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How to Use Learning Rate Annealing with Neural Networks?

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Learning rate is an important parameter in neural networks for that we often spend much time tuning it and we even don't get the optimum result even trying for some different rates. The learning rate annealing comes to the picture here, in which we have certain methods to traverse around different learning in various fashions to get optimal cost function. In this post, we are going to discuss what learning rate annealing and at the last, we practically see the difference between the standard and annealing process in neural networks. The major points to be discussed are listed below. Let's start the discussion by understanding the paradigm of learning rate.


Tabular Classification and Regression Made Easy with Lightning Flash

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Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. When it comes to articles on deep learning, advances in Computer Vision or Natural Language Processing (NLP) receive the lion's share of the attention.


Spoken digit recognition application on cAInvas

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The audio dataset used here is a subset of the Tensorflow speech commands dataset. Each sample is 1-second long mono audio recorded at 8000 Hz. The dataset is a balanced one with 2360 samples in each class. There are many ways to represent audio data, like, waveform, MFCCs, Mel spectrograms, spectrograms and many more. Among them all, the Mel scale is a closer representation of the human audio perception than the standard scale.


Training a Neural Network in PyTorch for a Computer Vision Task -- Person Re-Identification

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Neural networks are powerful constructs that mimic the functionality of the human brain to solve various problems that are difficult to be solved with deterministic algorithms. PyTorch is one of the best frameworks which can help us easily write and train neural networks in Python. Though neural networks are used to solve a variety of problems, we will focus on a computer vision problem called "person re-identification". It is somewhat similar to facial recognition, but we can use full-body images of people to identify them, You can read more about this in my blog linked below. In this blog, I have simplified the person re-id implementation at [1] so that the code is easy to understand even for beginners who are getting started with computer vision and deep neural networks.


#016 PyTorch - Three hacks for improving the performance of Deep Neural Networks: Transfer Learning, Data Augmentation, and Scheduling the Learning rate in PyTorch

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In this post, we are going to talk about very popular deep learning techniques that we can apply to speed up training and improve the performance of our deep learning model. You will learn how you can use transfer learning and some other popular methods like data augmentation and scheduling the learning rate. Transfer learning is an incredibly powerful technique where pre-trained models are used as the starting point on computer vision and natural language processing tasks. So in other words, a network trained for one task is adapted to another task. With transfer learning, you're likely to spend much less time in training.


How Parallelization and Large Batch Size Improve the Performance of Deep Neural Networks.

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Large Batch Size had till recently been viewed as a deterrent for good accuracy. However recent studies show that increasing the batch size can significantly reduce the training time while maintaining a considerable level of accuracy. In this blog, we draw on our inferences from four such technical papers. The RMSprop Warm-up phase is used to address the optimization difficulty at the start of the training. The update rule demonstrated below utilizes both the Stochastic Gradient Descent (SGD) along the RMSprop optimization algorithm.


Summary of VGG paper

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Architecture is a stack of convolution layers with filters of smallreceptive field: 3*3 or 1*1 in some cases 1*1 convolution layers can be treated as linear transformation followed by non-linear transformation. Stride is fixed to 1 px . Padding is such that size is maintained. After this 3 FC layers are used. All hidden layers used ReLU.


#017 PyTorch - How to apply Batch Normalization in PyTorch

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Today, we'll discuss another popular method used to improve the performance of your deep neural network called batch normalization. It is a technique for training deep neural networks that standardizes the inputs to a layer for each mini-batch. After finishing the theoretical part, we will explain how to implement batch normalization in Python using PyTorch. So, let's begin with our lecture. In order to understand batch normalization, first, we need to understand what data normalization is. Data normalization is the process of rescaling the input values in the training dataset to the interval of 0 to 1.


An overview on gradient descent and its variants

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The term "optimization" refers to the process of iteratively training a model to produce a maximum and minimum function evaluation to get a minimum cost function. It is crucial since it will assist us in obtaining a model with the least amount of error (as there will be discrepancies between the actual and predicted values). There are various optimization methods; in this article, we'll look at gradient descent and its three forms: batch, stochastic, and mini-batch. Note: Hyperparameter optimization is required to fine-tune the model. Before you begin training the model, you must first specify hyperparameters.


BetaBoosting

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At this point, we all know of XGBoost due to the massive success it has had in numerous Data Science competitions held on platforms like Kaggle. Along with its success, we have seen several variations such as CatBoost and LightGBM. All of these implementations are based on the Gradient Boosting algorithm developed by Friedman¹, which involves iteratively building an ensemble of weak learners (usually decision trees) where each subsequent learner is trained on the previous learner's errors. Let's take a look at some general pseudo-code for the algorithm from Elements of Statistical Learning²: However, this is not complete! A core mechanism which allows boosting to work is a shrinkage parameter that penalizes each learner at each boosting round that is commonly called the'learning rate'.