Pytorch Power: From Zero To Deep Learning Hero – Pytorch

PyTorch and Deep Learning: From Tensors to Deep Neural Networks and CNNs – Build Real-World AI Applications

What you’ll learn
• A solid understanding of tensors and their operations in PyTorch.
• The ability to build and train basic to complex neural networks.
• Knowledge of different loss functions, optimizers, and activation functions.
• Expertise in using DataLoader and transforms for data handling.
• Practical experience in constructing convolutional neural networks.
• A completed project on brain tumor detection from MRI images, showcasing your skills in deep learning and PyTorch.

Requirements
• Basic understanding of Python programming.
• Familiarity with fundamental concepts of machine learning is a plus but not mandatory.

Description
Welcome to “Deep Learning with PyTorch”! This course is designed to take you from the basics of tensors to building complex neural networks, culminating in mastering convolutional neural networks (CNNs) for advanced image recognition tasks. Whether you’re a beginner or an experienced developer, this course offers a comprehensive learning experience.You’ll learn the theory behind neural networks, gain practical coding experience, and test your knowledge with quizzes and activities. Our final project will challenge you to build a CNN for detecting brain tumors from MRI images, preparing you for real-world applications.Enroll now to start your journey in deep learning and PyTorch, and unlock the potential of AI to transform industries and drive innovation.Course ModulesIntroductionIntro to Instructor: Meet your instructor and understand their background and expertise in deep learning and PyTorch.Intro to Course: Get an overview of the course structure, including the flow of theory, practice, quizzes, and activities. Understand the course content, which covers basics of tensors, neural networks with tensors, and CNNs with PyTorch.Why Use a Framework: Learn about the advantages of using frameworks like PyTorch, including autograd and GPU acceleration.Installations: Step-by-step guidance on setting up your development environment, including IDE and Google Colab, to ensure you are ready to code along.Intro to TensorsList vs Array vs Tensor: Understand the differences between lists, arrays, and tensors, and why tensors are crucial for deep learning.Tensor Operations: Learn various operations that can be performed on tensors, from basic manipulations to complex transformations.Math Operations on Tensors: Dive into mathematical operations on tensors, which form the backbone of neural network computations.Autograd: Explore automatic differentiation with autograd, a core feature in PyTorch that simplifies gradient calculations.Check GPU of Notebook: Ensure your setup is optimized for performance by checking GPU availability in your development environment.Neural Networks with TensorsBasic Neural Networks: Build your first neural network using PyTorch and understand its components.Loss Functions: Learn about different loss functions and their importance in training neural networks.Optimizers: Explore various optimization algorithms that help improve your neural network’s performance.Activation Functions: Understand the role of activation functions in introducing non-linearity to neural networks.Data Loader: Master the DataLoader class in PyTorch for efficient data handling and batching.Transforms: Learn how to preprocess and augment data using transforms.Deep Neural Networks (DNN): Scale your knowledge to build deeper and more complex neural networks.CNN with PyTorchConvolution: Understand the concept of convolution and how it applies to image recognition tasks.Pooling: Learn about pooling layers and their role in reducing spatial dimensions.Building a CNN: Step-by-step guide to constructing a convolutional neural network in PyTorch.Final Project: Brain Tumor Detection from MRI Images: Apply everything you’ve learned to build a CNN that detects brain tumors from MRI images. This hands-on project will not only solidify your understanding but also give you a portfolio-worthy piece to showcase your skills.Who Should EnrollThis course is ideal for:Beginners who want to enter the field of deep learning and artificial intelligence.Software developers and data scientists looking to expand their skillset with PyTorch.Machine learning enthusiasts eager to build and deploy neural network models.Anyone interested in applying deep learning to real-world problems and projects.PrerequisitesBasic understanding of Python programming.Familiarity with fundamental concepts of machine learning is a plus but not mandatory.What You’ll GainA solid understanding of tensors and their operations in PyTorch.The ability to build and train basic to complex neural networks.Knowledge of different loss functions, optimizers, and activation functions.Expertise in using DataLoader and transforms for data handling.Practical experience in constructing convolutional neural networks.A completed project on brain tumor detection from MRI images, showcasing your skills in deep learning and PyTorch.Unlock the power of deep learning with PyTorch and take a significant step towards becoming an AI expert. Enroll now and transform your career!

Overview
Section 1: Introduction

Lecture 1 Course Content

Lecture 2 Introduction to Instructor and Aisciences

Lecture 3 Benefits of Framework

Lecture 4 Installations and Setups

Lecture 5 Links for the Course’s Materials and Codes

Section 2: Tensor

Lecture 6 Links for the Course’s Materials and Codes

Lecture 7 Introduction to Tensor

Lecture 8 List vs Array vs Tensor

Lecture 9 Arithmetic Operations

Lecture 10 Tensor Operations

Lecture 11 Auto-Gradiants

Lecture 12 Activity Solution

Lecture 13 Detaching Gradients

Lecture 14 Loading GPU

Section 3: 3-NN with Tensor

Lecture 15 Links for the Course’s Materials and Codes

Lecture 16 Introduction to Module

Lecture 17 Basic NN part 1

Lecture 18 Basic NN part 2

Lecture 19 Loss Functions

Lecture 20 Activation Functions & Hidden Layers

Lecture 21 Optimizers

Lecture 22 Data Loader & Dataset

Lecture 23 Activity

Lecture 24 Activity Solution

Lecture 25 Formating the Output

Lecture 26 Graph for Loss

Section 4: CNN

Lecture 27 Links for the Course’s Materials and Codes

Lecture 28 Introduction to Module

Lecture 29 CNN vs NN

Lecture 30 Introduction to Convolution

Lecture 31 Convolution Animations

Lecture 32 Convolution using Pytorch

Lecture 33 Introduction to Pooling

Lecture 34 Pooling using Numpy

Lecture 35 Pooling in Pytorch

Lecture 36 Introduction to Project

Lecture 37 Project (Data Loading)

Lecture 38 Project (Transforms)

Lecture 39 Project (DataLoaders)

Lecture 40 Project (CNN Architect)

Lecture 41 Project (Forward Propagation)

Lecture 42 Project (Training CNN)

Lecture 43 Project (Analyzing Model Output)

Lecture 44 Project (Making Predictions)

• Beginners who want to enter the field of deep learning and artificial intelligence.,• Software developers and data scientists looking to expand their skillset with PyTorch.,• Machine learning enthusiasts eager to build and deploy neural network models.,• Anyone interested in applying deep learning to real-world problems and projects.

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