Bootcamp for introduction to Artificial Intelligence

Name: Bootcamp for introduction to Artificial Intelligence
Rating: 4.4 (42 reviews)

Understanding AI at a Conceptual Level: Moving Beyond Just Using Python Libraries and Preexisting Model users

Created bySachin Kapale

Last updated 5/2024

English

What you'll 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

Course content

6 sections • 41 lectures • 1h 59m total length

Introduction to course and about myself2:31
•www.sachinkapale.com
•www.linkedin.com/in/sachinkapale
•https://kapalesachin.medium.com/
•https://www.youtube.com/@pranmya1
•https://www.youtube.com/@learnITInFiveMiutes

https://www.amazon.com/Immutability-Migration-Strategies-Implementation-Achievement/dp/9355512090

Book is also available on BPB publication site.
Intended audience and course content3:29
You will learn about

•Understanding AI Terminologies
•AI Concepts in depth
•Environment setup offline and online
•Runtime Selections(CPU,GPU and TPU)
•Continuous Data Validation
•Hands-on CNN Model Creation –Environment Setup
•Various Libraries
•Model Testing and validation
•Fine tuning the models
•Bonus: Ethical Model Selection-for pretrained model
•Bonus: Commercially Available AI Models
•Neurons, Perceptron's working, Weights, biases, linear equations, role of activation functions, gradient descent, learning rate, epochs, batches, training data sets, hyper parameters, layers, hidden layers, output layers fine tuning model and understanding of underfitting and overfitting.
What is Artificial Intelligence(AI) and various classifications?6:15
Explore what artificial intelligence means, its relation to machine learning, neural networks, deep learning, and generative AI, including large language models. Identify how ChatGPT and LLMs generate text and imagery.
What are various runtimes and difference in between CPU,TPU and GPU?7:15
Compare cpu, gpu, and tpu runtimes for ai training and execution, detailing cpu's generic design, gpu's parallel cores and high bandwidth memory, and tpu's tensor-oriented parallelism on cloud platforms.
What is LPU and how it is different than CPU,GPU and TPU?4:49
Learn what a language processing unit (lpu) is, how its sequential NLP design differs from cpu, gpu, and tpu, and why it enables token output for chat and call-center apps.
What is Model and what we should be looking when it comes to model card?2:47
Discover what an ai model is and how a model card documents training data, parameters, context length, evaluation benchmarks, and safety, privacy, and ethical considerations.

Real-Life Analogies: Understanding Neuron Weightage in Everyday Scenarios2:56
Curious about how the concept of neuron weightage relates to real-life situations? Join us on a fascinating exploration where we bridge the gap between neural networks and everyday life in this enlightening video tutorial.
In this video, we'll unravel the mystery behind neuron weightage and how it mirrors decision-making processes in our daily experiences. We'll start by explaining the fundamentals of neurons and perceptrons, highlighting their role as building blocks of artificial intelligence.
Next, we'll delve into relatable analogies to illustrate how neuron weightage operates in real-time scenarios. From choosing a restaurant based on reviews to making investment decisions influenced by various factors, we'll draw parallels between neural networks and human decision-making.
Discover how the concept of weightage influences our choices, preferences, and actions, mirroring the synaptic strengths within neural networks. By the end of this tutorial, you'll gain a deeper understanding of how neural networks mimic human cognition and decision-making processes.
Whether you're a student, enthusiast, or professional in the field of artificial intelligence, this video will provide valuable insights into the fascinating intersection of neuroscience and everyday life. Join us as we uncover the hidden connections between neurons and the world around us!
How perceptrons work and get triggerred and what are weightages2:51
Perceptrons receive multiple inputs with assigned weightages and use a threshold to decide whether to output yes or no.
How weightages in perceptrons/neurons work in real time?2:48
Explore how weights in perceptrons influence decisions through a real-time scenario, showing how multiple neurons cooperate and adjust weights to shape the neural network’s output.
What are biases and how they work?1:24
Explore how biases affect perceptron outputs by adding a bias to the weighted sum of inputs, altering threshold-triggered decisions in neural units.
What is activation function and why we need it1:51
Explore activation functions and why they matter, enabling neurons to produce outputs by squashing weighted sums and bias into 0 or 1.
What are types of activation functions and use cases?3:31
Explore common activation functions including sigmoid, tanh, ReLU, leaky ReLU, and softmax, with use cases for binary classification, sentiment analysis, CNNs, preventing dead neurons, and probabilistic outputs.
What is Neural Network and Deep Learning network?2:08
Explore how neural networks transform inputs through input, hidden, and output layers, and see how deep learning uses multiple hidden layers and CNNs for image recognition.
What are parameters?2:08
Parameters in a deep learning network consist of weights, biases, and the number of neurons across layers, and act as levers that adjust outputs during learning.
What is CNN Model?1:23
Learn how convolutional neural networks (CNNs) process grid-like image data for image recognition, object detection, and facial recognition, and implement a CNN model for handwritten digit recognition within deep learning.
What are hyperparameters,epochs,learing rate and batches?2:10
hyperparameters are fixed, not learned, and include model choices like neurons, layers, and activation functions, and training settings like learning rate, batch, and epochs that shape learning.

