Deep Learning | Video 4 | Part 2 | Model Building for Word Prediction | Venkat Reddy AI Classes

Course Materials https://github.com/venkatareddykonasani/Youtube_videos_Material To keep up with the latest updates, join our WhatsApp community: https://chat.whatsapp.com/GidY7xFaFtkJg5OqN2X52k In this video, we dive into the process of building a sequential artificial intelligence (AI) model for word prediction. We start by explaining the overall approach and then delve into the code implementation. To solve the problem of predicting words using machine learning, we can't directly use words as input. Instead, we convert words into numeric representations. We begin by creating two dictionaries: Word to Number Dictionary: This maps words to unique numbers, enabling mathematical analysis. Number to Word Dictionary: This reverses the mapping, allowing us to convert numeric predictions back into words. To handle a dataset of 139 unique words, we utilize a technique called one-hot encoding. Each word becomes a column with a value of 1 or 0, indicating its presence or absence in a given context. In the code, we demonstrate how to prepare the data (X1 and X2) and set up the architecture of our first neural network (Ann1). Ann1 takes word one (X1) as input and predicts word two (Y1). Next, we introduce Ann2, which leverages the partial output from Ann1 to make more accurate predictions. This sequential model-building process maintains the order of word prediction, crucial for understanding context in sequential data. The architecture of Ann1: Input Layer: 139 nodes corresponding to unique words. Hidden Layer (H1): 10 nodes for feature extraction. Output Layer (Y1): 139 nodes representing predicted words. For Ann2, we append the output of Ann1 (H1) to the input (X2), allowing Ann2 to learn from the context provided by Ann1's predictions. This creates a more comprehensive model for word prediction based on sequential data. Key Steps Covered: Building the initial neural network (Ann1) for word prediction. Leveraging the partial output of Ann1 to enhance predictions in Ann2. Demonstrating the sequential nature of model building and its importance in understanding context. By following this step-by-step process, you'll gain a practical understanding of how sequential AI models are constructed and how they can be applied to tasks like word prediction. #MachineLearning #WordPrediction #SequentialModel #NeuralNetworks #AI #DataScience #Python #CodeTutorial #genai #promptengineering