Using Neural Networks for Tabular and Image Data: A Practical Guide Neural networks are versatile models that can learn patterns from a wide variety of data types. In this article, we explore how to feed tabular data and image data into neural networks for classification tasks, with practical examples in PyTorch. 1. Tabular Data: Features and Targets Tabular data is structured in rows and columns , where: Features: Input variables used by the model to learn patterns (e.g., age, salary, scores). Target: The output variable the model predicts (e.g., personality type, class label). Example: Personality Classification Suppose we have a synthetic dataset with 29 features representing various attributes of individuals and a target column personality_type . my_df = pd.read_csv('personality_synthet...

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Yes! You’ve got the concept mostly correct , but we can tighten the explanation and make it a bit more precise and clear. Here’s a clean way to describe it: How FastAPI Works 1. FastAPI is the application It’s where you define routes , API endpoints , and business logic . Think of it as the “brain” of your web service. 2. Uvicorn is the server Uvicorn is an ASGI server that listens for HTTP requests from clients (like browsers or mobile apps). It’s responsible for receiving requests and sending responses , but it does not know your business logic. 3. The request flow A client calls an API endpoint (e.g., GET /users or POST /predict ). Uvicorn receives the request and passes it to the FastAPI app. FastAPI processes the request: ...

  Bubble Sort is a sorting algorithm that arranges the elements of an array in ascending order (from lowest to highest). Conceptually, the algorithm works by repeatedly comparing adjacent elements in the array. If the current element is greater than the next one, the two elements are swapped. The process starts by comparing the first and second elements, then the second and third, and continues through the array. Each pass places the largest unsorted element in its correct position. This process is repeated until the entire array is sorted. To implement the Bubble Sort algorithm in a programming language, the following components are required: An array containing the values to be sorted. An inner loop that iterates through the array, comparing adjacent elements and swapping them if the first element is greater than the next. With each pass, this loop runs one fewer iteration because the largest elements are already in their correct positions. An outer loop that determines ...

Finding the PDF from a Given Graph When a probability density function (PDF) is given as a graph, the goal is to write its mathematical expression from the picture. Step 1: Identify the Support The support is the interval where the graph is above the x-axis. From the graph: -1 ≤ x ≤ 1 Outside this interval, the PDF is zero. Step 2: Identify Key Points Read the important coordinates from the graph: (-1, 0) (0, 1) (1, 0) The graph is piecewise linear, forming a triangle. Step 3: Find the Equation of Each Line Left side: from -1 to 0 Slope: m = (1 - 0) / (0 - (-1)) = 1 Equation of the line: f X (x) = x + 1    for -1 ≤ x ≤ 0 Right side: from 0 to 1 Slope: m = (0 - 1) / (1 - 0) = -1 ...

Why MSE Is Often Used in Wireless Communication Understanding MSE and MMSE In wireless communication systems, signals transmitted over the air are distorted by noise, multipath effects, and interference. To measure how well a receiver can recover the original transmitted signal, engineers frequently use Mean Squared Error (MSE) , which calculates the average of the squares of errors between estimated and true values. The Minimum Mean Square Error (MMSE) approach finds the estimator that minimizes this squared error.  Channel Estimation and Equalization In systems like MIMO and OFDM, receivers must estimate channel characteristics to undo the distortion caused by the propagation environment. Using pilot symbols or known training sequences, the receiver compares the observed signal with expected values and computes estimates of the channel. Minimizing the MS...

Optical Camera Communication (OCC) Optical Camera Communication (OCC) is a wireless communication method that uses light to send data and a camera (or image sensor) to receive it. Think of it as: Wi-Fi, but with light + a camera instead of radio waves + an antenna How OCC Works Transmitter: Light source (LED, screen, traffic light, car headlight) Light is modulated (on/off or intensity changes) Modulation is too fast for the human eye to notice Receiver: Camera or image sensor Captures...

RNN vs Transformer RNN (Recurrent Neural Network) — Word-by-Word Processing RNNs process text sequentially , one word at a time. At each step, the model reads the current word and updates a hidden memory state that carries information from all previous words. This means each new word depends on the previous words through that hidden state. :contentReference[oaicite:0]{index=0} RNNs are inherently sequential — the next step cannot start until the previous one finishes. :contentReference[oaicite:1]{index=1} Analogy: Imagine an assembly line where each worker only knows what the previous worker passed on — nothing else. Transformers — All Words at Once Using Self-Attention Instead of processing words one by one, Transformers look at the entire sentence simultaneously using a mechanism called ...