Bootcamp for introduction to Artificial Intelligence
Bootcamp for introduction to Artificial Intelligence, available at Free, has an average rating of 4.31, with 41 lectures, based on 8 reviews, and has 763 subscribers.
You will learn about Gain insights into diverse AI terminologies, from algorithms to neural networks, expanding your knowledge base significantly. Learn ethical model selection, ensuring alignment with societal values and understanding your role in responsible AI implementation. Develop hands-on expertise in CNN model creation, mastering design principles for practical application in complex scenarios. Understand the importance of continuous data validation and thorough model testing to identify and mitigate errors, limitations and ensuring robustness Fine tuning the model This course is ideal for individuals who are This course caters to a diverse audience, including leaders, developers, and users who are poised to utilize pre-trained and commercially available AI models. It's imperative for participants to exercise caution and mindfulness when leveraging these models, ensuring they understand potential biases, limitations, and ethical implications associated with their deployment. As stewards of AI technology, it's crucial for users to uphold ethical responsibilities toward society, prioritizing fairness, transparency, and accountability in their AI endeavors. Moreover, this course offers comprehensive coverage of AI terminologies, equipping learners with a deeper understanding of the models they're working with. Whether you're a leader seeking informed decision-making, a developer aiming for proficient model development, or a user navigating AI applications, this course empowers you to navigate the complex AI landscape with confidence and ethical awareness. It is particularly useful for This course caters to a diverse audience, including leaders, developers, and users who are poised to utilize pre-trained and commercially available AI models. It's imperative for participants to exercise caution and mindfulness when leveraging these models, ensuring they understand potential biases, limitations, and ethical implications associated with their deployment. As stewards of AI technology, it's crucial for users to uphold ethical responsibilities toward society, prioritizing fairness, transparency, and accountability in their AI endeavors. Moreover, this course offers comprehensive coverage of AI terminologies, equipping learners with a deeper understanding of the models they're working with. Whether you're a leader seeking informed decision-making, a developer aiming for proficient model development, or a user navigating AI applications, this course empowers you to navigate the complex AI landscape with confidence and ethical awareness.
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Summary
Title: Bootcamp for introduction to Artificial Intelligence
Price: Free
Average Rating: 4.31
Number of Lectures: 41
Number of Published Lectures: 41
Number of Curriculum Items: 41
Number of Published Curriculum Objects: 41
Original Price: Free
Quality Status: approved
Status: Live
What You Will Learn
- Gain insights into diverse AI terminologies, from algorithms to neural networks, expanding your knowledge base significantly.
- Learn ethical model selection, ensuring alignment with societal values and understanding your role in responsible AI implementation.
- Develop hands-on expertise in CNN model creation, mastering design principles for practical application in complex scenarios.
- Understand the importance of continuous data validation and thorough model testing to identify and mitigate errors, limitations and ensuring robustness
- Fine tuning the model
Who Should Attend
- This course caters to a diverse audience, including leaders, developers, and users who are poised to utilize pre-trained and commercially available AI models. It's imperative for participants to exercise caution and mindfulness when leveraging these models, ensuring they understand potential biases, limitations, and ethical implications associated with their deployment. As stewards of AI technology, it's crucial for users to uphold ethical responsibilities toward society, prioritizing fairness, transparency, and accountability in their AI endeavors. Moreover, this course offers comprehensive coverage of AI terminologies, equipping learners with a deeper understanding of the models they're working with. Whether you're a leader seeking informed decision-making, a developer aiming for proficient model development, or a user navigating AI applications, this course empowers you to navigate the complex AI landscape with confidence and ethical awareness.
Target Audiences
- This course caters to a diverse audience, including leaders, developers, and users who are poised to utilize pre-trained and commercially available AI models. It's imperative for participants to exercise caution and mindfulness when leveraging these models, ensuring they understand potential biases, limitations, and ethical implications associated with their deployment. As stewards of AI technology, it's crucial for users to uphold ethical responsibilities toward society, prioritizing fairness, transparency, and accountability in their AI endeavors. Moreover, this course offers comprehensive coverage of AI terminologies, equipping learners with a deeper understanding of the models they're working with. Whether you're a leader seeking informed decision-making, a developer aiming for proficient model development, or a user navigating AI applications, this course empowers you to navigate the complex AI landscape with confidence and ethical awareness.
