Author name: Sandeep Giri

Just Imagine teaching your pet new tricks without direct commands but by rewarding its good attempts and gently discouraging the not-so-great ones. This process, based on Feedback and rewards, is at the heart of Reinforcement Learning (RL), a captivating field in machine learning. Does it not seem interesting? Imagine an algorithm that not only learns from its experiences but does so by emulating the way humans make decisions – by weighing the potential outcomes and choosing the most promising path. Picture a computer program that learns to play complex video games. This is the magic of DQNs, which are a type of neural network architecture used in reinforcement learning. Welcome to the world of Deep Q-Networks, where neural networks play the game and play it exceedingly well. Want to know more? Let us dive deep into this in this blog. What is Deep Q Networks? Deep Q-Networks (DQNs) are a type of neural network architecture used in reinforcement learning. A DQN is a neural network that takes in the state of an environment as input and outputs an estimate of the expected future rewards for each possible action that can be taken in that state. Difficult to understand, right? Let me clear you with a simple example. Imagine you have a computer program that’s learning to play a video game. It is not being told exactly what to do, but it’s figuring things out on its own. Now, instead of just knowing what is happening on the screen, this program is trying to predict which actions will give it the best score. This is where Deep Q Networks (DQNs) come in. They are like the brains of this program. They are really good at learning patterns, so they try to figure out the best actions to take based on what the program is seeing. But here’s the cool part: this brain (DQN) doesn’t just memorize what to do in every single situation. It learns and gets better over time by making mistakes and learning from them. I hope you got some idea about this very interesting topic. Now let us check some interesting facts about this Concept. This word, Deep Q Networks, has been searched 80 times on average every month for the last two years worldwide. China comes in first place when it comes to game development, and undoubtedly, China stands 1st in using this Concept on an average of 80 times per month. The above images are the facts to know about this interesting topic. Let us now understand the concepts behind Deep Q Networks. The concepts behind Deep Q-Networks (DQNs) involve a combination of deep learning and reinforcement learning. Here is a breakdown of the key concepts: 1. Reinforcement Learning (RL): 2. Q-Learning: 3. Action-Value Function (Q-Function): 4. Deep Neural Networks: 5. Approximation of Q-Function: 6. Experience Replay: 7. Target Network: 8. Loss Function and Training Process: Let us now understand the architecture of Deep Q Networks: 1. Input Layer: 2. Deep Neural Network Layers: 3. Activation Functions: 4. Output Layer: 5. Output Activation Function: 6. Experience Replay Buffer: 7. Target Network: 8. Loss Function: 9. Optimizer: Deep Q Networks in the Gaming World: Deep Q-Networks (DQNs) are powerful algorithms in reinforcement learning that have shown exceptional performance in playing video games. Here’s how DQNs work in the context of gaming: 1. State Representation: 2. Action Selection: 3. Exploration and Exploitation: 4. Taking an Action: 5. Receiving Feedback: 6. Experience Replay: 7. Training the DQN: 8. Target Network: 9. Iterative Learning: 10. Convergence: Now, we will see a sample code of Cart-Pole. Before that, let us understand what this Cart-Pole is: The CartPole environment is a classic benchmark problem in the field of reinforcement learning. It is provided by the OpenAI Gym, which is a widely used toolkit for developing and comparing reinforcement learning algorithms. In the CartPole environment, there is a pole standing upright on top of a cart. The goal of the agent is to keep the pole balanced by applying forces to the left or right, causing the cart to move. The agent receives a positive reward for each time step that the pole remains upright. The episode ends if: Let us see the code: 1. Importing Libraries: Here, we import the necessary libraries, the OpenAI Gym library, for accessing the CartPole environment. 2. Defining the Q-Network: This defines a simple feedforward neural network. It takes input_size as input (the number of features in the state space) and produces output_size outputs (the Q-values for each action). In this case, input_size is 4 (CartPole has 4 state features) and output_size is 2 (two possible actions: left or right). 3. Defining the DQN Agent: The DQNAgent class is initialized with the size of the state space (state_size), the number of possible actions (action_size), the learning rate for the optimizer (learning_rate), and the discount factor (gamma). 4. Selecting Actions: The select_action method implements an epsilon-greedy strategy. With probability epsilon, a random action is selected. Otherwise, the action with the highest Q-value from the Q-network is chosen. 5. Training the Agent: The train method performs a single step of training. It computes the Q-value for the chosen action, computes the target Q-value using the Bellman equation, and minimizes the loss using a Huber loss function (smooth L1 loss). 6. Hyperparameters: These are the hyperparameters for the DQN agent and training process. 7. Initializing Environment and Agent: This creates the CartPole environment and initializes the DQN agent. 8. Training Loop: This is the main training loop. It iterates over episodes, where each episode is a run of the environment. Within each episode, the agent interacts with the environment, selects actions, and updates the Q-network. When you see “total reward: 10”, it means that in that particular episode, the agent managed to keep the pole upright for 10 time steps before the episode terminated. Conclusion Now we have got a fair idea of this Concept. We have seen what Deep Q Networks means, the logic behind it, its application

