https://blog.sksoumik.com/tags/deep-learning/ Recent content in deep learning on Sadman Kabir Soumik Hugo -- gohugo.io en Copyright © 2022, Sadman Kabir Soumik Tue, 15 Nov 2022 00:00:00 +0000 https://blog.sksoumik.com/artificial-intelligence/frequently-asked-data-science-interview-qestions/ Tue, 15 Nov 2022 00:00:00 +0000 https://blog.sksoumik.com/artificial-intelligence/frequently-asked-data-science-interview-qestions/ Can you explain the bias-variance trade-off and how it relates to model performance? Machine learning and statistics have a fundamental concept that requires balancing the model's bias and variance, known as the bias-variance trade-off. These two types of errors can affect a model's performance. Bias, a type of error, occurs when a model makes assumptions about the data that are too simplistic. High bias means the model is too simple to capture the underlying patterns. https://blog.sksoumik.com/artificial-intelligence/machine_learning_research_vs_production/ Mon, 10 Jan 2022 00:00:00 +0000 https://blog.sksoumik.com/artificial-intelligence/machine_learning_research_vs_production/ There are several key differences between using machine learning for research and using it for production. One of the main differences is the focus of the work. Machine learning for research typically focuses on exploring new ideas and techniques, and on advancing the state of the art in the field. In contrast, machine learning for production focuses on building practical, real-world applications that can deliver value to organizations and individuals. https://blog.sksoumik.com/artificial-intelligence/writing-model-keras-vs-pytorch/ Thu, 09 Dec 2021 00:00:00 +0000 https://blog.sksoumik.com/artificial-intelligence/writing-model-keras-vs-pytorch/ PyTorch and Keras are both open-source deep learning frameworks, but they have some significant differences. PyTorch is a low-level framework that allows you to define your own computation graphs, while Keras is a high-level framework that provides a pre-defined set of layers and routines for building deep learning models. This means that PyTorch offers more flexibility and customization, while Keras is easier to use and more accessible to beginners. Choosing Framework - PyTorch vs. https://blog.sksoumik.com/artificial-intelligence/vanishing-gradient_problem/ Sun, 24 Oct 2021 00:00:00 +0000 https://blog.sksoumik.com/artificial-intelligence/vanishing-gradient_problem/ What is Vanishing Gradient Problem Neural networks are trained using stochastic gradient descent. This involves first calculating the prediction error made by the model and using the error to estimate a gradient used to update each weight in the network so that less error is made next time. This error gradient is propagated backward through the network from the output layer to the input layer. As the backpropagation algorithm advances downwards (or backward) from the output layer towards the input layer, the gradients often get smaller and smaller and approach zero, eventually leaving the weights of the initial or lower layers nearly unchanged. https://blog.sksoumik.com/artificial-intelligence/understanding-word-embeddings/ Fri, 11 Dec 2020 00:00:00 +0000 https://blog.sksoumik.com/artificial-intelligence/understanding-word-embeddings/ Word embedding is a technique in natural language processing (NLP) where words are represented as vectors of real numbers. This allows words with similar meanings to have similar representation, and can be used in various NLP tasks such as machine translation and text classification. There are several different techniques for word embedding in natural language processing (NLP), including: TF-IDF — Term Frequency-Inverse Document Frequency TF-IDF (Term Frequency-Inverse Document Frequency) is a technique used in natural language processing to measure the importance of a word in a document. https://blog.sksoumik.com/artificial-intelligence/understanding-recurrent-neural-networks/ Sun, 25 Oct 2020 00:00:00 +0000 https://blog.sksoumik.com/artificial-intelligence/understanding-recurrent-neural-networks/ Recurrent Neural Network A recurrent neural network (RNN), is a type of neural network that can process sequential data, like text, audio, or time series data. Here's how it works: first, the RNN takes in some input data, which could be a word in a sentence, a sound wave from an audio recording, or a measurement from a sensor at a specific time. Then, the RNN processes this input and generates an output, which could be a predicted next word in a sentence, a generated audio waveform, or a predicted sensor measurement. https://blog.sksoumik.com/artificial-intelligence/tips-to-avoid-overfitting-machine-learning/ Thu, 09 May 2019 00:00:00 +0000 https://blog.sksoumik.com/artificial-intelligence/tips-to-avoid-overfitting-machine-learning/ Very deep neural networks with a massive number of parameters are very robust machine learning systems. But, in this type of huge network, overfitting is a common serious problem. Learning how to deal with overfitting is essential to mastering machine learning. The fundamental issue in machine learning is the tension between optimization and generalization. Optimization refers to adjusting a model to get the best performance possible on the training data (the learning in machine learning). https://blog.sksoumik.com/artificial-intelligence/guide-to-transfer-learning-techniques/ Mon, 21 May 2018 00:00:00 +0000 https://blog.sksoumik.com/artificial-intelligence/guide-to-transfer-learning-techniques/ Transfer learning is a technique in machine learning that allows a model trained on one task to be reused and fine-tuned for another similar task. The idea behind transfer learning is that a model that has already learned to recognize patterns in one set of data can be applied to a different but related problem, allowing the model to learn faster and with less data than if it were trained from scratch.