Beginners Guide to Deep Learning

Beginners Guide to Deep Learning, available at $54.99, has an average rating of 4.63, with 27 lectures, based on 4 reviews, and has 24 subscribers.

You will learn about Understand the intuition behind Artificial Neural Networks Apply Artificial Neural Networks in practice Understand the intuition behind Recurrent Neural Networks Understand the intuition behind Convolution Neural Networks Learn how to apply neural networks in several practical examples Build model in tensorflow and keras This course is ideal for individuals who are Students who wants to learn about Deep Learning or Machine-learning enthusiasts or Data scientists who want to expand their library of skills or Scientists and researchers interested in deep learning It is particularly useful for Students who wants to learn about Deep Learning or Machine-learning enthusiasts or Data scientists who want to expand their library of skills or Scientists and researchers interested in deep learning.

Enroll now: Beginners Guide to Deep Learning

Summary

Title: Beginners Guide to Deep Learning

Price: $54.99

Average Rating: 4.63

Number of Lectures: 27

Number of Published Lectures: 27

Number of Curriculum Items: 27

Number of Published Curriculum Objects: 27

Original Price: $74.99

Quality Status: approved

Status: Live

What You Will Learn

Understand the intuition behind Artificial Neural Networks
Apply Artificial Neural Networks in practice
Understand the intuition behind Recurrent Neural Networks
Understand the intuition behind Convolution Neural Networks
Learn how to apply neural networks in several practical examples
Build model in tensorflow and keras

Who Should Attend

Students who wants to learn about Deep Learning
Machine-learning enthusiasts
Data scientists who want to expand their library of skills
Scientists and researchers interested in deep learning

Target Audiences

Students who wants to learn about Deep Learning
Machine-learning enthusiasts
Data scientists who want to expand their library of skills
Scientists and researchers interested in deep learning

If you’re a data scientist familiar with machine learning, this course will provide you with a solid, practical introduction to deep learning, the fastest-growing and most significant subfield of machine learning.

 If you’re a deep-learning expert looking to get started with the Keras framework, you’ll find this course to be the best Keras crash course available.

 If you’re a graduate student studying deep learning in a formal setting, you’ll find this course to be a practical complement to your education, helping you build intuition around the behavior of deep neural networks and familiarizing you with key best practices.

[Note: This course will be updated every weeks with tons of projects, and deep learning concepts]

Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example. Deep learning is a key technology behind driverless cars, enabling them to recognize a stop sign, or to distinguish a pedestrian from a lamppost. It is the key to voice control in consumer devices like phones, tablets, TVs, and hands-free speakers. Deep learning is getting lots of attention lately and for good reason. It’s achieving results that were not possible before.

Deep Learning is a branch of artificial intelligence (AI) focused on building applications that learn from data and improve their accuracy over time without being programmed to do so.

In data science, an algorithm is a sequence of statistical processing steps. In machine learning, algorithms are ‘trained’ to find patterns and features in massive amounts of data in order to make decisions and predictions based on new data. The better the algorithm, the more accurate the decisions and predictions will become as it processes more data.

Deep Learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts.

Google’s AlphaGo program was able to beat a world champion in the strategy game go using deep reinforcement learning.

Topics covered in this course:

1. Building Theoretical Concept for Deep Learning: Neurons, Neural Networks, Activation Function etc

2. Building Practical Concept: Tensor, Tensor Operations, Gradient Descent, Backpropagation etc

3. Neural Networks in Details for Deep Learning building Projects: Movie Review Classification, Newswire classification, house price predictions.

4. Machine Learning concepts for Deep Learning: Data preprocessing, Network size, dropout etc

5. Deep Learning for Computer Vision: Convolution Neural Network

6. Recurrent Neural Network (will be added in 25 Nov, 2022)

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Before you start!!

Lecture 3: Setting up Google Colab

Chapter 2: Building Theoretical Concept

Lecture 1: Neurons Explained!

Lecture 2: Introduction to neural network

Lecture 3: Introduction to Activation Function

Chapter 3: Building Practical Concept

Lecture 1: Introduction to tensor

Lecture 2: Real World Tensor Example

Lecture 3: Tensor Operations

Lecture 4: Introduction to Gradient Optimization

Lecture 5: Gradient Optimization in detail

Lecture 6: BackPropagation

Lecture 7: Example of Neural Network

Chapter 4: Getting started with Neural Network

Lecture 1: Introduction to the section

Lecture 2: Anatomy of Neural Network

Lecture 3: Quick Overview of Keras

Lecture 4: Movie Review Classification Problem

Lecture 5: Classifying NewsWire

Lecture 6: Predicting House Price

Chapter 5: ML concepts for Deep Learning

Lecture 1: Data Preprocessing

Lecture 2: Network size and weight regularization

Lecture 3: Dropout Layer

Lecture 4: Deep Learning Workflow

Chapter 6: Deep Learning for Computer Vision

Lecture 1: Introduction to CNN

Lecture 2: Convolution Layer

Lecture 3: Pooling Layer

Lecture 4: Project: Implementing CNN

Instructors

Sachin Kafle
Founder of CSAMIN & Bit4Stack Tech Inc. [[Author, Teacher]]

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5 stars: 3 votes

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