small update for MLX user of apple – M4 Chips is coming
Apple Releases MacBook Pro with M4 Chip in 2024 Apple Announces New MacBook Pro with M4 Chip Posted on October 4, 2024 by Admin What […]
UNLOCKING RNN, Layer Normalization, and LSTMs – Mastering the Depth of RNNs in Deep Learning – Part 8 of RNN Series by INGOAMPT – Day 62
A Deep Dive into Recurrent Neural Networks, Layer Normalization, and LSTMs So far we have explained in pervious days articles a lot about RNN. We have explained, Recurrent Neural Networks (RNNs) are a cornerstone in handling sequential data, ranging from time series analysis to natural language processing. However, training RNNs comes with challenges, particularly when dealing with long sequences and issues like unstable gradients. This post will cover how Layer Normalization (LN) addresses these challenges and how Long Short-Term Memory (LSTM) networks provide a more robust solution to memory retention in sequence models. The Challenges of RNNs: Long Sequences and Unstable Gradients When training an RNN over long sequences, the network can experience the unstable gradient problem—where gradients either explode or vanish during backpropagation. This makes training unstable and inefficient. Additionally, RNNs may start to “forget” earlier inputs as they move forward through the sequence, leading to poor retention of important data points, a phenomenon referred to as the short-term memory problem. Addressing Unstable Gradients: Gradient Clipping: Limits the maximum value of gradients, ensuring they don’t grow excessively large. Smaller Learning Rates: Using a smaller learning rate helps prevent gradients from overshooting during updates. Activation Functions: Saturating activation functions like...
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...
To learn what is RNN (Recurrent Neural Networks ) why not understand ARIMA, SARIMA first ? – RNN Learning – Part 5 – day 59
ARIMA, SARIMA, and Their Relationship with Deep Learning for Time Series Forecasting A Deep Dive into ARIMA, SARIMA, and Their Relationship with Deep Learning for Time Series Forecasting In recent years, deep learning has become a dominant force in many areas of data analysis, and time series forecasting is no exception. Traditional models like ARIMA (Autoregressive Integrated Moving Average) and its seasonal extension SARIMA have long been the go-to solutions for forecasting time-dependent data. However, newer models based on Recurrent Neural Networks (RNNs), particularly Long Short-Term Memory (LSTM) networks, have emerged as powerful alternatives. Both approaches have their strengths and applications, and understanding their relationship helps in choosing the right tool for the right problem. In this blog post, we’ll explore ARIMA and SARIMA models in detail, discuss how they compare to deep learning-based models like RNNs, and demonstrate their practical implementation. Deep Learning and Time Series Forecasting Deep learning is a subset of machine learning where models learn hierarchical features from data using multiple layers of neural networks. When it comes to time series forecasting, one of the most common deep learning architectures used is Recurrent Neural Networks (RNNs). RNNs are particularly well-suited for time series because they are...
Understanding RNNs: Why Not compare it with FNN to Understand the Math Behind it Better? – DAY 58
In this article we try to show an example of FNN and for RNN TO understand the math behind it better by comparing to each other: Neural Networks Example Example Setup Input for FNN: Target Output for FNN: RNNs are tailored for sequential data because they are designed to remember and utilize information from previous inputs in a sequence, allowing them to capture temporal relationships and context effectively. This characteristic differentiates RNNs from other neural network types that are not inherently sequence-aware., Input for RNN (Sequence): Target Output for RNN (Sequence): Learning Rate: 1. Feedforward Neural Network (FNN) Structure Input Layer: 1 neuron Hidden Layer: 1 neuron Output Layer: 1 neuron Weights and Biases Initial Weights: (Input to Hidden weight) (Hidden to Output weight) Biases: (Hidden layer bias) (Output layer bias) Step-by-Step Calculation for FNN Step 1: Forward Pass Hidden Layer Output: Output: Step 2: Loss Calculation Using Mean Squared Error (MSE): Step 3: Backward Pass Gradient of Loss with respect to Output: Gradient of Output with respect to Hidden Layer: Gradient of Hidden Layer Output with respect to Weights: Assuming : Step 4: Weight Update Update Output Weight: Update Input Weight: 2. Recurrent Neural Network (RNN) Structure Input...
Time Series Forecasting with Recurrent Neural Networks (RNNs) – part 3 – day 57
Time Series Forecasting with Recurrent Neural Networks (RNNs): A Complete Guide Introduction Time series data is all around us: from stock prices and weather patterns to daily ridership on public transport systems. Accurately forecasting future values in a time series is a challenging task, but Recurrent Neural Networks (RNNs) have proven to be highly effective at this. In this article, we will explore how RNNs can be applied to time series forecasting, explain the key concepts behind them, and demonstrate how to clean, prepare, and visualize time series data before feeding it into an RNN. What Is a Recurrent Neural Network (RNN)? A Recurrent Neural Network (RNN) is a type of neural network specifically designed for sequential data, such as time series, where the order of inputs matters. Unlike traditional feed-forward neural networks, RNNs have loops that allow them to carry information from previous inputs to future inputs. This makes them highly suitable for tasks where temporal dependencies are critical, such as language modeling or time series forecasting. How RNNs Learn: Backpropagation Through Time (BPTT) Understanding BPTT In a traditional feed-forward neural network, backpropagation is used to calculate how much each weight contributes to the error at each layer. In...
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...
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 of the most important concepts in understanding RNNs is unrolling. Unlike feedforward neural networks, which process inputs independently, RNNs have a “memory” that allows them to keep track of previous inputs by maintaining hidden states. Unrolling in Time At each time step \( t \), an RNN processes both: The current input \( x(t) \) The hidden state \( h(t-1) \), which contains information from the previous steps The RNN essentially performs the same task repeatedly at each step, but it does so by incorporating past data (via the hidden state), making it ideal for sequence data. Time Step Input...
Mastering the Mathematics Behind CNN or Convolutional Neural Networks in Deep Learning – Day 54
You have seen what’s CNN in our previous article:View Article.Now let’s check the mathematics behind in details & step by step with a very simple example. Part 1: Input Layer, Convolution, and Pooling (Steps 1-4) Step 1: Input Layer We are processing two 3×3 grayscale images—one representing a zebra and one representing a cat. Image 1: Zebra Image (e.g., with stripe-like patterns) Image 2: Cat Image (e.g., with smoother, fur-like textures) These images are represented as 2D grids of pixel values, with each value between 0 and 1 indicating pixel intensity. Step 2: Convolutional Layer (Feature Extraction) We’ll apply a 3×3 convolutional filter to detect patterns such as edges. For simplicity, we’ll use the same filter for both images. Convolution Filter (Edge Detector): Convolution on the Zebra Image For the first patch (the full 3×3 grid), the element-wise multiplication with the filter is: Summing the values: The feature map value for this part of the zebra image is 0.7. Convolution on the Cat Image Now, let’s perform the convolution on the cat image. Summing the values: The feature map value for this part of the cat image is -0.3. Step 3: ReLU Activation (Non-Linearity) The ReLU activation function converts...
