Pickl AI

JANUARY 15, 2023

Explaining through examples of Deep Learning, you might find yourself searching for career prospects in the domain as well. Multi-layer Perceptron Within this type of network, there are more than 3 layers to classify data which is not linear. Having more than three layers, the networks connect effectively with every node.

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Pickl AI

AUGUST 14, 2024

Neural networks come in various forms, each designed for specific tasks: Feedforward Neural Networks (FNNs) : The simplest type, where connections between nodes do not form cycles. Models such as Long Short-Term Memory (LSTM) networks and Transformers (e.g., Data moves in one direction—from input to output.

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Mlearning.ai

DECEMBER 13, 2023

Explain the vanishing and exploding gradient problems. Vanishing Gradient: When training a neural network, sometimes the updates to the weights get too small. Difference: Traditional neural networks usually have a single goal, like classification or regression. Explain the concept of batch normalization.

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Pickl AI

SEPTEMBER 23, 2024

In image recognition, Convolutional Neural Networks (CNNs) can accurately identify objects and faces in images. Natural Language Processing (NLP) uses Deep Learning models to understand and generate human language, enabling applications like chatbots and translation. Explain the Concept of Forward Propagation.

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Pickl AI

JULY 14, 2023

Deep Learning, a subfield of ML, gained attention with the development of deep neural networks. Moreover, Deep Learning models, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), achieved remarkable breakthroughs in image classification, natural language processing, and other domains.

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Snorkel AI

MARCH 1, 2023

Chatbots and virtual assistants ChatGPT demonstrated that foundation models can serve as the seed for competent chat bots and virtual assistants that may help businesses provide customer support and answer common questions. This can make it challenging for businesses to explain or justify their decisions to customers or regulators.

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Viso.ai

MARCH 26, 2024

Arguably, one of the most pivotal breakthroughs is the application of Convolutional Neural Networks (CNNs) to financial processes. 7: Innovations in Customer Service and Experience AI chatbots and virtual assistants in finance may significantly enhance user experiences by providing quick, personalized, and efficient responses.

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Viso.ai

JULY 9, 2024

The difference between a generative vs. a discriminative problem explained. 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.

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Pickl AI

AUGUST 1, 2024

Here are some of the key applications of Deep Learning in healthcare: Medical Imaging Deep Learning algorithms, particularly convolutional neural networks (CNNs), excel at analysing medical images like X-rays, CT scans, and MRIs. This data can be used to detect early signs of health issues and provide personalised interventions.

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Pickl AI

NOVEMBER 13, 2024

It enables a neural network to learn by iteratively adjusting its weights to minimise errors. Key Components of Backpropagation The key mathematical elements of backpropagation help explain how neural networks learn. Let’s explore its key components and the step-by-step process.

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Mlearning.ai

JUNE 8, 2023

Vector Embeddings for Developers: The Basics | Pinecone Used geometry concept to explain what is vector, and how raw data is transformed to embedding using embedding model. Pinecone Used a picture of phrase vector to explain vector embedding. What are Vector Embeddings? using its Spectrogram ).

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ML Review

JUNE 4, 2018

These ideas also move in step with the explainability of results. If language grounding is achieved, then the network tells me how a decision was reached. In image captioning a network is not only required to classify objects, but instead to describe objects (including people and things) and their relations in a given image.

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Heartbeat

JANUARY 12, 2023

Other practical examples of deep learning include virtual assistants, chatbots, robotics, image restoration, NLP (Natural Language Processing), and so on. Convolution, pooling, and fully connected layers are just a few components that make up a convolutional neural network.

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