In the ever-evolving landscape of artificial intelligence, OpenAI stands at the forefront, pioneering breakthroughs that redefine our interactions with technology. Among their stellar contributions, OpenAI’s Natural Language Processing (NLP) models have emerged as trailblazers, reshaping the way we comprehend, communicate, and innovate. OpenAI’s commitment to pushing the boundaries of AI is epitomized by two standout NLP models: PEGASUS and GPT (Generative Pre-trained Transformer). Each of these models brings a unique set of capabilities to the table, transforming the realm of language processing in unprecedented ways. In this blog post, we embark on a fascinating journey through the corridors of OpenAI’s NLP innovations. From the ingenious extractive summarization prowess of PEGASUS to the awe-inspiring language generation capabilities of GPT, we will uncover how these models are redefining natural language understanding and generation. Get ready to delve into the future of communication—where language is not just processed; it is understood, synthesized, and utilized in ways that were once the realm of science fiction. What is Pegasus Model? PEGASUS is a natural language processing (NLP) model developed by Google Research. It is specifically designed for abstractive text summarization, where the model generates a concise and coherent summary that captures the essential information from a given document. Here are some key features and aspects of the PEGASUS model: Amazing facts about Pegasus model On an average 80 times every month, this Pegasus model is searched for the last year. From Google Trends, we can see Uganda is leading in the countries list in this field. PaLM Models Pathways Language Model (PaLM) is a large language model (LLM) developed by Google AI. PaLM is a transformer-based language model. Transformers are a type of neural network that are particularly well-suited for natural language processing tasks. They work by learning to represent the relationships between words and phrases in a sentence. PaLM is trained on a massive dataset of text and code. This dataset includes books, articles, code, and other forms of text. PaLM learns to represent the relationships between words and phrases in this dataset. This allows it to perform a wide range of tasks, such as translation, summarization, question answering, code generation, and creative writing. Google Trends facts: On an average 64 times per month, topics related to PaLM has been searched worldwide in the last one year. UAE is leading the list in this field Some understanding of NLP models Natural Language Processing (NLP) models play a crucial role in understanding and generating human language. Here are some important things to know about NLP models: 1. Types of NLP Models: 2. Pre-trained Models: 3. Transfer Learning: 4. Common NLP Tasks: 5. Attention Mechanism: 6. BERT and Contextual Embeddings: 7. Ethical Considerations: 8. Evaluation Metrics: 9. Challenges: 10. Continual Advancements: Coding of Pegasus model: Important points on Pegasus and PaLM: 1.PEGASUS: Use Case: PEGASUS is primarily designed for abstractive text summarization. Key Features: Applications: 2.PaLM (Passage Language Model): Use Case: Key Features: Applications: Possible Use Cases and Synergies: 1. Summarization Followed by Question-Answering: 2. Document Understanding: 3.Multimodal Applications: Future of NLP models: 1. Large Pre-trained Models: 2. Multimodal Models: 3. Efficiency and Model Compression: 4. Few-shot and Zero-shot Learning: 5. Domain-Specific and Specialized Models: 6. Explainability and Interpretability: 7. Continual Learning: 8. Robustness and Adversarial Défense: 9. Ethical Considerations: 10. Interactive and Conversational AI: Conclusion In the ever-evolving landscape of Natural Language Processing (NLP), models like PaLM and PEGASUS stand as testament to the relentless pursuit of excellence in understanding and generating human-like text. As we traverse the realms of abstractive summarization with the majestic wings of PEGASUS and delve into the intricate passages of contextual comprehension with PaLM, it becomes evident that the future of NLP is both thrilling and promising. PaLM, with its focus on mastering the art of passage-level understanding, opens doors to a world where machines can truly grasp the nuances and intricacies within textual passages. This specialized approach in the quest for better question-answering capabilities paints a picture of NLP models not just as processors of words but as interpreters of context-rich information. On the other hand, PEGASUS soars to new heights in the realm of abstractive summarization. Its ability to distil the essence of lengthy documents into concise, coherent summaries mirrors the elegance of a well-crafted narrative. PEGASUS not only summarizes text but breathes life into the art of compression, presenting an exciting paradigm for distillation and synthesis of information. Thanks for having patience to read my blog. If you liked it give feedback in the comment session.

