Announcement: ai academy : deep leaning app by us
We discuss the transformative world of deep learning and the AI Academy Deep Learning app, which simplifies complex AI concepts like neural networks, convolutional neural […]
Solo Developer’s Guide to Building Competitive Language Model Application – day 9
Solo Developer’s Guide to Building Competitive Language Model Applications A Solo Developer’s Guide to Building Competitive Language Model Applications With the explosion of large language […]
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...
Do you want to read a summery of what is BERT in 2 min read? (Bidirectional Encoder Representations from Transformers) – day 67
Transformer Models Comparison Transformer Models Comparison Feature BERT GPT BART DeepSeek Full Transformer Uses Encoder? ✅ Yes ❌ No ✅ Yes ❌ No ✅ Yes Uses Decoder? ❌ No ✅ Yes ✅ Yes ✅ Yes ✅ Yes Training Objective Masked Language Modeling (MLM) Autoregressive (Predict Next Word) Denoising Autoencoding Mixture-of-Experts (MoE) with Multi-head Latent Attention (MLA) Sequence-to-Sequence (Seq2Seq) Bidirectional? ✅ Yes ❌ No ✅ Yes (Encoder) ❌ No Can be both Application NLP tasks (classification, Q&A, search) Text generation (chatbots, summarization) Text generation and comprehension (summarization, translation) Advanced reasoning tasks (mathematics, coding) Machine translation, speech-to-text Understanding BERT: How It Works and Why It’s Transformative in NLP BERT (Bidirectional Encoder Representations from Transformers) is a foundational model in Natural Language Processing (NLP) that has reshaped how machines understand language. Developed by Google in 2018, BERT brought significant improvements in language understanding tasks by introducing a bidirectional transformer-based architecture that reads text in both directions (left-to-right and right-to-left). This blog post will dive deep into how BERT works, its architecture, pretraining strategies, and its applications, complemented by tables and figures for better comprehension. BERT’s Architecture At its core, BERT is based on the transformer architecture, specifically utilizing the encoder part of the...
Transformers in Deep Learning: Breakthroughs from ChatGPT to DeepSeek – Day 66
Transformer Models Comparison Transformer Models Comparison Feature BERT GPT BART DeepSeek Full Transformer Uses Encoder? ✅ Yes ❌ No ✅ Yes ❌ No ✅ Yes Uses Decoder? ❌ No ✅ Yes ✅ Yes ✅ Yes ✅ Yes Training Objective Masked Language Modeling (MLM) Autoregressive (Predict Next Word) Denoising Autoencoding Mixture-of-Experts (MoE) with Multi-head Latent Attention (MLA) Sequence-to-Sequence (Seq2Seq) Bidirectional? ✅ Yes ❌ No ✅ Yes (Encoder) ❌ No Can be both Application NLP tasks (classification, Q&A, search) Text generation (chatbots, summarization) Text generation and comprehension (summarization, translation) Advanced reasoning tasks (mathematics, coding) Machine translation, speech-to-text Table 1: Comparison of Transformers, RNNs, and CNNs Feature Transformers RNNs CNNs Processing Mode Parallel Sequential Localized (convolution) Handles Long Dependencies Efficient Struggles with long sequences Limited in handling long dependencies Training Speed Fast (parallel) Slow (sequential) Medium speed due to parallel convolution Key Component Attention Mechanism Recurrence (LSTM/GRU) Convolutions Number of Layers 6–24 layers per encoder/decoder 1-2 (or more for LSTMs/GRUs) Typically 5-10 layers Backpropagation Through attention and feed-forward layers Backpropagation Through Time (BPTT) Standard backpropagation Self-Attention Mechanism The self-attention mechanism allows each word in a sequence to attend to every other word, capturing relationships between distant parts of the input. This mechanism is...
Iterative Forecasting which is Predicting One Step at a Time 2- Direct Multi-Step Forecasting with RNN 3- Seq2Seq Models for Time Series Forecasting – day 61
Mastering Time Series Forecasting with RNNs and Seq2Seq Models: Detailed Iterations with Calculations, Tables, and Method-Specific Features Time series forecasting is a crucial task in various domains such as finance, weather prediction, and energy management. Recurrent Neural Networks (RNNs) and Sequence-to-Sequence (Seq2Seq) models are powerful tools for handling sequential data. In this guide, we will provide step-by-step calculations, including forward passes, loss computations, and backpropagation for two iterations across three forecasting methods: Iterative Forecasting: Predicting One Step at a Time Direct Multi-Step Forecasting with RNN Seq2Seq Models for Time Series Forecasting Assumptions and Initial Parameters For consistency across all methods, we’ll use the following initial parameters: Input Sequence: Desired Outputs: For Iterative Forecasting and Seq2Seq: For Direct Multi-Step Forecasting: Initial Weights and Biases: Weights: (hidden-to-hidden weight) (input-to-hidden weight) will vary per method to accommodate output dimensions. Biases: Activation Function: Hyperbolic tangent () Learning Rate: Initial Hidden State: 1. Iterative Forecasting: Predicting One Step at a Time In iterative forecasting, the model predicts one time step ahead and uses that prediction as an input to predict the next step during inference. Key Feature: During training, we use actual data to prevent error accumulation, but during inference, predictions are fed back into...
Step-by-Step Explanation of RNN for Time Series Forecasting – part 6 – day 60
RNN Time Series Forecasting Step-by-Step Explanation of RNN for Time Series Forecasting Step 1: Simple RNN for Univariate Time Series Forecasting Explanation: An RNN processes sequences of data, where the output at any time step depends on both the current input and the hidden state (which stores information about previous inputs). In this case, we use a Simple RNN with only one recurrent neuron. TensorFlow Code: Numerical Example: Let’s say we have a sequence of three time steps: . 1. Input and Hidden State Initialization: The RNN starts with an initial hidden state , typically initialized to 0. Each step processes the input and updates the hidden state: where: is the weight for the hidden state. is the weight for the input. is the bias term. is the activation function (hyperbolic tangent). Assume: Let’s calculate the hidden state updates for each time step: Time Step 1: Time Step 2: Time Step 3: Thus, the final output of the RNN for the sequence is . PyTorch Equivalent Code: — Step 2: Understanding the Sequential Process of the RNN Explanation: At each time step, the RNN processes the input by updating the hidden state based on both the current input and the...
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...
