3.83 out of 5

3.83

3 reviews on Udemy

Python for Deep Learning and Artificial Intelligence

Neural Networks, TensorFlow, ANN, CNN, RNN, LSTM, Transfer Learning and Much More

Instructor:

Laxmi Kant | KGP Talkie

2,311 students enrolled

English [Auto]

The basics of Python programming language

Foundational concepts of deep learning and neural networks

How to build a neural network from scratch using Python

Advanced techniques in deep learning using TensorFlow 2.0

Convolutional neural networks (CNNs) for image classification and object detection

Recurrent neural networks (RNNs) for sequential data such as time series and natural language processing

Generative adversarial networks (GANs) for generating new data samples

Transfer learning in deep learning

Reinforcement learning and its applications in AI

Deployment options for deep learning models

Applications of deep learning in AI, such as computer vision, natural language processing, and speech recognition

The current and future trends in deep learning and AI, as well as ethical and societal implications.

This comprehensive course covers the latest advancements in deep learning and artificial intelligence using Python. Designed for both beginner and advanced students, this course teaches you the foundational concepts and practical skills necessary to build and deploy deep learning models.

Module 1: Introduction to Python and Deep Learning

Overview of Python programming language
Introduction to deep learning and neural networks

Module 2: Neural Network Fundamentals

Understanding activation functions, loss functions, and optimization techniques
Overview of supervised and unsupervised learning

Module 3: Building a Neural Network from Scratch

Hands-on coding exercise to build a simple neural network from scratch using Python

Module 4: TensorFlow 2.0 for Deep Learning

Overview of TensorFlow 2.0 and its features for deep learning
Hands-on coding exercises to implement deep learning models using TensorFlow

Module 5: Advanced Neural Network Architectures

Study of different neural network architectures such as feedforward, recurrent, and convolutional networks
Hands-on coding exercises to implement advanced neural network models

Module 6: Convolutional Neural Networks (CNNs)

Overview of convolutional neural networks and their applications
Hands-on coding exercises to implement CNNs for image classification and object detection tasks

Module 7: Recurrent Neural Networks (RNNs)

Overview of recurrent neural networks and their applications
Hands-on coding exercises to implement RNNs for sequential data such as time series and natural language processing

By the end of this course, you will have a strong understanding of deep learning and its applications in AI, and the ability to build and deploy deep learning models using Python and TensorFlow 2.0. This course will be a valuable asset for anyone looking to pursue a career in AI or simply expand their knowledge in this exciting field.

Course Setup

Course Introduction and How to Download Code Files

Google Colab Introduction

Deep Learning Environment Setup [Optional]

Jupyter Notebook Introduction

Please watch each lecture carefully!

Python for Deep Learning

Python Introduction Part 1

Python Introduction Part 2

Python Introduction Part 3

Numpy Introduction Part 1

Numpy Introduction Part 2

Pandas Introduction

Matplotlib Introduction Part 1

Matplotlib Introduction Part 2

Seaborn Introduction Part 1

Seaborn Introduction Part 2

Introduction to Machine Learning

Classical Machine Learning Introduction

Logistic Regression

Support Vector Machine - SVM

Decision Tree

Random Forest

L2 Regularization

L1 Regularization

Model Evaluation

ROC-AUC Curve

Code Along in Python Part 1

Code Along in Python Part 2

Code Along in Python Part 3

Code Along in Python Part 4

Introduction to Deep Learning and TensorFlow

Machine Learning Process Introduction

Types of Machine Learning

Supervised Learning

Unsupervised Learning

Reinforcement Learning

What is Deep Learning and ML

What is Neural Network

How Deep Learning Process Works

Application of Deep Learning

Deep Learning Tools

MLops with AWS

End to End Deep Learning Project

What is Neuron

Multi-Layer Perceptron

Shallow vs Deep Neural Networks

Activation Function

What is Back Propagation

Optimizers in Deep Learning

Steps to Build Neural Network

Customer Churn Dataset Loading

Data Visualization Part 1

Data Visualization Part 2

Data Preprocessing

Import Neural Networks APIs

How to Get Input Shape and Class Weights

Neural Network Model Building

Model Summary Explanation

Model Training

Model Evaluation

Model Save and Load

Prediction on Real-Life Data

Introduction to Computer Vision with Deep Learning

5 Steps of Computer Vision Model Building

Fashion MNIST Dataset Download

Fashion MNIST Dataset Analysis

Train Test Split for Data

Deep Neural Network Model Building

Model Summary and Training

Discovering Overfitting - Early Stopping

Model Save and Load for Prediction

Introduction to Convolutional Neural Networks [Theory and Intuitions]

What is Convolutional Neural Network?

Working Principle of CNN

Convolutional Filters

Feature Maps

Padding and Strides

Pooling Layers

Activation Function

Dropout

CNN Architectures Comparison

LeNet-5 Architecture Explained

AlexNet Architecture Explained

GoogLeNet (Inception V1) Architecture Explained

RestNet Architecture Explained

MobileNet Architecture Explained

EfficientNet Architecture Explained

Horses vs Humans Classification with Simple CNN

Overview of Image Classification using CNNs

Introduction to TensorFlow Datasets (TFDS)

Download Humans or Horses Dataset Part 1

Download Humans or Horses Dataset Part 2

Use of Image Data Generator

Data Display in Subplots Matrix

CNN Introduction

Building CNN Model

CNN Parameter Calculation

CNN Parameter Calculations Part 2

CNN Parameter Calculations Part 3

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!

3.8

3.8 out of 5

3 Ratings

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