In the vast landscape of artificial intelligence, language models have emerged as powerful tools that can understand, generate, and manipulate human-like text. One fascinating aspect of harnessing these models lies in the art and science of prompt engineering—a skill that can unlock the true potential of these language behemoths. In this blog series, we will unravel the intricacies of prompt engineering from the basics of constructing effective queries to the nuances of fine-tuning prompts for specific tasks. In this blog we would be looking at the application of prompt engineering that is PAL. Go through the entire blog to under the concept of PAL to understand the importance of Prompt Engineering. So, let us begin our journey. What is PAL? Before we understand PAL,let us first understand what LLM is. In the context of artificial intelligence and natural language processing, “LLMs” could refer to Large Language Models. These are powerful machine learning models, such as OpenAI’s GPT (Generative Pre-trained Transformer) series, which are trained on massive amounts of data to understand and generate human-like text. Large language models (LLMs) have recently demonstrated an impressive ability to perform arithmetic and symbolic reasoning tasks, when provided with a few examples at test time (“few-shot prompting”). Much of this success can be attributed to prompting methods such as “chain-of-thought”, which employ LLMs for both understanding the problem description by decomposing it into steps, as well as solving each step of the problem. While LLMs seem to be adept at this sort of step-by-step decomposition, LLMs often make logical and arithmetic mistakes in the solution part, even when the problem is decomposed correctly. we present Program-Aided Language models (PAL): a novel approach that uses the LLM to read natural language problems and generate programs as the intermediate reasoning steps, but offloads the solution step to a runtime such as a Python interpreter. With PAL, decomposing the natural language problem into runnable steps remains the only learning task for the LLM, while solving is delegated to the interpreter. Let us understand some interesting facts and figures of Prompt Engineering and Large Language Models: Nearly on an average of 50 times per month, Prompt Engineering topics have been searched worldwide for the last year. China is leading the list in searching for these advanced topics. That was about Prompt Engineering. Now let us check about Large language models. Nearly on an average of 70 times per month, Large language models topics have been searched worldwide for the last year. Here also China is leading the list. More insights about PAL Let us first understand about chain of thought engineering. “Chain of thought” prompt engineering involves constructing prompts in a way that encourages a language model to generate a sequence of connected and coherent ideas. It’s about guiding the model’s thinking in a logical progression. In this example, the prompt is designed to lead the language model through a logical sequence: 1. Historical Roots and Evolution of AI: 2. Key Breakthroughs: 3. Ethical Considerations: 4. Future Developments: Let us check a code of Prompt Engineering: 1. Import OpenAI Library and Set API Key: This line imports the OpenAI library, which provides access to the OpenAI GPT-3 API. Replace ‘YOUR_API_KEY’ with your actual OpenAI API key. You need a valid API key to make requests to the OpenAI API. 2. Define Prompt: This line defines a prompt that will be used to instruct the model. In this case, it indicates that the user wants to translate English text to French. 3. Combine Prompt and User Input: This line combines the predefined prompt and the user’s input to create a complete prompt for the translation task. 4. Call OpenAI API: This line calls the OpenAI API (openai.Completion.create) with the specified parameters. It sends the combined prompt to the API and receives a response. 5. Extract and Print Translated Text: This part extracts the generated text from the API response and prints it as the translated text. Uses of Prompt Engineering: 1. Task Customization: 2. Improving Specificity: 3. Mitigating Bias: 4. Controlling Output Length: 5. Domain Adaptation: 6. Enhancing Specific Features: 7. Generating Multiturn Conversations: 8. Fine-tuning and Training: 9. Translating Instructions: 10. Adapting to User Preferences: Interesting facts about PAL: Prompt engineering is an intriguing aspect of working with language models, and it plays a significant role in shaping the behaviour and output of these models. Here are some interesting facts about prompt engineering: 1. Creative Control: 2. Influence on Bias: 3. Iterative Process: 4. Task Adaptation: 5. Combination with Constraints: 6. Human-in-the-Loop Collaboration: 7. Transfer Learning Challenges: 8. Interplay with Model Architecture: 9. Evolving Strategies: 10. Ethical Considerations: Conclusion: In conclusion, PAL is a fascinating and powerful technique in the realm of natural language processing (NLP) that empowers users