Deep Learning Bootcamp: Neural Networks With Python, Pytorch

Master Neural Networks, DNNs, and CNNs with Python, PyTorch, and TensorFlow in this all-in-one Deep Learning Bootcamp.

What you’ll learn
• The basics of Machine Learning.
• The basics of Neural Networks.
• The basics of training a Deep Neural Network (DNN) using Gradient Descent Algorithm.
• Using Deep Learning for IRIS dataset.
• 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.
• A completed project on brain tumor detection from MRI images, showcasing your skills in deep learning and PyTorch.
• A Solid Grasp of TensorFlow Basics
• Hands-on Experience in Building Deep Learning Models
• Knowledge of Model Training, Evaluation, and Optimization
• Confidence to Explore More Complex AI and Machine Learning Projects

Requirements
• No prior knowledge of Deep Learning or Math is needed. You will start from the basics and build your knowledge of the subject step by step.
• Basic understanding of Python programming.
No prior experience with TensorFlow is required, but a basic understanding of machine learning concepts and Python will be helpful.

Description
Are you ready to unlock the full potential of Deep Learning and AI by mastering not just one but multiple tools and frameworks? This comprehensive course will guide you through the essentials of Deep Learning using Python, PyTorch, and TensorFlow—the most powerful libraries and frameworks for building intelligent models.Whether you’re a beginner or an experienced developer, this course offers a step-by-step learning experience that combines theoretical concepts with practical hands-on coding. By the end of this journey, you’ll have developed a deep understanding of neural networks, gained proficiency in applying Deep Neural Networks (DNNs) to solve real-world problems, and built expertise in cutting-edge deep learning applications like Convolutional Neural Networks (CNNs) and brain tumor detection from MRI images.Why Choose This Course?This course stands out by offering a comprehensive learning path that merges essential aspects from three leading frameworks: Python, PyTorch, and TensorFlow. With a strong emphasis on hands-on practice and real-world applications, you’ll quickly advance from fundamental concepts to mastering deep learning techniques, culminating in the creation of sophisticated AI models.Key Highlights:Python: Learn Python from the basics, progressing to advanced-level programming essential for implementing deep learning algorithms.PyTorch: Master PyTorch for neural networks, including tensor operations, optimization, autograd, and CNNs for image recognition tasks.TensorFlow: Unlock TensorFlow’s potential for creating robust deep learning models, utilizing tools like Tensorboard for model visualization.Real-world Projects: Apply your knowledge to exciting projects like IRIS classification, brain tumor detection from MRI images, and more.Data Preprocessing & ML Concepts: Learn crucial data preprocessing techniques and key machine learning principles such as Gradient Descent, Back Propagation, and Model Optimization.Course Content Overview:Module 1: Introduction to Deep Learning and PythonIntroduction to the course structure, learning objectives, and key frameworks.Overview of Python programming: from basics to advanced, ensuring you can confidently implement any deep learning concept.Module 2: Deep Neural Networks (DNNs) with Python and NumPyProgramming with Python and NumPy: Understand arrays, data frames, and data preprocessing techniques.Building DNNs from scratch using NumPy.Implementing machine learning algorithms, including Gradient Descent, Logistic Regression, Feed Forward, and Back Propagation.Module 3: Deep Learning with PyTorchLearn about tensors and their importance in deep learning.Perform operations on tensors and understand autograd for automatic differentiation.Build basic and complex neural networks with PyTorch.Implement CNNs for advanced image recognition tasks.Final Project: Brain Tumor Detection using MRI Images.Module 4: Mastering TensorFlow for Deep LearningDive into TensorFlow and understand its core features.Build your first deep learning model using TensorFlow, starting with a simple neuron and progressing to Artificial Neural Networks (ANNs).TensorFlow Playground: Experiment with various models and visualize performance.Explore advanced deep learning projects, learning concepts like gradient descent, epochs, backpropagation, and model evaluation.Who Should Take This Course?Aspiring Data Scientists and Machine Learning Enthusiasts eager to develop deep expertise in neural networks.Software Developers looking to expand their skillset with PyTorch and TensorFlow.Business Analysts and AI Enthusiasts interested in applying deep learning to real-world problems.Anyone passionate about learning how deep learning can drive innovation across industries, from healthcare to autonomous driving.What You’ll Learn:Programming with Python, NumPy, and Pandas for data manipulation and model development.How to build and train Deep Neural Networks and Convolutional Neural Networks using PyTorch and TensorFlow.Practical deep learning applications like brain tumor detection and IRIS classification.Key machine learning concepts, including Gradient Descent, Model Optimization, and more.How to preprocess and handle data efficiently using tools like DataLoader in PyTorch and Transforms for data augmentation.Hands-on Experience:By the end of this course, you will not only have learned the theory but will also have built multiple deep learning models, gaining hands-on experience in real-world projects.

