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


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