and developers to shape the behaviour of language models. In essence, PAL empowers us to navigate the intricate landscape of language models, providing a means to achieve tailored and desired outcomes. The impact of prompt engineering in today’s era is profound, influencing various aspects of natural language processing (NLP) and AI applications. It empowers users, enhances model performance, addresses ethical considerations, and plays a crucial role in shaping the future of AI applications. As technology continues to advance, the role of prompt engineering is likely to become even more central in optimizing and refining language model outputs. Thanks for having patience to read out this blog. If you like this blog, give your feedback in the comment section.

Suppose you are given a task to solve a mystery. But instead of a magnifying glass and trench coat, you are armed with data and a curious mind. Your mission is to uncover the hidden connection between two variables, to reveal the story lurking beneath the surface. Welcome to the world of simple linear regression, where data is your clue, and the regression equation is your secret decoder. Welcome to my new blog on one of the most important and widely used Supervised technique i.e., Simple Linear Regression. Supervised learning techniques, a subset of machine learning, are widely used in various real-world applications across different domains. These techniques involve training models on labelled data to make predictions or classifications. Such a technique is Simple Linear Regression, it is a fundamental statistical method, that is used to understand the relationship between two continuous variables. It establishes a linear relationship between a dependent and an independent variable. The goal is to find the best-fitting straight line, also known as the regression line or the least-squares line, that describes the relationship between these two variables. We will dive deep into the concepts in this blog. What Simple Linear Regression means? Simple Linear Regression is a supervised learning technique that is used to model the relationship between two continuous variables. It assumes that there is a linear relationship between a dependent variable Y and an independent variable X. Finding a linear equation that best fits the data is the goal. The equation for the Simple linear Regression is: Y=aX+b Where: The primary objective of simple linear regression is to estimate the values of a and b that minimize the sum of the squared differences between the observed values of Y and the values predicted by the linear equation. This is often referred to as the method of least squares. Now let us check some interesting facts of Simple Linear Regression from Google Trends: It has been observed that nearly on an average 60 times per month this word has been searched all over the world. Let us check the countries where the concept of simple linear regression is followed As we can see 100 times per month sectors in Ethiopia have searched for Simple linear Regression. From these facts we can see how these supervised learning concepts are searched all over the world. Concepts of Simple Linear Regression The steps followed in Simple Linear Regression are: OLS method in Simple Linear Regression: OLS stands for Ordinary Least Squares, and it is a method used in simple linear regression to estimate the parameters of a linear relationship between two variables. It finds the line that best fits the data by minimizing the sum of the squared residuals. Now let us solve one problem using Simple Linear Regression Supervised technique: We are taking waist circumference and adipose tissue dataset for our analysis. Here we need to predict Adipose Tissue of body based on Waist Circumference. Let us see our dataset Let us do the coding part step by step. Step 1. Importing necessary libraries and importing the dataset. This is the Dataframe we have after reading csv file. Step 2. Checking dimensions of data and getting data description. As we can see the data dimension is (109,2) that means 109 rows and 2 columns are present and parameters like mean, standard deviation, minimum, maximum etc are given for the dataset. After doing preprocessing, we are going to do model building. Step 3. Importing required library for Simple linear Regression and fitting our model. Package statsmodel is imported and ols is one of the statistical models used to get coefficients of linear regression equation that creates a relationship between dependent and independent variables. In our case Waist is the independent variable and AT is the dependent variable and ols method used to minimize the sum of square error between the observed and predicted values. Now let us check the output and get the summary of fitted model. The equation we are getting from our model is AT=3.4589*Waist-215.9815.R-Squared and Adj. R-squared determines goodness of fit.67% variability is there in the data and the model is fitting well. Step 4: Now check the predicted values and find out the error The error of 32.76 is coming. Step 5. Now let us try with some transformation. We can see R-squared value is 0.675 which has increased from earlier model. RMSE value is also bit lesser than 1st, model. We can see still many observed points are not lying on the predicted line. Step 6:Let us do polynomial transformation This model is good than the previous one, it is because here R-squared value is more. The equation of this model is AT=-7.8241+0.2289*Waist-(0.0010*Waist*Waist) Step 7: Let us check the predicted values and calculate the RMSE. As we can see most of the observed values are lying on the line, error is 32.24 which is less than other models we have seen. Thus, the model giving the least error is having polynomial transformation. We can see the errors for both train and test data are closer to each other and less. Therefore, the model is right fit model. Some Applications of Simple Linear Regression: The following are some of the areas where Simple Linear Regression is used: A lot of areas are still there where linear relationship between variables persisting. Conclusion From this blog we learnt what Simple Linear Regression model is, why it is widely used in Data Science, it’sapplication in real world and a code example with Waist circumference Adipose Tissue dataset. In conclusion, simple linear regression offers unique insights into the linear relationship between two variables, making it a valuable tool for understanding and predicting outcomes in various fields. Simple linear regression will continue to be used in the future, alongside more advanced statistical techniques, for various purposes in data analysis and decision-making, its simplicity, interpretability, and historical significance ensure that it will continue to be a valuable and relevant statistical technique in the future. Simple linear regression can

Imagine a world where machines not only hear your words but understand them with astonishing precision. This astonishment is made possible by the ingenious design of Speech Recognition Acoustic Models. Welcome to the wondrous world of Speech Recognition Acoustic Models, where speech becomes a universal language comprehended by silicon minds. The futuristic vision is made possible by the intricate workings of speech recognition acoustic models, the unsung heroes behind the scenes of voice assistants, transcription services, and a myriad of other applications. Speech recognition acoustic models are a vital component of automatic speech recognition (ASR) systems. They play a key role in converting spoken language into written text. Now let us deep dive into the concept pf this wonderful topic. What is Speech Recognition – Acoustic model? Automatic Speech Recognition (ASR) is a technology that enables machines to convert spoken language into written text. This technology has numerous applications including voice assistants (like Siri, Alexa), transcription services, customer service applications, and more. It’s essentially a mathematical model that learns to understand the relationship between audio input (speech signals) and the corresponding linguistic units, such as phonemes or sub-word units. Speech recognition acoustic models have evolved significantly with the advent of deep learning. Models like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are widely used in acoustic modelling. These models are at the core of technologies like voice assistants (e.g., Siri, Alexa), transcription services, and more. They play a crucial role in enabling machines to understand and interact with spoken language. Some interesting insights of this model: In the following attachments, you can observe that the given model has been searched for an average of 60 times every month for the past 2 years. India and USA are the 2 countries mostly dealing with this model. How Speech Recognition Acoustic model works? The following steps are followed: 1.Audio Input: 2. Feature Extraction: 3. Deep Learning Architecture: 4. Learning Relationships: 5. Probabilistic Output: 6. Integration with Language Model and Lexicon: 7. Decoding: 8. Final Transcription: 9. Adaptability and Fine-tuning: 10. Feedback Loop: Overall, a speech recognition acoustic model plays a crucial role in understanding the acoustic characteristics of speech and converting it into a form that can be processed by a computer algorithm to generate accurate transcriptions. Speech Recognition model code: Now we are going to see codes with the use of popular ASR libraries or APIs Google Speech-to-Text API: we will be working with codes with the use of Python Library SpeechRecognition,which wraps various ASR(Acoustic Speech Recognition) engines, including Google Web Speech API, Sphinx, etc. In the above code, SpeechRecognition library is installed. The module speech_recognition is imported with alias sr. The Recognizer class is part of the SpeechRecognition library in Python. This class is used to perform various operations related to speech recognition, such as listening for audio input from a microphone, recognizing speech from an audio source, and working with different speech recognition