Model Optimization Overview | Codebasics

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Contents

1.1: Course Overview
1.2: Unlock Discord Channels

2.1: Neural Networks: The Foundation of Deep Learning
2.2: Deep Learning vs Statistical ML: When to Use What?
2.3: Neural Network Architectures
2.4: Real World Applications of Deep Learning
2.5: Tooling: PyTorch vs Tensorflow
2.6: Tooling: GPU, TPU
2.7: Quiz
2.8: Chapter Summary

3.1: What is Neuron?
3.2: Perceptron and Multilayer Perceptron (MLP)
3.3: Neural Network Intuition: Insurance Prediction
3.4: The Purpose of Activation Function
3.5: Activation Functions: Sigmoid, ReLU, Tanh, SoftMax
3.6: Quiz
3.7: Chapter Summary

4.1: PyTorch Installation
4.2: Matrix Fundamentals
4.3: PyTorch Tensor Basics 1
4.4: PyTorch Tensor Basics 2
4.5: Derivative and Partial Derivative
4.6: Chain Rule
4.7: Autograd in PyTorch
4.8: Numpy Arrays vs PyTorch Tensors
4.9: Quiz
4.10: Exercise
4.11: Chapter Summary

5.1: Training through Backpropagation
5.2: Gradient Descent: Theoretical Foundation
5.3: Gradient Descent: PyTorch Implementation
5.4: Batch GD vs Mini Batch GD vs SGD
5.5: Mini Batch GD and SGD: PyTorch Implementation
5.6: Quiz
5.7: Exercise
5.8: Chapter Summary

6.1: Employee Bonus Prediction
6.2: Datasets and DataLoaders
6.3: Handwritten Digits Classification
6.4: Classification Report and Confusion Matrix
6.5: Cost Function: Binary Cross Entropy (a.k.a. Log Loss)
6.6: Cost Function: Cross Entropy
6.7: Quiz
6.8: Exercise
6.9: Chapter Summary

7.1: Model Optimization Overview
7.2: Exponentially Weighted Moving Average
7.3: Gradient Descent with Momentum
7.4: RMSprop
7.5: Adam
7.6: Optimizers in Action with Fashion MNIST
7.7: Quiz
7.8: Exercise
7.9: Chapter Summary

8.1: What is Regularization?
8.2: Dropout Regularization
8.3: L1, L2 Regularization
8.4: Batch Normalization
8.5: Early Stopping
8.6: Quiz
8.7: Exercise
8.8: Chapter Summary

9.1: What is Hyperparameter Tuning?
9.2: GridSearchCV, RandomSearchCV
9.3: Optuna
9.4: Quiz
9.5: Exercise
9.6: Chapter Summary

10.1: What is CNN? Convolution, Kernels, Pooling and Beyond
10.2: Padding and Strides
10.3: CIFAR10 Image Classification using CNN
10.4: Data Augmentation
10.5: Transfer Learning
10.6: Pre-trained Models – ResNet, EfficientNet, MobileNet etc.
10.7: Caltech101 Classification Using Transfer Learning
10.8: Quiz
10.9: Exercise
10.10: Chapter Summary

11.1: Sequence Models
11.2: RNN in Depth
11.3: Types of RNN
11.4: Vanishing Gradient Problem
11.5: LSTM
11.6: GRU
11.7: Quiz
11.8: Chapter Summary

12.1: Introduction to Transformer Architecture
12.2: Word Embeddings
12.3: Contextual Embeddings
12.4: Overview of Encoder and Decoder
12.5: Tokenization, Positional Embeddings
12.6: Attention Mechanism
12.7: Multi Headed Attention
12.8: Decoder
12.9: How Transformers are Trained?
12.10: Hugging Face: BERT Basics
12.11: Hugging Face: Spam Classification Using BERT
12.12: Hugging Face: Next Word Prediction Using GPT2
12.13: Quiz
12.14: Exercise
12.15: Chapter Summary

13.1: AtliQ AI’s First Big Client: Vroom Cars
13.2: Problem Statement & SOW
13.3: Data Load and Transformation
13.4: Model Training with CNN
13.5: Model Training with CNN and Regularization
13.6: Model Training using Transfer Learning
13.7: Hyperparameter Tunning using Optuna
13.8: Model Evaluation and Export
13.9: Streamlit App
13.10: FastAPI Server

14.1: Quiz

15.1: Transition to NLP

Instructor

Dhaval Patel

Dhaval Patel

Data Entrepreneur | YouTuber | Ex - Bloomberg, Nvidia

I have 17 years of experience in Programming and Data Science working for big tech companies like NVIDIA and Bloomberg. I also run a famous YouTube channel called Codebasics where I pursue my passion for teaching. Codebasics is one of the top channels on YouTube when it comes to data science, machine learning, data structures, etc. I firmly believe that “Anyone can code” and I use analogies, simple explanations, and step-by-step storytelling to explain difficult concepts in such a way that even a high school student can understand them easily.

Model Optimization Overview

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