Why and what is Notebook(AI Context) ?1:34
Learn how notebooks serve as interactive computing environments that blend code, text, and visualization, with Jupyter notebook as the primary AI development tool supporting multiple languages.
Mastering Jupyter Notebook: A Comprehensive Guide to Navigation3:22
Mastering Jupyter notebook: navigate notebooks, leverage code and markdown cells, view outputs and graphs, manage kernels, rename notebooks, run cells, and organize projects with Anaconda.
Setting Up a Local Anaconda Jupyter Notebook: A Beginner's Guide5:02
Download Link for Anaconda
https://www.anaconda.com/download
Anaconda Navigator Environment Setup: Simplifying Your Data Science Workflow5:47
Set up Anaconda Navigator environments to specify Python versions and libraries, clone and manage environments, then launch Jupyter notebooks to run code, view graphs, and work with code and markdown cells.
Setting Up Your Google Colab Account and Creating Your First Notebook4:22
Description:
Embark on your journey into the world of collaborative data science with our beginner-friendly tutorial on setting up your online Google Colab account and creating your first notebook. Whether you're a student, researcher, or data enthusiast, this step-by-step video guide will empower you to harness the power of cloud-based computing with ease.

In this video, we'll guide you through the process of creating a Google account if you don't already have one, and then seamlessly transition to setting up your Google Colab environment. Learn how to access Google Colab through your web browser, eliminating the need for complex installations and configurations.

Follow along as we demonstrate how to create a new notebook in Google Colab, providing you with a blank canvas to write and execute Python code, conduct data analysis, and explore machine learning models. Discover the intuitive interface of Google Colab, featuring convenient tools for collaboration, version control, and real-time editing.

Unlock the full potential of Google Colab by leveraging its integration with Google Drive, allowing you to store, share, and access your notebooks seamlessly across devices. Plus, explore advanced features such as GPU and TPU acceleration, enabling you to tackle computationally intensive tasks with lightning speed.

Whether you're a beginner or an experienced coder, Google Colab offers a versatile platform for learning, experimentation, and collaboration in the field of data science. Join us as we demystify the setup process and empower you to embark on your data science journey with confidence!
Setting up your runtime to GPU and verifying GPU runtime5:22
Title: Setting Up Runtimes in Google Colab: A Programmatically Verified Guide with GPU Integration

Description:
Unlock the full potential of Google Colab by setting up various runtimes, including GPU, seamlessly and verifying their configurations programmatically. In this comprehensive guide, we'll walk you through the steps to optimize your Colab environment for maximum performance.