Overview
Section 1: Deep Learning:Deep Neural Network for Beginners Using Python

Lecture 1 Promo & Highlights

Lecture 2 Introduction: Introduction to Instructor and Aisciences

Lecture 3 Links for the Course’s Materials and Codes

Lecture 4 Basics of Deep Learning: Problem to Solve Part 1

Lecture 5 Basics of Deep Learning: Problem to Solve Part 2

Lecture 6 Basics of Deep Learning: Problem to Solve Part 3

Lecture 7 Basics of Deep Learning: Linear Equation

Lecture 8 Basics of Deep Learning: Linear Equation Vectorized

Lecture 9 Basics of Deep Learning: 3D Feature Space

Lecture 10 Basics of Deep Learning: N Dimensional Space

Lecture 11 Basics of Deep Learning: Theory of Perceptron

Lecture 12 Basics of Deep Learning: Implementing Basic Perceptron

Lecture 13 Basics of Deep Learning: Logical Gates for Perceptrons

Lecture 14 Basics of Deep Learning: Perceptron Training Part 1

Lecture 15 Basics of Deep Learning: Perceptron Training Part 2

Lecture 16 Basics of Deep Learning: Learning Rate

Lecture 17 Basics of Deep Learning: Perceptron Training Part 3

Lecture 18 Basics of Deep Learning: Perceptron Algorithm

Lecture 19 Basics of Deep Learning: Coading Perceptron Algo (Data Reading & Visualization)

Lecture 20 Basics of Deep Learning: Coading Perceptron Algo (Perceptron Step)

Lecture 21 Basics of Deep Learning: Coading Perceptron Algo (Training Perceptron)

Lecture 22 Basics of Deep Learning: Coading Perceptron Algo (Visualizing the Results)

Lecture 23 Basics of Deep Learning: Problem with Linear Solutions

Lecture 24 Basics of Deep Learning: Solution to Problem

Lecture 25 Basics of Deep Learning: Error Functions

Lecture 26 Basics of Deep Learning: Discrete vs Continuous Error Function

Lecture 27 Basics of Deep Learning: Sigmoid Function

Lecture 28 Basics of Deep Learning: Multi-Class Problem

Lecture 29 Basics of Deep Learning: Problem of Negative Scores

Lecture 30 Basics of Deep Learning: Need of Softmax

Lecture 31 Basics of Deep Learning: Coding Softmax

Lecture 32 Basics of Deep Learning: One Hot Encoding

Lecture 33 Basics of Deep Learning: Maximum Likelihood Part 1

Lecture 34 Basics of Deep Learning: Maximum Likelihood Part 2

Lecture 35 Basics of Deep Learning: Cross Entropy

Lecture 36 Basics of Deep Learning: Cross Entropy Formulation

Lecture 37 Basics of Deep Learning: Multi Class Cross Entropy

Lecture 38 Basics of Deep Learning: Cross Entropy Implementation

Lecture 39 Basics of Deep Learning: Sigmoid Function Implementation

Lecture 40 Basics of Deep Learning: Output Function Implementation

Lecture 41 Deep Learning: Introduction to Gradient Decent

Lecture 42 Deep Learning: Convex Functions

Lecture 43 Deep Learning: Use of Derivatives

Lecture 44 Deep Learning: How Gradient Decent Works

Lecture 45 Deep Learning: Gradient Step

Lecture 46 Deep Learning: Logistic Regression Algorithm

Lecture 47 Deep Learning: Data Visualization and Reading

Lecture 48 Deep Learning: Updating Weights in Python

Lecture 49 Deep Learning: Implementing Logistic Regression

Lecture 50 Deep Learning: Visualization and Results

Lecture 51 Deep Learning: Gradient Decent vs Perceptron

Lecture 52 Deep Learning: Linear to Non Linear Boundaries

Lecture 53 Deep Learning: Combining Probabilities

Lecture 54 Deep Learning: Weighted Sums

Lecture 55 Deep Learning: Neural Network Architecture

Lecture 56 Deep Learning: Layers and DEEP Networks

Lecture 57 Deep Learning: Multi Class Classification

Lecture 58 Deep Learning: Basics of Feed Forward

Lecture 59 Deep Learning: Feed Forward for DEEP Net

Lecture 60 Deep Learning: Deep Learning Algo Overview

Lecture 61 Deep Learning: Basics of Back Propagation

Lecture 62 Deep Learning: Updating Weights

Lecture 63 Deep Learning: Chain Rule for BackPropagation

Lecture 64 Deep Learning: Sigma Prime

Lecture 65 Deep Learning: Data Analysis NN Implementation

Lecture 66 Deep Learning: One Hot Encoding (NN Implementation)

Lecture 67 Deep Learning: Scaling the Data (NN Implementation)

Lecture 68 Deep Learning: Splitting the Data (NN Implementation)

Lecture 69 Deep Learning: Helper Functions (NN Implementation)

Lecture 70 Deep Learning: Training (NN Implementation)

Lecture 71 Deep Learning: Testing (NN Implementation)

