Pre-requisites

1.1: Deep Learning and Your Career
1.2: The AI family tree
1.3: Pre-requisites
1.4: System Requirements
1.5: Course Overview

2.1: Join Discord for Doubt Clearing Support & Networking
2.2: Setting Video Resolution and Video Speed Globally

3.1: Neural Networks: The Foundation of Deep Learning
3.2: Deep Learning vs Statistical ML: When to Use What?
3.3: Neural Network Architectures
3.4: Real World Applications of Deep Learning
3.5: Tooling: PyTorch vs Tensorflow
3.6: Tooling: GPU, TPU
3.7: Quiz
3.8: Chapter Summary

4.1: What is Neuron?
4.2: Perceptron and Multilayer Perceptron (MLP)
4.3: Neural Network Intuition: Insurance Prediction
4.4: The Purpose of Activation Function
4.5: Activation Functions: Sigmoid, ReLU, Tanh, SoftMax
4.6: Quiz
4.7: Chapter Summary

5.1: PyTorch Installation
5.2: Matrix Fundamentals
5.3: PyTorch Tensor Basics 1
5.4: PyTorch Tensor Basics 2
5.5: Derivative and Partial Derivative
5.6: Chain Rule
5.7: Autograd in PyTorch
5.8: Numpy Arrays vs PyTorch Tensors
5.9: Quiz
5.10: Exercise
5.11: Chapter Summary

6.1: Training through Backpropagation
6.2: Gradient Descent: Theoretical Foundation
6.3: Gradient Descent: PyTorch Implementation
6.4: Batch GD vs Mini Batch GD vs SGD
6.5: Mini Batch GD and SGD: PyTorch Implementation
6.6: Quiz
6.7: Exercise
6.8: Chapter Summary

7.1: Employee Bonus Prediction
7.2: Datasets and DataLoaders
7.3: Handwritten Digits Classification
7.4: Classification Report and Confusion Matrix
7.5: Cost Function: Binary Cross Entropy (a.k.a. Log Loss)
7.6: Cost Function: Cross Entropy
7.7: Quiz
7.8: Exercise
7.9: Chapter Summary

8.1: Model Optimization Overview
8.2: Exponentially Weighted Moving Average
8.3: Gradient Descent with Momentum
8.4: RMSprop
8.5: Adam
8.6: Optimizers in Action with Fashion MNIST
8.7: Quiz
8.8: Exercise
8.9: Chapter Summary

9.1: What is Regularization?
9.2: Dropout Regularization
9.3: L1, L2 Regularization
9.4: Batch Normalization
9.5: Early Stopping
9.6: Quiz
9.7: Exercise
9.8: Chapter Summary

10.1: What is Hyperparameter Tuning?
10.2: GridSearchCV, RandomSearchCV
10.3: Optuna
10.4: Quiz
10.5: Exercise
10.6: Chapter Summary

11.1: What is CNN? Convolution, Kernels, Pooling and Beyond
11.2: Padding and Strides
11.3: CIFAR10 Image Classification using CNN
11.4: Data Augmentation
11.5: Transfer Learning
11.6: Pre-trained Models – ResNet, EfficientNet, MobileNet etc.
11.7: Caltech101 Classification Using Transfer Learning
11.8: Quiz
11.9: Exercise
11.10: Chapter Summary

12.1: Sequence Models
12.2: RNN in Depth
12.3: Types of RNN
12.4: Vanishing Gradient Problem
12.5: LSTM
12.6: GRU
12.7: Quiz
12.8: Chapter Summary

13.1: Introduction to Transformer Architecture
13.2: Word Embeddings
13.3: Contextual Embeddings
13.4: Overview of Encoder and Decoder
13.5: Tokenization, Positional Embeddings
13.6: Attention Mechanism
13.7: Multi Headed Attention
13.8: Decoder
13.9: How Transformers are Trained?
13.10: Hugging Face: BERT Basics
13.11: Hugging Face: Spam Classification Using BERT
13.12: Hugging Face: Next Word Prediction Using GPT2
13.13: Quiz
13.14: Exercise
13.15: Chapter Summary

