Deep Learning Bootcamp Neural Networks With Python, Pytorch
Deep Learning Bootcamp: Neural Networks With Python, Pytorch
Published 11/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 6.00 GB | Duration: 14h 21m
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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