Announcement : Ai academy : deep learning
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Where to Get Data for Machine Learning and Deep Learning Model Creation – day 8
Where to Get Data for Machine Learning and Deep Learning Model Creation Where to Get Data for Machine Learning and Deep Learning Model Creation 1. […]
Fine-Tuning in Deep Learning with a practical example – day 6
Understanding Fine-Tuning in Deep Learning Understanding Fine-Tuning in Deep Learning: A Comprehensive Overview Fine-tuning in deep learning has become a powerful technique, allowing developers to […]
Mastering NLP: Unlocking the Math Behind It for Breakthrough Insights with a scientific paper study – day 71
What is NLP and the Math Behind It? Understanding Transformers and Deep Learning in NLP Introduction to NLP Natural Language Processing (NLP) is a crucial subfield of artificial intelligence (AI) that focuses on enabling machines to process and understand human language. Whether it’s machine translation, chatbots, or text analysis, NLP helps bridge the gap between human communication and machine understanding. But what’s behind NLP’s ability to understand and generate language? Underneath it all lies sophisticated mathematics and cutting-edge models like deep learning and transformers. This post will delve into the fundamentals of NLP, the mathematical principles that power it, and its connection to deep learning, focusing on the revolutionary impact of transformers. What is NLP? NLP is primarily about developing systems that allow machines to communicate with humans in their natural language. It encompasses two key areas: Natural Language Understanding (NLU): The goal here is to make machines comprehend and interpret human language. NLU allows systems to recognize the intent behind the text or speech, extracting key information such as emotions, entities, and actions. For instance, when you ask a voice assistant “What’s the weather like?”, NLU helps the system determine that the user is asking for weather information. Natural...
Understanding Recurrent Neural Networks (RNNs) – part 2 – Day 56
Understanding Recurrent Neural Networks (RNNs) Recurrent Neural Networks (RNNs) are a class of neural networks that excel in handling sequential data, such as time series, text, and speech. Unlike traditional feedforward networks, RNNs have the ability to retain information from previous inputs and use it to influence the current output, making them extremely powerful for tasks where the order of the input data matters. In day 55 article we have introduced RNN. In this article, we will explore the inner workings of RNNs, break down their key components, and understand how they process sequences of data through time. We’ll also dive into how they are trained using Backpropagation Through Time (BPTT) and explore different types of sequence processing architectures like Sequence-to-Sequence and Encoder-Decoder Networks. What is a Recurrent Neural Network (RNN)? At its core, an RNN is a type of neural network that introduces the concept of “memory” into the model. Each neuron in an RNN has a feedback loop that allows it to use both the current input and the previous output to make decisions. This creates a temporal dependency, enabling the network to learn from past information. Recurrent Neuron: The Foundation of RNNs A recurrent neuron processes sequences...
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 pre-trained models like GPT, BERT, and Llama for immediate integration. • PyTorch: Fine-tune models and convert them to Core ML for iOS deployment. Generative AI Models Generative AI models create unique content, including text, images, and audio, making them crucial for creative apps. Top Generative AI Models: • GANs (Generative Adversarial Networks): Generate photorealistic images, videos, and audio. • Llama with Multimodal Extensions: Handles both text and images efficiently, ideal for creative applications. • VAEs (Variational Autoencoders): Useful for reconstructing data and personalization. Example Use Cases: • Apps for generating digital art and music. • Tools for personalized content creation,...
DropOut and Monte Carlo Dropout (MC Dropout)- Day 48
Understanding Dropout in Neural Networks 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 a subset of those neurons during the training phase. When dropout is applied, each neuron has a probability \( p \) of being “dropped out” (i.e., set to zero). For instance, if \( p = 0.5 \), each neuron has a 50% chance of being dropped for a particular training iteration. Importantly, dropout does not remove neurons or weights permanently. Instead, it temporarily deactivates them during training, and they may be active again in future iterations. Let’s walk through a numerical example to see how dropout works in action and how weights are managed...
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 it learns not just the actual patterns in the training data but also the noise and irrelevant details. While this enables the model to perform well on training data, it results in poor performance on new, unseen data. The flexibility of neural networks, with their vast number of parameters, makes them highly prone to overfitting. This flexibility allows them to model very complex relationships in the data, but without precautions, they end up memorizing the training data instead of generalizing from it. Regularization is the key to addressing this challenge. L1 and L2 Regularization: The Mathematical Backbone L1 Regularization (Lasso...
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 training and smaller, more refined updates as the model approaches convergence. Formula: Where: is the learning rate at time step , is the initial learning rate, is the decay rate, controlling how fast the learning rate decreases, represents the current time step (or epoch). Mathematical Proof of Exponential Decay The core idea of exponential decay is that the learning rate decreases over time. Let’s prove that this results in convergence. The parameter update rule for gradient descent is: Substituting the exponentially decayed learning rate: As , the decay factor , meaning that the updates to become smaller and smaller, allowing...
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 are pushing the boundaries of deep learning with this method, solidifying its role as a key component in the training of modern AI models. How the 1Cycle Policy Works The 1Cycle policy deviates from conventional learning rate schedules by alternating between two distinct phases: Phase 1: Increasing Learning Rate – The learning rate starts low and steadily rises to a peak value (η_max). This phase promotes rapid exploration of the loss landscape, avoiding sharp local minima. Phase 2: Decreasing Learning Rate – Once the peak is reached, the learning rate gradually decreases to a very low value, enabling the model...