First, we'll demonstrate how to access the runtime settings menu in Google Colab, where you can choose between CPU, GPU, or TPU options. Learn how to select the GPU runtime, which accelerates your computations and significantly reduces processing time for machine learning tasks.

Next, we'll guide you through the process of verifying the GPU runtime programmatically within a Colab notebook. You'll discover how to leverage Python libraries such as TensorFlow or PyTorch to detect and utilize the GPU for your computations automatically.

We'll also cover advanced topics, including runtime switching, which allows you to seamlessly switch between different runtime configurations without losing your work. This flexibility is invaluable for experimenting with various hardware setups and optimizing your workflow.

Whether you're a data scientist, machine learning practitioner, or enthusiast, optimizing your runtime configuration in Google Colab is essential for accelerating your workloads and maximizing productivity. With this guide, you'll master the art of setting up runtimes and harnessing the power of GPU acceleration in Google Colab like a pro!
Setting Up Runtimes and Programmatically Verifying TPU Availability in Colab2:15

Description:
Maximize your efficiency in Google Colab by mastering the setup of various runtimes, including the potent TPU (Tensor Processing Unit), and programmatically verifying its availability. In this comprehensive tutorial, we'll guide you through the steps to optimize your Colab environment for top-tier performance.

First, we'll demonstrate how to access the runtime settings menu within Google Colab, where you can select among CPU, GPU, or TPU options. Learn the nuances of choosing the TPU runtime, renowned for its lightning-fast computation speeds, particularly in TensorFlow workloads.

Next, we'll delve into the crucial task of programmatically verifying the availability of a TPU within your Colab notebook. Harness the power of Python libraries like TensorFlow to automatically detect the presence of a TPU and configure your code accordingly, ensuring seamless integration with this cutting-edge hardware.

Additionally, we'll cover advanced techniques such as runtime switching, allowing you to effortlessly transition between different runtime configurations without sacrificing your progress. This adaptability is invaluable for fine-tuning your workflow and optimizing resource utilization.

Whether you're a machine learning practitioner, researcher, or enthusiast, understanding how to leverage TPUs in Google Colab can supercharge your productivity and accelerate your experiments. With this tutorial, you'll gain the expertise to set up runtimes and harness the full potential of TPUs in Google Colab, empowering you to tackle your projects with unprecedented speed and efficiency!
Unleashing the Power of Processing Units:Comparative Analysis of CPU, GPU, TPU6:21
https://sachinkapale.blog/2024/05/03/part-3-demystifying-ai-workloads-choosing-the-right-compute-for-successcpugputpu-and-lpu/

Description:
Ever wondered which processing unit reigns supreme in the world of computing? Join us on an illuminating journey as we dissect the performance characteristics of CPUs, GPUs, TPUs, and LPUs in this comprehensive video analysis.

In this video, we'll delve into the capabilities of each processing unit, exploring their strengths and weaknesses across various computational tasks. From traditional CPU architectures optimized for general-purpose computing to the parallel processing prowess of GPUs and the specialized acceleration of TPUs and LPUs, we'll uncover the unique attributes that define each unit.

Through insightful benchmarks and real-world performance tests, we'll compare the efficiency, speed, and scalability of CPUs, GPUs, TPUs, and LPUs across a spectrum of applications, including machine learning, scientific simulations, and gaming.

Whether you're a developer, researcher, or enthusiast, this video will provide valuable insights into choosing the right processing unit for your computational needs. Join us as we demystify the world of processing units and empower you to harness the full potential of modern computing technology!