engines. Method record is used to record audio from the source. recognize_goggle, this method recognizes speech using the Google Web Speech API.If audio is not understandable error will raise. Google Web Speech API, also known as the Web Speech API, is a browser-based API that allows web developers to integrate speech recognition capabilities into their web applications. It enables applications to convert spoken language into text, making it useful for tasks like voice commands, transcription services, and more. Hugging Face’s transformers Library: ‘Transformers’ library is used here to create pipeline where task and model must be taken and thus ASR is performed on audio file. This is the output we got and the text from the audio is extracted. Language Model in Speech Recognition: In speech recognition, a language model plays a crucial role in improving the accuracy and fluency of the recognized text. It provides additional context to help the system make more accurate predictions about the next word or sequence of words in the transcription. Important properties of language model in speech recognition: The language model can be built using various approaches: By combining an acoustic model with an appropriate language model, you can significantly improve the accuracy and fluency of transcriptions in a speech recognition system. Sample code to understand the language model for speech recognition: We have imported required deep learning libraries and then under the variable corpus we have given a text. Tokenization to be done where we use the Tokenizer from Keras to convert text into sequences of integers. Then Input Sequences and Labels is created where we create sequences of increasing length from the tokenized text. These sequences are used as input, and the next word in each sequence is used as the label. Model is Defined where we define a simple LSTM-based neural network. Model Compilation is done where we compile the model with suitable loss function and optimizer.Model is trained where we train the model on the input sequences and labels.Text Generation: We use the trained model to generate text. Starting with a seed text, we predict the next word and append it to the seed text. Model is trained with 100 epochs and we got good accuracy of 93.75% with loss of 0.7415. As we can see we have got next 5 predicted words as ‘icc cricket world cup trophy’. Acoustic wave simulation Acoustic wave simulation is a technique used in the development and testing of speech recognition systems. Let us see how acoustic wave simulation is relevant to speech recognition: 1. Data Augmentation: 2. Noise and Environmental Variation: Simulating different acoustic environments (like noisy environments, reverberant rooms, etc.) helps in training the model to be more robust to different real-world scenarios. 3. Speaker Variability: Simulation can be used to introduce variations in speaker characteristics, like pitch, accent, or gender. This helps the model generalize better across different speakers. 4. Adversarial Attack Simulation: In security-related scenarios, simulating adversarial attacks helps in making speech recognition systems more secure against intentional distortions. 5. Testing and Evaluation: Simulated data is often used in testing and evaluating

Welcome to the fascinating world of generative models in recommender systems, where the power of artificial intelligence meets the art of personalization! Generative models for recommendation systems – an innovative approach that surpasses traditional algorithms to craft personalized suggestions like a master artist painting vibrant portraits of your interests Imagine a recommender system that not only understands your preferences but has the creative power to implore entirely new possibilities adapted just for you. In this domain, data is not just analysed; it becomes a canvas on which these models paint a rich tapestry of novel and diverse recommendations, promising a journey of delightful discoveries at every turn. Let us begin on this fascinating exploration of generative models, where the boundaries of personalized recommendations are redefined, and the art of suggestion reaches unprecedented heights. Let us dive deep into these concepts in this blog. Read the entire blog to gain immense knowledge on this topic. What is Generative model for Recommendation system? Generative models for recommender systems are a class of machine learning models that aim to understand the underlying patterns in user-item interaction data and generate personalized recommendations. This means that generative models not only predict user preferences for existing items but also have the ability to generate entirely new item recommendations that are tailored to each user’s unique preferences. The fundamental idea behind generative models is to learn the patterns and relationships within the user-item interaction data in a way that allows the model to create new, realistic examples. Let us check some facts about Generative models: This is searching trends of Generative Model in India for the