Ann | Cnn |Rnn | Rbm | Auto Encoders | Gan | Tensor Flow

Master Basic and Advanced Concepts | Learn Boltzmann Machines, Auto Encoders and Adversarial Networks

What you'll learn

Why we need neural networks?

What is a tensor in tensorflow?

Math behind neural networks

Artificial Neural Network

Convolutional Neural Network

Recurrent Neural Network

Long Short Term Memory

Requirements

Machine Learning

Python Programming

Description

Deep learning is a rapidly growing field of artificial intelligence that has revolutionized the way we approach and solve complex problems. In this course, we will dive into the fundamentals of deep learning, covering the most important concepts and techniques used in the field. We will focus on the three major types of deep neural networksArtificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs) and the three unsupervised deep learning networks like Boltzmann Machines, Auto Encoders and Adversarial Networks.Using TensorFlow, one of the most popular and widely used deep learning libraries, we will explore the architecture and functioning of each type of network, and learn how to build, train, and evaluate them. From recognizing objects in images to processing sequences of data with higher accuracy, deep learning is finding applications across multiple areas in real-world.The program covers both concepts as well as coding related to the neural networks.By the end of this course, you will have a solid understanding of deep learning, and be able to apply these techniques to your own projects. Whether you are a beginner to the field of AI or a seasoned practitioner, this course will equip you with the tools and knowledge you need to advance your skills in deep learning. So, let's get started on our journey to mastering deep learning!

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: Tensor Intro

Lecture 2 Tensor Intro

Lecture 3 Tensor Computations

Section 3: Understanding Deep Learning

Lecture 4 Understanding Deep Learning in Simple Terms

Lecture 5 Activation Function

Lecture 6 Convex Optimization

Lecture 7 ANN

Section 4: Convolution

Lecture 8 Understanding Convolution in CNN

Lecture 9 Deploying a CNN Model

Section 5: RNN

Lecture 10 Why RNNs?

Lecture 11 Math Behind RNN

Lecture 12 LSTM

Lecture 13 Spam Detection - RNN & LSTM

Section 6: ANN Vs CNN Vs RNN

Lecture 14 ANN Vs CNN Vs RNN

Section 7: Unsupervised (Deep) Learning

Lecture 15 Generative Adversarial Network

Lecture 16 Restricted Boltzmann Machines and Deep Belief Networks

Lecture 17 Auto Encoder

Lecture 18 Building The Models

Machine Learning Enthusiasts,Students,Machine Learning Engineers