Deep Learning For Professionals

Learn the fundamentals of deep learning using real world solutions

What you'll learn
Learn the core concepts of deep Learning
Learn the structure of deep learning algorithms
Learn to build deep learning pipeline using Python and Tensorflow
Implement a neural network solution from ground up
Requirements
Basic knowledge of Python and machine learning algorithms is required to complete this course
Description
Do you want to accelerate your machine learning career with a new skill?We brought you the professional course on Deep Learning covering the latest concepts and skills required in the market today.In this course, you'll learn fundamental concepts of Deep Learning, including various Neural Networks designs and ingredients of deep learning algorithms. This course will help you learn how to implement a deep-learning pipeline using TensorFlow and Python.Deep learning is a very important aspect of machine learning. Deep learning is used for real-world scenarios such as object recognition, computer vision, image and video processing, text analytics, recommender systems, and other types of classifiers.Major Topics That This Deep Learning Course Covers!Introduction to the Structure of a DL ResearchBasic Ingredients of a Deep Learning AlgorithmImplementing DL Pipeline in TensorFlowDeep dive – NN designWhy Should You Learn The Deep Learning?Deep learning has got approval from all major business functions from customer service to cybersecurity and marketing. It's helping in the new age of personalization, fraud detection, forecasting, and even supply chain optimization.Perks Of Availing This Program!Get Well-Structured ContentStep-By-Step Building of Deep Learning Research StructureLearn From Industry ExpertsGet a Certificate of CompletionJoin today and be market ready!!See You In The Class!

Overview

Section 1: Course Introduction

Lecture 1 Course Introduction

Section 2: Introduction to the Structure of a DL Research

Lecture 2 Section Introduction

Lecture 3 What is Artificial Intelligence?

Lecture 4 Ethical Implications of Artificial Intelligence

Lecture 5 Introduction to Machine Learning

Lecture 6 Types of Machine Learning

Lecture 7 What is Deep Learning?

Lecture 8 Deep Learning - Real World Applications

Lecture 9 Hardware Requirement

Lecture 10 Resources for Project

Lecture 11 Visualize Neural Networks

Lecture 12 Summary

Section 3: Basic Ingredients of a Deep Learning Algorithm

Lecture 13 Section Introduction

Lecture 14 Probability in Deep Learning

Lecture 15 Calculus in Deep Learning

Lecture 16 Chain Rule in Deep Learning

Lecture 17 Math in Neural Network

Lecture 18 Partial Derivatives

Lecture 19 Bayes Theorem

Lecture 20 Visualizing Gradient Descent

Lecture 21 Overfitting

Lecture 22 Underfitting

Lecture 23 Cross Validation

Lecture 24 Activation Functions

Lecture 25 Implement Gradient Descent

Lecture 26 Hyperparameter Tuning - 1

Lecture 27 Hyperparameter Tuning - 2

Lecture 28 Optimizers

Lecture 29 Decision Tree

Lecture 30 Precision and Recall

Lecture 31 Data Cleaning

Lecture 32 Principle Component Analysis

Lecture 33 Summary

Section 4: Implementing DL Pipeline in TensorFlow

Lecture 34 Section Introduction

Lecture 35 Introduction to Exploratory Data Analysis

Lecture 36 Implementing Exploratory Data Analysis

Lecture 37 Handling Missing Values

Lecture 38 Features of Exploratory Data Analysis

Lecture 39 Introduction to Tensorflow

Lecture 40 Different Types of Tensors

Lecture 41 Comparing different versions of Tensorflow

Lecture 42 Data Augmentation

Lecture 43 Implement Image Augmentation

Lecture 44 Implement Gradient Tape

Lecture 45 Summary

Section 5: Deep dive – NN design

Lecture 46 Section Introduction

Lecture 47 Convolutional Neural Network

Lecture 48 Dot Product

Lecture 49 Vanishing and Exploding gradients

Lecture 50 Residual Neural Networks

Lecture 51 Recurrent Neural Networks

Lecture 52 Implement Convolutional Neural Network

Lecture 53 Summary

Anyone who wants to learn real world deep learning will find this course very useful