RNN Deep Learning – Part 1 – Day 55

Understanding Recurrent Neural Networks (RNNs) and CNNs for Sequence Processing Introduction In the world of deep learning, neural networks have become indispensable, especially for handling tasks involving sequential data, such as time series, speech, and text. Among the most popular architectures for such data are Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs). Although RNNs are traditionally associated with sequence processing, CNNs have also been adapted to perform well in this area. This blog will take a detailed look at how these networks work, their differences, their challenges, and their real-world applications.  Unrolling RNNs: How RNNs Process Sequences One...

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Deep Learning Models integration for iOS Apps – briefly explained – Day 52

Key Deep Learning Models for iOS Apps Natural Language Processing (NLP) Models NLP models enable apps to understand and generate human-like text, supporting features like chatbots, sentiment analysis, and real-time translation. Top NLP Models for iOS: • Transformers (e.g., GPT, BERT, T5): Powerful for text generation, summarization, and answering queries. • Llama: A lightweight, open-source alternative to GPT, ideal for mobile apps due to its resource efficiency. Example Use Cases: • Building chatbots with real-time conversational capabilities. • Developing sentiment analysis tools for analyzing customer feedback. • Designing language translation apps for global users. Integration Tools: • Hugging Face: Access...

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41

DropOut and Monte Carlo Dropout (MC Dropout)- Day 48

Understanding Dropout in Neural Networks with a Real Numerical Example In deep learning, overfitting is a common problem where a model performs extremely well on training data but fails to generalize to unseen data. One popular solution is dropout, which randomly deactivates neurons during training, making the model more robust. In this section, we will demonstrate dropout with a simple example using numbers and explain how dropout manages weights during training. What is Dropout? Dropout is a regularization technique used in neural networks to prevent overfitting. In a neural network, neurons are connected between layers, and dropout randomly turns off...

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55

Understanding Regularization in Deep Learning – Day 47

Understanding Regularization in Deep Learning – A Mathematical and Practical Approach Introduction One of the most compelling challenges in machine learning, particularly with deep learning models, is overfitting. This occurs when a model performs exceptionally well on the training data but fails to generalize to unseen data. Regularization offers solutions to this issue by controlling the complexity of the model and preventing it from overfitting. In this post, we’ll explore the different types of regularization techniques—L1, L2, and dropout—diving into their mathematical foundations and practical implementations. What is Overfitting? In machine learning, a model is said to be overfitting when...

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46

Learning Rate – 1-Cycle Scheduling, exponential decay and Cyclic Exponential Decay (CED) – Part 4 – Day 45

Advanced Learning Rate Scheduling Methods for Machine Learning: Learning rate scheduling is critical in optimizing machine learning models, helping them converge faster and avoid pitfalls such as getting stuck in local minima. So far in our pervious days articles we have explained a lot about optimizers, learning rate schedules, etc. In this guide, we explore three key learning rate schedules: Exponential Decay, Cyclic Exponential Decay (CED), and 1-Cycle Scheduling, providing mathematical proofs, code implementations, and theory behind each method. 1. Exponential Decay Learning Rate Exponential Decay reduces the learning rate by a factor of , allowing larger updates early in...

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84
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Theory Behind 1Cycle Learning Rate Scheduling & Learning Rate Schedules – Day 43

  The 1Cycle Learning Rate Policy: Accelerating Model Training  In our pervious article  (day 42) , we have explained The Power of Learning Rates in Deep Learning and Why Schedules Matter, lets now focus on 1Cycle Learning Rate to explain it  in more detail :  The 1Cycle Learning Rate Policy, first introduced by Leslie Smith in 2018, remains one of the most effective techniques for optimizing model training. By 2025, it continues to prove its efficiency, accelerating convergence by up to 10x compared to traditional learning rate schedules, such as constant or exponentially decaying rates. Today, both researchers and practitioners...

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98

The Power of Learning Rates in Deep Learning and Why Schedules Matter – Day 42

The Power of Learning Rates in Deep Learning and Why Schedules Matter In deep learning, one of the most critical yet often overlooked hyperparameters is the learning rate. It dictates how quickly a model updates its parameters during training, and finding the right learning rate can make the difference between a highly effective model and one that never converges. This post delves into the intricacies of learning rates, their sensitivity, and how to fine-tune training using learning rate schedules. Why is Learning Rate Important? The learning rate controls the size of the step the optimizer takes when adjusting model parameters...

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147

Adam vs SGD vs AdaGrad vs RMSprop vs AdamW – Day 39

Choosing the Best Optimizer for Your Deep Learning Model When training deep learning models, choosing the right optimization algorithm can significantly impact your model’s performance, convergence speed, and generalization ability. Below, we will explore some of the most popular optimization algorithms, their strengths, the reasons they were invented, and the types of problems they are best suited for. 1. Stochastic Gradient Descent (SGD) Why It Was Invented SGD is one of the earliest and most fundamental optimization algorithms used in machine learning and deep learning. It was invented to handle the challenge of minimizing cost functions efficiently, particularly when dealing...

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AdaGrad vs RMSProp vs Adam: Why Adam is the Most Popular? – Day 38

A Comprehensive Guide to Optimization Algorithms: AdaGrad, RMSProp, and Adam In the realm of machine learning, selecting the right optimization algorithm can significantly impact the performance and efficiency of your models. Among the various options available, AdaGrad, RMSProp, and Adam are some of the most widely used optimization algorithms. Each of these algorithms has its own strengths and weaknesses. In this article, we’ll explore why AdaGrad ( which we explained fully on day 37 ) might not always be the best choice and how RMSProp & Adam could address some of its shortcomings. AdaGrad: Why It’s Not Always the Best...

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261

Nag as optimiser in deep learning – day 36

Nesterov Accelerated Gradient (NAG): A Comprehensive Overview Introduction to Nesterov Accelerated Gradient Nesterov Accelerated Gradient (NAG), also known as Nesterov Momentum, is an advanced optimization technique introduced by Yurii Nesterov in the early 1980s. It is an enhancement of the traditional momentum-based optimization used in gradient descent, designed to accelerate the convergence rate of the optimization process, particularly in the context of deep learning and complex optimization problems. How NAG Works The core idea behind NAG is the introduction of a “look-ahead” step before calculating the gradient, which allows for a more accurate and responsive update of parameters. In traditional...

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