Gain comprehensive insights into diverse AI terminologies, from algorithms to neural networks, significantly expanding your knowledge base. This bootcamp emphasizes ethical model selection, ensuring alignment with societal values and understanding your role in responsible AI implementation.
You will learn the importance of continuous data validation and thorough model testing to identify and mitigate biases, errors, and limitations, ensuring robustness. Develop hands-on expertise in CNN model creation, mastering design principles for practical application in complex scenarios.
This course is designed for a diverse audience, including leaders, developers, and users who are poised to utilize pre-trained and commercially available AI models. Participants are encouraged to exercise caution and mindfulness when leveraging these models, understanding the limitations and implications associated with their deployment.
As stewards of AI technology, it’s crucial for users to uphold responsibilities toward society by prioritizing fairness, transparency, and accountability in their AI endeavors. This bootcamp offers comprehensive coverage of AI terminologies, equipping learners with a deeper understanding of the models they’re working with.
Whether you’re a leader seeking informed decision-making, a developer aiming for proficient model development, or a user navigating AI applications, this course empowers you to navigate the complex AI landscape with confidence and awareness. Learn from industry experts and become proficient in the ethical and practical aspects of AI technology.
Course Curriculum
Chapter 1: Introduction
Lecture 1: Introduction to course and about myself
Lecture 2: Intended audience and course content
Lecture 3: What is Artificial Intelligence(AI) and various classifications?
Lecture 4: What are various runtimes and difference in between CPU,TPU and GPU?
Lecture 5: What is LPU and how it is different than CPU,GPU and TPU?
Lecture 6: What is Model and what we should be looking when it comes to model card?
Chapter 2: Developing your first AI Model
Lecture 1: Real-Life Analogies: Understanding Neuron Weightage in Everyday Scenarios
Lecture 2: How perceptrons work and get triggerred and what are weightages
Lecture 3: How weightages in perceptrons/neurons work in real time?
Lecture 4: What are biases and how they work?
Lecture 5: What is activation function and why we need it
Lecture 6: What are types of activation functions and use cases?
Lecture 7: What is Neural Network and Deep Learning network?
Lecture 8: What are parameters?
Lecture 9: What is CNN Model?
Lecture 10: What are hyperparameters,epochs,learing rate and batches?
Chapter 3: Setup of Local AI Notebook
Lecture 1: Why and what is Notebook(AI Context) ?
Lecture 2: Mastering Jupyter Notebook: A Comprehensive Guide to Navigation
Lecture 3: Setting Up a Local Anaconda Jupyter Notebook: A Beginner's Guide
Lecture 4: Anaconda Navigator Environment Setup: Simplifying Your Data Science Workflow
Lecture 5: Setting Up Your Google Colab Account and Creating Your First Notebook
Lecture 6: Setting up your runtime to GPU and verifying GPU runtime
Lecture 7: Setting Up Runtimes and Programmatically Verifying TPU Availability in Colab
Lecture 8: Unleashing the Power of Processing Units:Comparative Analysis of CPU, GPU, TPU
Chapter 4: Hand on CNN -Deep Leaning
Lecture 1: What is tensorflow and Keras Libraries?
Lecture 2: Downloading dataset and understand type of datasets
Lecture 3: Verifying Training and test dataset
Lecture 4: Matrix representation of image data
Lecture 5: Normalize pixel data for CNN and add additional dimensions
Lecture 6: Define Convolution step in the model defining process
Lecture 7: Understand typical CNN Model
Lecture 8: Understand Convolution layer matrix multiplication process for edge detections
Lecture 9: Understand edge detection and remaining layers and set hyperparameters
Lecture 10: What is maxpooling?
Lecture 11: Verify and validate model accuracy
Lecture 12: Saving Model locally and testing local image
Lecture 13: Test local sample image
Chapter 5: Conducting a comprehensive assessment of model completeness ?
Lecture 1: What is loss or cost function ?
Lecture 2: What is backpropogation and forward propogation?
Lecture 3: What is gradient Descent and type of gradient Descent?
Chapter 6: Part 2 Hands On CNN Model-Verification and Fine tuning
Lecture 1: Tweaking hyperparameters of model for increasing accuracy
Instructors
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Sachin Kapale
Director Of Architect
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- 2 stars: 1 votes
- 3 stars: 0 votes
- 4 stars: 4 votes
- 5 stars: 3 votes
Frequently Asked Questions
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