14.1: AtliQ AI’s First Big Client: Vroom Cars
14.2: Problem Statement & SOW
14.3: Data Load and Transformation
14.4: Model Training with CNN
14.5: Model Training with CNN and Regularization
14.6: Model Training using Transfer Learning
14.7: Hyperparameter Tunning using Optuna
14.8: Model Evaluation and Export
14.9: Streamlit App
14.10: FastAPI Server

15.1: Quiz

Pre-requisites

Prev Next

There are a couple of prerequisites for this course,

Basic Python Programming
Knowledge of Statistical Machine Learning

If you do not have these, then please refer to free resources on the internet or the Codebasics machine learning course.

Contents

1: Welcome to Our Deep Learning Experience

1.1: Deep Learning and Your Career

1.2: The AI family tree

1.3: Pre-requisites

1.4: System Requirements

1.5: Course Overview

2: Enhance your Learning Experience

2.1: Join Discord for Doubt Clearing Support & Networking

2.2: Setting Video Resolution and Video Speed Globally

3: Getting Started

3.1: Neural Networks: The Foundation of Deep Learning

3.2: Deep Learning vs Statistical ML: When to Use What?

3.3: Neural Network Architectures

3.4: Real World Applications of Deep Learning

3.5: Tooling: PyTorch vs Tensorflow

3.6: Tooling: GPU, TPU

3.7: Quiz

3.8: Chapter Summary

4: Neural Networks: Fundamentals

4.1: What is Neuron?

4.2: Perceptron and Multilayer Perceptron (MLP)

4.3: Neural Network Intuition: Insurance Prediction

4.4: The Purpose of Activation Function

4.5: Activation Functions: Sigmoid, ReLU, Tanh, SoftMax

4.6: Quiz

4.7: Chapter Summary

5: Getting Started with PyTorch

5.1: PyTorch Installation

5.2: Matrix Fundamentals

5.3: PyTorch Tensor Basics 1

5.4: PyTorch Tensor Basics 2

5.5: Derivative and Partial Derivative

5.6: Chain Rule

5.7: Autograd in PyTorch

5.8: Numpy Arrays vs PyTorch Tensors

5.9: Quiz

5.10: Exercise

5.11: Chapter Summary

6: Neural Networks: Training

6.1: Training through Backpropagation

6.2: Gradient Descent: Theoretical Foundation

6.3: Gradient Descent: PyTorch Implementation

6.4: Batch GD vs Mini Batch GD vs SGD

6.5: Mini Batch GD and SGD: PyTorch Implementation

6.6: Quiz

6.7: Exercise

6.8: Chapter Summary

7: Neural Networks in PyTorch

7.1: Employee Bonus Prediction

7.2: Datasets and DataLoaders

7.3: Handwritten Digits Classification

7.4: Classification Report and Confusion Matrix

7.5: Cost Function: Binary Cross Entropy (a.k.a. Log Loss)

7.6: Cost Function: Cross Entropy

7.7: Quiz

7.8: Exercise

7.9: Chapter Summary

8: Model Optimization: Training Algorithms

8.1: Model Optimization Overview

8.2: Exponentially Weighted Moving Average

8.3: Gradient Descent with Momentum

8.4: RMSprop

8.5: Adam

8.6: Optimizers in Action with Fashion MNIST

8.7: Quiz

8.8: Exercise

8.9: Chapter Summary

9: Model Optimization: Regularization Techniques

9.1: What is Regularization?

9.2: Dropout Regularization

9.3: L1, L2 Regularization

9.4: Batch Normalization

9.5: Early Stopping

9.6: Quiz

9.7: Exercise

9.8: Chapter Summary

10: Model Optimization: Hyperparameter Tuning

10.1: What is Hyperparameter Tuning?

10.2: GridSearchCV, RandomSearchCV