IIRF Online > Development > Data Science > Deep Learning > Deep Learning & Neural Networks with Python
Deep Learning & Neural Networks with Python by CodingNomads
Course Highlights
- Learn the fundamentals of Deep Learning applications by building, training and deploying PyTorch models from scratch. You’ll work with transfer learning using convolutional neural networks (CNNs) and recurrent neural networks (RNNs) as well as learn how to deploy your models.
Skills you will learn!
Curriculum
4 Topics
About the Deep Learning Course
Deep Learning Course Setup
What is Deep Learning
Feedback: Introduction
24 Topics
Introduction to Neural Networks
Video: Introduction to Neural Networks
Single-variable Linear Regression with Torch
Video: Single Variable Linear Regression
Video: Linear Regression Refresher
Video: Mean Squared Error (MSE)
Video Solution: Exercise 2.1 Visualize Your Data
Mean Squared Error (MSE) Loss Function
Video: scikit-learn Walkthrough
Sklearn Linear Regression
Pytorch Linear Regression
Video: Pytorch Linear Regression
What is Gradient Descent
Link: DS/ML Gradient Descent
Train Your First Torch Model
Video Solution: Exercise 2.5 Train Your Model
Multi Variate Linear Regression
Video: Multi Variate Linear Regression
PyTorch Linear Layer
Non Linearities and Activation Functions
Video: Non Linearities and Activation Functions
Create a Deep Neural Network
Video: Create a Deep Neural Network
Feedback: Linear Regression with Torch
18 Topics
Introduction: Pytorch Tensor
Video Introduction: Pytorch Tensor
What is a Tensor
Video: What is a Tensor
Tensor Scalar Operations
Video: Tensor Scalar Operations
Tensor Element Wise Operations
Video: Tensor Element Wise Operations
Broadcasting with Pytorch
Vector Multiplication: The Dot Product of Two Vectors
Video: Vector Multiplication and Dot Product of Two Vectors
Matrix Multiplication
Video: Matrix Multiplication
Useful Tensor Functions and Methods
Torch Reshape Methods
Create Custom Sigmoid and Softmax Functions
Video: Create Custom Sigmoid and Softmax Functions
Feedback: Tensors and Tensor Operations
14 Topics
Getting Started with the PyTorch Data API
Video: Pytorch Data API
Python Pandas: Create DataFrame In-Memory
Create a PyTorch Dataset
Video: Creating Pytorch Datasets
PyTorch DataLoader
Video: PyTorch Dataloaders
Out-of-Memory Data: Create a fastai Image Dataset
Video: Working with OOM Datasets
CIFAR-10 Dataset & Utility Functions
Video: Image Loading Dataset & Utility Functions
PyTorch DataSet to DataLoader using collate_fn
Video: DataSet to DataLoader with Collate Function
Feedback: PyTorch Data API
11 Topics
Loss Functions
Video: Intro to Loss Functions
Regression: Mean Squared Error
Video: Revisiting Mean Squared Error (MSE)
Classification: Cross Entropy Loss
Video: Cross Entropy Loss
Binary Classification: Binary Cross Entropy
Video: Binary Cross Entropy
Multi-class Classification
Video: Multi-class Classification
Feedback: Loss Functions
14 Topics
Introduction to ML Optimizers
Video: Intro to ML Optimizers
Create a Bivariate Dataset
Create a Small Neural Network
Video: Exercise 6.1 & 6.2 Solution
Stochastic Gradient Descent (SGD)
Video: Stochastic Gradient Descent (SGD)
SGD with Momentum
Video: SGD with Momentum
RMSProp Optimizer
Video: RMSProp Optimizer
Adam Optimizer
Video: Adam Optimizer
Feedback: Optimizers
17 Topics
Introduction: How Machine Learning Models Learn
Video: Intoduction to How Models Learn
Definition of Derivative
Video: Revisiting Derivatives
Example of the Chain Rule Derivative
Video: The Chain Rule & Torch Backprop
Gradient: Partial Derivative
Video: From Derivative to Gradient
Exploding Gradient and Vanishing Gradient Problem
Video: Problems with Training Neural Networks
Improved Parameter Initialization
Video: Improved Parameter Initialization
ReLU Activation Function
Video: Non-Saturating Activation Functions
Batch Normalization
Video: Batch Normalization
Feedback: How Models Learn
12 Topics
Introduction to Classification
Video: Introduction to Classification
Video: GPU Setup
Explore Images with Matplotlib imshow
Video: Explore Images with Matplotlib imshow
Build a Classification Model
Video: Build a Classification Model
How to Train a Classification Model
Train Your Classification Model
Classification Model Evaluation
Video: Train and Evaluate a Classification Model
Feedback: Introduction to Classification
20 Topics
Introduction: Pytorch Lightning and FastAi
Video: Introduction to Pytorch Lightning and FastAi
MNIST Dataset Library
Video: MNIST Dataset Library
MNIST Data Cleaning
Video Solution: MNIST Data Cleaning
Load and Save Dataset
What is Pytorch Lightning
Launch Tensorboard in Google Colab
Video: Introduction to Pytorch Lightning
Pytorch Lightning: DataModule
Pytorch Lightning: LightningModule
Pytorch Lightning: Logging and Callback Functions
Pytorch Lightning: Trainer
ML Model Inference and Checkpoint
What is FastAi
Video: What is FastAi
Fastai DataLoaders
Fastai Learner
Feedback: Introduction to High-Level Libraries
8 Topics
Mini-Project Introduction
Video: Fundamentals Mini-Project Introduction
Mini Project Setup & Instructions
Data Selection EDA and Feature Engineering
Training a Non-Deep Learning Model
Training a Deep Learning Model
Fundamentals Mini Project Report
Feedback: Fundamentals Mini Project
8 Topics
Introduction: Convolutional Neural Networks (CNNs)
CNNs for Edge Detection
CNN: Reflective and Zero Padding
CNN: Strided Convolution
Multi-Channel Convolutional Filter
The Pytorch Conv2d Layer
Average and Max Pooling
Feedback: Introduction to Convolutional Neural Networks (CNNs)
11 Topics
Introduction: Build a Convolutional Neural Network
Video: Image Classification with CNNs
Fastai: MNIST Dataset Cleaning
Video: Fastai MNIST Dataset Cleaning
Convolutional Neural Network Tutorial
Video: Convolutional Neural Network Tutorial
Convolutional Neural Network Training
Video: Convolutional Neural Network Training
Examine Feature Map of CNN Layers
Video: What is Your Model Learning?