past 30 days. Daily average search of this topic is more than 75 times. This is how the world is proceeding with the concept of Generative Model. Let us check the states where several companies are working with this concept. As we can see Karnataka and Telangana are the states leading from front. These are the countries searched the most of this topic in the last 30 days. China, Singapore leading the search history. We have 3 popular generative model: Variational Autoencoder (VAE) and Generative Adversarial Network (GAN), Autoregressive model, these can grasp the intricate relationships between users and items. They create a multidimensional representation of preferences, allowing for a better understanding of individual tastes and preferences. Let us discuss VAE and GAN in detail. Variational Autoencoder (VAE): It is a generative AI algorithm that uses deep learning to detect anomalies, remove noise and generate new content. It is used to learn a probabilistic mapping of input data into a lower-dimensional latent space, and from that latent space, generate new data that resembles the original input. The basic structure of a Variational Autoencoder consists of two main components: 1.Encoder: The encoder takes the input data (e.g., user-item interactions in the case of recommender systems) and maps it into a latent space that is lower dimension space. The output of the encoder is not a deterministic encoding, but rather a probability distribution of the latent variables. Now Question arises PCA and Encoder same? No, as PCA learns linear relationship and Encoder learns Non-Linear relationship. From the figure we can see that PCA is learning the data having linear structure and Encoder is learning the data having complex structure. Once Encoder uses linear activation functions then in that case PCA=Encoder. 2.Decoder: The decoder takes a sample from the latent space (i.e., latent variables) and reconstructs the original input data from that sample. Like the encoder, the decoder also produces a probability distribution for the output data. Let us now see what VAE for Recommendation system is: Here is a high-level overview of how a VAE-based recommender system works: 1.Data Representation: Convert user-item interactions into a matrix format, where rows represent users, columns represent items, and the entries contain user-item interactions (e.g., ratings, binary indicators, or other relevant metrics). 2.VAE Architecture: 3.VAE Training: 4.Recommendation Generation: Let us check one example code of Variational Encoder. We are considering Movies Lens dataset and working with it to develop the Recommendation model. First, we install required packages. ! pip install tensorflow –upgrade import numpy as np import pandas as pd from keras.layers import Input, Dense, Lambda from keras.models import Model from keras import backend as K from keras import objectives from sklearn.model_selection import train_test_split   Load data and do preprocessing. data = pd.read_csv(‘/content/ratings.csv’) # Preprocess the data to create the user-item interactions matrix   user_item_matrix = data.pivot(index=’userId’, columns=’movieId’, values=’rating’) user_item_matrix = user_item_matrix.fillna(0)   # Convert the user-item interactions matrix to a NumPy array   user_item_matrix_array = user_item_matrix.to_numpy()   # Spliting the data into train and test   train_data, test_data = train_test_split(user_item_matrix_array, test_size=0.2, random_state=42)   Defining parameters, Building Encoder, Decoder and combining encoder and decoder to build the model. # Define VAE parameters original_dim = user_item_matrix_array.shape[1]  # Number of items (features) latent_dim = 32                                 # Number of latent dimensions batch_size = 64 epochs = 50 # VAE encoder architecture x = Input(shape=(original_dim,)) h = Dense(64, activation=’relu’)(x) z_mean = Dense(latent_dim)(h) z_log_var = Dense(latent_dim)(h) # Reparameterization trick def sampling(args):     z_mean, z_log_var = args     epsilon = K.random_normal(shape=(K.shape(z_mean)[0], latent_dim), mean=0., stddev=1.0)     return z_mean + K.exp(z_log_var) * epsilon z = Lambda(sampling)([z_mean, z_log_var]) # VAE decoder architecture decoder_h = Dense(64, activation=’relu’) decoder_mean = Dense(original_dim, activation=’sigmoid’) h_decoded = decoder_h(z) x_decoded_mean = decoder_mean(h_decoded) # Define VAE custom loss function (binary cross-entropy + KL divergence) def vae_loss(x, x_decoded_mean):     xent_loss = objectives.binary_crossentropy(x, x_decoded_mean)     kl_losss = – 0.5 * K.sum(1 + zlog_var – K.square(z_mean) – K.exp(zlog_var), axis=-1)     return xent_loss + kl_losss # Compile VAE model vae = Model (x, x_decoded_mean) vae.compile(optimizer=’adam’, loss=vae_loss) Training the model. # Train the VAE model vae.fit(train_data, train_data,         shuffle=True,         epochs=epochs,         batch_size=batch_size,         validation_data=(test_data, test_data)) # Use the trained VAE model to generate recommendations for a user def generate_recommendations(user_data, vae_model):