Lecture 72 Optimizations: Underfitting vs Overfitting

Lecture 73 Optimizations: Early Stopping

Lecture 74 Optimizations: Quiz

Lecture 75 Optimizations: Solution & Regularization

Lecture 76 Optimizations: L1 & L2 Regularization

Lecture 77 Optimizations: Dropout

Lecture 78 Optimizations: Local Minima Problem

Lecture 79 Optimizations: Random Restart Solution

Lecture 80 Optimizations: Vanishing Gradient Problem

Lecture 81 Optimizations: Other Activation Functions

Lecture 82 Final Project: Final Project Part 1

Lecture 83 Final Project: Final Project Part 2

Lecture 84 Final Project: Final Project Part 3

Lecture 85 Final Project: Final Project Part 4

Lecture 86 Final Project: Final Project Part 5

Section 2: PyTorch Power: From Zero to Deep Learning Hero – PyTorch

Lecture 87 Links for the Course’s Materials and Codes

Lecture 88 Introduction: Module Content

Lecture 89 Introduction: Benefits of Framework

Lecture 90 Introduction: Installations and Setups

Lecture 91 Tensor: Introduction to Tensor

Lecture 92 Tensor: List vs Array vs Tensor

Lecture 93 Tensor: Arithmetic Operations

Lecture 94 Tensor: Tensor Operations

Lecture 95 Tensor: Auto-Gradiants

Lecture 96 Tensor: Activity Solution

Lecture 97 Tensor: Detaching Gradients

Lecture 98 Tensor: Loading GPU

Lecture 99 NN with Tensor: Introduction to Module

Lecture 100 NN with Tensor: Basic NN part 1

Lecture 101 NN with Tensor: Basic NN part 2

Lecture 102 NN with Tensor: Loss Functions

Lecture 103 NN with Tensor: Activation Functions & Hidden Layers

Lecture 104 NN with Tensor: Optimizers

Lecture 105 NN with Tensor: Data Loader & Dataset

Lecture 106 NN with Tensor: Activity

Lecture 107 NN with Tensor: Activity Solution

Lecture 108 NN with Tensor: Formating the Output

Lecture 109 NN with Tensor: Graph for Loss

Lecture 110 CNN: Introduction to Module

Lecture 111 CNN: CNN vs NN

Lecture 112 CNN: Introduction to Convolution

Lecture 113 CNN: Convolution Animations

Lecture 114 CNN: Convolution using Pytorch

Lecture 115 CNN: Introduction to Pooling

Lecture 116 CNN: Pooling using Numpy

Lecture 117 CNN: Pooling in Pytorch

Lecture 118 CNN: Introduction to Project

Lecture 119 CNN: Project (Data Loading)

Lecture 120 CNN: Project (Transforms)

Lecture 121 CNN: Project (DataLoaders)

Lecture 122 CNN: Project (CNN Architect)

Lecture 123 CNN: Project (Forward Propagation)

Lecture 124 CNN: Project (Training CNN)

Lecture 125 CNN: Project (Analyzing Model Output)

Lecture 126 CNN: Project (Making Predictions)

Section 3: TensorFlow Fundamentals: From Basics to Brilliant AI Project

Lecture 127 Links for the Course’s Materials and Codes

Lecture 128 Introduction to TensorFlow: Module Introduction

Lecture 129 Introduction to TensorFlow: TensorFlow Definition and Properties

Lecture 130 Introduction to TensorFlow: Tensor Types and Tesnor Board

Lecture 131 Introduction to TensorFlow: How to use TensorFlow

Lecture 132 Introduction to TensorFlow: Google Colab

Lecture 133 Introduction to TensorFlow: Exercise

Lecture 134 Introduction to TensorFlow: Exercise Solution

Lecture 135 Introduction to TensorFlow: Quiz

Lecture 136 Introduction to TensorFlow: Quiz Solution

Lecture 137 Building your first deep learning Project: Module Introduction

Lecture 138 Building your first deep learning Project: ANNs

Lecture 139 Building your first deep learning Project: TensorFlow Playground

Lecture 140 Building your first deep learning Project: Load TF and Data

Lecture 141 Building your first deep learning Project: Model Training and Evaluation

Lecture 142 Building your first deep learning Project: Project

Lecture 143 Building your first deep learning Project: Project Implementation

Lecture 144 Building your first deep learning Project: Quiz

Lecture 145 Building your first deep learning Project: Quiz Solution

Lecture 146 Multi-layer Deep Learning Project: Module Introduction

Lecture 147 Multi-layer Deep Learning Project: Training and Epochs

Lecture 148 Multi-layer Deep Learning Project: Gradient Decent and Back Propagation

Lecture 149 Multi-layer Deep Learning Project: Bias Variance Trade-Off

Lecture 150 Multi-layer Deep Learning Project: Performance Metrics

Lecture 151 Multi-layer Deep Learning Project: Project-Sales Predition

Lecture 152 Multi-layer Deep Learning Project: Quiz

Lecture 153 Multi-layer Deep Learning Project: Quiz Solution

• Anyone interested in Data Science.,• People who want to master DNNs with real datasets in Deep Learning.,• People who want to implement DNNs in realistic projects.,• Software developers and data scientists looking to expand their skillset with PyTorch.,• Beginners who want to enter the field of deep learning and artificial intelligence.,• Anyone Curious About Deep Learning and TensorFlow

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