeat

JANUARY 3, 2024

Enter machine learning (ML) , the technological powerhouse that has revolutionized industries from healthcare to finance, with its unparalleled ability to analyze vast datasets, identify patterns, and make predictions. Can algorithms, neural networks, and data analytics offer tangible solutions to mitigate the climate crisis?

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Pickl AI

SEPTEMBER 18, 2024

Algorithm and Model Development Understanding various Machine Learning algorithms—such as regression , classification , clustering , and neural networks —is fundamental. By combining a robust academic background with technical expertise and strong soft skills, you can position yourself for success as a Machine Learning Engineer.

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Heartbeat

SEPTEMBER 25, 2023

One remarkable advancement of machine learning is the ability to analyze and interpret medical images with unparalleled accuracy and speed. Machine learning algorithms can continuously learn and update treatment models by incorporating data on how patients respond to different therapies.

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AI News

DECEMBER 18, 2023

Zheng first explained how over a decade working in digital marketing and e-commerce sparked her interest more recently in data analytics and artificial intelligence as machine learning has become hugely popular. They then analyse and assess risks to ensure compliance with regulations.

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