What is tensorflow and Keras Libraries?1:00
Explore TensorFlow and Keras, open-source tools from Google Brain for neural networks and deep learning, using Python and single-line code to build and train models with a simplified API.
Downloading dataset and understand type of datasets3:33
Download and validate the MNIST dataset, review its training and validation splits, and analyze data distribution and pixel values to prepare input for a CNN model.
Verifying Training and test dataset2:25
Load and verify the TensorFlow Keras MNIST data, visualize training and test images with Matplotlib, and inspect corresponding labels to verify the dataset for model training.
Matrix representation of image data2:16
Learn how to read a 28 by 28 grayscale image, map each pixel to a 0–255 gray value in an array, and print its matrix representation for visualization.
Normalize pixel data for CNN and add additional dimensions2:16
Normalize pixel values by dividing by 255 to scale images to 0–1, accelerating CNN convergence. Expanddims adds a channel dimension (1 for grayscale, 3 for RGB) to prepare input.
Define Convolution step in the model defining process1:20
Define a cnn model with TensorFlow using a sequential model and a conv2d layer. Apply it to identify edges with a 28 by 1 by 1 input and ReLU activation.
Understand typical CNN Model0:56
Learn how a typical cnn model processes 28 by 28 images using filters to detect edges and generate feature maps. Explore how max pooling and dense layers drive final classification.
Understand Convolution layer matrix multiplication process for edge detections0:29
Explore how a convolutional layer applies a filter via matrix multiplication to produce a smaller feature map, which is then used in max pooling, illustrated with edge-detection examples.
Understand edge detection and remaining layers and set hyperparameters6:02
Explore edge detection with kernels and how a convolutional neural network identifies digits. Implement max pooling, flattening, dense layers with ReLU, and softmax, trained with Adam and sparse categorical cross-entropy.
What is maxpooling?0:32
Explain how max pooling selects the maximum value from four quadrants after applying a filter to pixel values, addressing data loss concerns and outlining the standard method.
Verify and validate model accuracy1:43
Develop a neural network with pooling and flattening to reach around 98–99% accuracy via trial‑and‑error tuning of batch size, layers, and neurons. Visualize training versus validation accuracy to optimize performance.
Saving Model locally and testing local image0:45
Save the model locally to avoid re-running it, then load and test with a local jpeg image from the MNIST dataset. The test predicts the digit eight, confirming proper execution.
Test local sample image0:22
Open the test image on the D drive, drawn with a paintbrush, feed it to the program; the model converts it to the required format and recognizes it.

What is loss or cost function ?3:56
Explain loss or cost function in neural network training, measuring deviation from expected outputs, with multiple formulas (binary/categorical/sparse cross entropy, hinge, huber, cosine loss) guiding weight updates.
What is backpropogation and forward propogation?1:53
Explore how forward propagation flows from input to hidden to output layers, comparing produced outputs with expected results, and how backpropagation adjusts weights and biases to improve accuracy.
What is gradient Descent and type of gradient Descent?4:26
Explore gradient descent and its variants, guiding neural network training toward the global minimum while avoiding local minima by adjusting weights via learning rates and batch strategies.

Requirements

Having a basic understanding of Python programming is a prerequisite for this course, as it serves as the primary language for implementing the concepts taught. Familiarity with Python syntax, data structures, and basic programming concepts will enable you to grasp the course material more effectively and participate in practical exercises. Additionally, proficiency in using Jupyter Notebooks is required as they are commonly used for interactive coding and data analysis in the field of data science and AI. Being comfortable with navigating and executing code in Jupyter Notebooks will facilitate your engagement with course materials and assignments. Furthermore, access to a stable internet connection is essential for accessing course materials, participating in online lectures, and engaging with collaborative activities or discussions. A reliable internet connection ensures uninterrupted learning experiences and timely communication with instructors and peers throughout the duration of the course.

Description

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.

Who this course is 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.

Bootcamp for introduction to Artificial Intelligence

What you'll learn

Explore related topics

Course content

Introduction6 lectures • 27min

Developing your first AI Model10 lectures • 23min

Setup of Local AI Notebook8 lectures • 34min

Hand on CNN -Deep Leaning13 lectures • 24min

Conducting a comprehensive assessment of model completeness ?3 lectures • 10min

Part 2 Hands On CNN Model-Verification and Fine tuning1 lecture • 2min

Requirements

Description

Who this course is for: