Objective: This project aims to leverage the deep learning techniques explored throughout the course, and to apply these methods to a domain of personal interest or academic relevance. Through this endeavor, you will demonstrate your ability to conceptualize, develop, and refine a deep learning model, showcasing your analytical and problem-solving skills. Project Phases 1. Dataset Selection and Task Definition: ° • Identify and select a dataset that aligns with your interests or field of study. Datasets can be found in public repositories (e.g., UCI Machine Learning Repository >>>) or as part of deep learning frameworks. ⚫ Clearly define the task you aim to address with your selected dataset. This could involve prediction, classification, detection, etc. 2. Model Construction: ⚫ Decide on an appropriate model architecture for your task. Consider whether a pre-existing model could be adapted through transfer learning, or if designing a new model from scratch is more suitable. • Justify your model choice based on its relevance and potential effectiveness for the task at hand. 3. Model Training: • Train your model using the chosen dataset, applying best practices in data preprocessing, augmentation (if applicable), and parameter tuning. 4. Model Evaluation and Refinement: • Evaluate your model's performance using appropriate metrics. • Implement strategies for performance improvement, such as hyperparameter tuning, regularization techniques, model ensembling, batch normalization, etc. Tasks and Requirements ⚫ Algorithm Implementation: Detailed implementation of the chosen deep learning model, focusing on the practical application of theoretical concepts. ⚫ Performance Evaluation: Comprehensive assessment of the model performance, employing both quantitative metrics and qualitative analysis. ⚫ Improvement Strategies: Exploration and application of various techniques to enhance model accuracy and efficiency. Comprehensive Report Your final submission should include a thorough report covering: ⚫ Background and Methodology: Overview of the chosen task, relevance to your field, and introduction to the methodology. ⚫ Dataset and Task Description: Detailed description of the dataset and a clear definition of the specific task(s) undertaken. . ⚫ Implementation Details: Elaborate on the model architecture, training process, and any unique implementation challenges. Include code snippets or screenshots as necessary. ⚫ Results: Presentation and discussion of the results achieved, including an analysis of model performance. Improvement Methods: Discussion on the applied strategies for performance enhancement, along with their outcomes. Submission Guidelines Submit your project as a Jupyter Notebook (.ipynb) along with an HTML export of the notebook. ⚫ Include the comprehensive report as a separate document. • Ensure each component is clearly labeled and organized. Grading Criteria ⚫ Technical Implementation (40%): Complexity, creativity, and correctness of the deep learning model implementation. ⚫ Analysis and Evaluation (30%): Depth and thoroughness of the performance evaluation, including the analysis of results and the implementation of improvements. ⚫ Report and Presentation (30%): Clarity, comprehensiveness, and professionalism of the written report and code documentation. The ability to communicate complex ideas effectively will be key.

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Objective: This project aims to leverage the deep learning techniques explored throughout the course, and to apply these methods to a domain of personal interest or academic relevance. Through this endeavor, you will demonstrate your ability to conceptualize, develop, and refine a deep learning model, showcasing your a

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This week you learned about a variety of deep learning techniques. In the previous week, you considered 5 applications. During this week, you determine if any of the applications are appropriate for deep learning techniques. Write a ½ to 1-page memo that justifies whether or not deep learning techniques should be used for any of these 5 applications: • Diagnosis of a well-established but complex disease • Price lookup subsystem for a high-volume merchandise seller • Automated voice inquiry processing system Training of new employees • Handwriting recognition

Use resources mentioned in the class and submit a screenshot of the "X-Y plot with a regression line" along with the rest of the desktop environment showing the system timestamp. You can submit either MATLAB or Python code. The dataset for the assignment is: assignment_data.txt (Do not use the dataset mentioned in the tutorial.) Use the code from the hands-on tutorial. It is available here.

Respond to the following in a minimum of 225 words: Distinguish between deep learning, traditional machine learning, and artificial intelligence and situations where you would utilize deep learning. APA style

Objective: The goal of this assignment is to implement the K-Nearest Neighbors (KNN) algorithm for image classification using the CIFAR-10 dataset. You are required to approach this task using two methods: 1. Utilize a built-in function from an existing library, such as scikit-learn. 2. Implement the algorithm independently. Tasks and Requirements: 1. Algorithm Implementation: • Employ the KNN algorithm for image classification on the CIFAR-10 dataset using both a pre-existing library function and a self-coded version. • Analyze and compare the performance of both implementations. 2. Performance Improvement Strategies: • Develop and apply strategies to enhance the performance of your self-implemented KNN algorithm. Focus on aspects like accuracy or computational efficiency. 3. Comprehensive Report: • Prepare a detailed report encompassing the following sections: • Background and Method Introduction: Provide an overview of the KNN algorithm and its application in image classification. • Dataset and Tasks Description: Describe the CIFAR-10 dataset and outline the specific classification tasks undertaken. ▪ Algorithms Used: Elaborate on the implementation details of both the library-based and self- implemented algorithms. Attach screenshot of the codes whenever necessary. • Results: Present and discuss the classification results obtained from both approaches. • Methods of Improvements: Discuss the strategies employed to enhance the performance of your algorithm, focusing on accuracy and speed. 4. Submission Format: • Submit your work in the form of Jupyter Notebook (.ipynb) and HTML files, along with the final report. Grading Criteria: Implementation of the Algorithm (40%): Demonstrated ability to effectively implement the KNN algorithm using both the library function and self-coded method (20% for each). • Preliminary Results (20%): Ability to achieve reasonable initial results from the implemented algorithms. • Algorithm Improvement and Validation (20%): Thoughtful considerations and implementations for validating and improving your algorithm, including techniques like cross-validation, hyper-parameter tuning, and efficient coding practices. Report Quality (20%): Overall quality, clarity, organization, and thoroughness of the submitted report. Please reference the materials in Lecture 2 slides for detailed information and instructions. To assist you in getting acquainted with fundamental coding techniques, such as acquiring datasets and performing basic operations, a sample answer is provided here This sample serves as a guide to help you understand the basic framework. However, it is crucial that you develop and implement your own code and strategies. You are encouraged to experiment with different hyperparameters, and the implementation of cross- validation is highly recommended.

TO do: it's related to our graduation project. We are using speech recognition algorithms in our system for error detection in the pronunciation of letters. what we are confused in is the part after completing the acoustic model and data processing pipeline, extracting the results from the acoustic model, and converting our audio data pipeline to the required format for the CRNN model, do we need to retrain the same model from scratch to achieve more accurate and faster results? After this step, how do we integrate this model into our program to receive audio data from users? we only need to understand how we will achieve this part. We already have documented how does the DNNs will work. We are only confused in this part and to clarify it more for you we aren't working on the implementation phase yet. we still in the documentation phase which indicates describing and explaining how these algorithms will work

Please read the assignment instructions carefully: 1. 2. The assignment is about the Neural Networks and MLP (Topic 5) You can work individually or in a teams of 2 (no more than 2 students per team). Students from different sections can work together. 3. One submission per team is enough. In the submission, both team members should be indicated in gradescope in the submission page. Multiple submission by different team members may be considered as copying the assignment (i.e. if both team members submitted their work separately, it will be considered cheating attempt) 4. Before you solve the assignment, watch the video that explain the assignment (Attached) and also watch the video that explain the jupyter notebook in topic 5: Neural Network (NNCircle). The assignment relies on these videos. 5. 6. Use the attached notebook to complete the assignment. you need to complete the code in the attached jupyter notebook. Complete the missing code as instructed in the code 7. You need to upload your jupyter notebook to gradescope (Programming Assignment 2). Make sure to indicate your partner when you upload your code to gradescope Any other form of submissions (e.g. email submission) is not accepted under any circumstances. 8. Try to start working on the assignment early. If you have any question, search for the answer in (assignment 2) channel in this team. in case you cannot find the answer, post your question in this channel and the Teaching assistant will answer it.