Feedback: Our First Convolutional Neural Network (CNN) Image Classifier
13 Topics
Introduction: Transfer Learning
Video: ResNet and Transfer Learning
Pets Data Cleaning
What is ResNet
Video: What is ResNet
ResNet Image Classification
Video: ResNet Image Classification
Transfer Learning: ResNet Pre-trained with ImageNet
Video: Transfer Learning with ResNet and ImageNet
Serve Models with GradIO
Video: Serve Models with GradIO
Build the Residual Block from Scratch (Optional)
Feedback: Transfer Learning and ResNet
10 Topics
Introduction: Image Augmentation
Video: Introduction to Image Augmentation
Image Augmentation with Albumentations
Video: Image Augmentation with Albumentations
Image Augmentation with Torchvision
Video: Image Augmentation with Torchvision
Image Augmentation with Fastai
Video: Image Augmentation with Fastai
Experiment: Is Image Augmentation Worth It?
Feedback: Augmentation
6 Topics
Mini-Project Introduction
Video: Mini-Project Introduction
Project Setup & Instructions
Mixup Data Augmentation Technique
Mini Project Report
Feedback: Image Classification Mini-Project
13 Topics
Introduction: NLP in Deep Learning
Video: Intro Data Prep for NLP
Traditional NLP with Count Vectorizer
Video: Traditional Text Prep for ML
NLP Normalization
Video: Text Normalization
NLP Tokenization
Video: NLP Tokenization
Create a Tokenizer
Video: Create a Tokenizer
Tokenizer Examples
Video: Tokenizer Examples
Feedback: Data Preparation for NLP
18 Topics
Introduction: Recurrent Neural Networks (RNN)
Tokenizer Data Cleaning
Video: Text Classification with RNNs (Intro & Setup)
Word Embedding and Word Vectors
Video: Embeddings Word Vectors and Identifying Bias
Introduction to Recurrent Neural Networks (RNNs)
Video: Understanding Vanilla RNNs
Quiz: What is an RNN
Problems with RNNs
Journal: RNN Problems
Create a Recurrent Neural Network
Video: Problems with RNNs (and Solutions!)
GRU & LSTM Solutions for RNNs
Gated Recurrent Unit (GRU)
Long Short Term Memory (LSTM)
NLP with RNNs for Text Classification
Video: Training an Emotion Classifier with RNNs
Feedback: Introduction to Recurrent Neural Networks
7 Topics
Introduction: Create RNNs from Scratch
Create a RNN from Scratch Tutorial
Video: RNNs from Scratch
Problems with Vanilla RNNs
Gated Recurrent Unit Networks (GRUs)
Long Short-Term Memory (LSTM) Networks
Feedback: Deep Dive - RNNs from Scratch
11 Topics
Introduction: Pre-Training and Transfer Learning with RNNs
Video: Pre-Training and Transfer Learning with RNNs
NLP Dataloaders
Video: Language Modeling Data
Build with Pre-Trained Language Model
Video: Language Modeling
NLP Transfer Learning
Video: Transfer Learning with RNNs
Transfer Learning vs Model from Scratch
Video: Transfer Learning vs Model from Scratch
Feedback: Pre-Training and Transfer Learning for RNNs
2 Topics
Continue Learning NLP
Video: Next Steps with NLP
5 Topics
Introduction: NLP with RNN Mini-Project
Video: Introduction Miniproject 3
Project Setup & Instructions
NLP with RNNs Project Report
Feedback: NLP with RNNs Mini-Project
4 Topics
Introduction: Production Environment
Video: Introduction to Production
Offline and Online Inference
Feedback: Introduction to Production
5 Topics
Introduction: Batch Inference
Crontab Example
Video: Offline Batch Inference
Additional Job Scheduling Resources
Feedback: Batch Inference Lab
5 Topics
Introduction: GradIO User Interface
GradIO: Create Predict Function
Create GradIO User Interface
Video: Create GradIO User Interface
Feedback: Serving with a User Interface
5 Topics
Introduction: Online Interfaces
Video: Introduction to Online Interfaces
Setup: Docker and Cog
Create Cog Predictor and Config File
Launch Online Inference
4 Topics
Feedback: Online Inference
Production Mini Project Instructions
Video: Introduction to Miniproject #4
Feedback: Production Mini-Project
10 Topics
Deep Learning Capstone Introduction
Video: Capstone Project Introduction
Problem Statement
Data Collection
Data Splitting EDA and Feature Engineering
ML Model Training & Selection
Production
Report
Presentation
Feedback: Capstone Project
1 Topic
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Deep Learning & Neural Networks with Python
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