Python for Deep Learning- Build Neural Networks in Python

Python for Deep Learning: Build Neural Networks in Python, available at $54.99, has an average rating of 4.22, with 59 lectures, based on 1092 reviews, and has 138311 subscribers.

You will learn about Learn the fundamentals of the Deep Learning theory Learn how to use Deep Learning in Python Learn how to use different frameworks in Python to solve real-world problems using deep learning and artificial intelligence Make predictions using linear regression, polynomial regression, and multivariate regression Build artificial neural networks with Tensorflow and Keras This course is ideal for individuals who are Programmers who are looking to add deep learning to their skillset or Professional mathematicians willing to learn how to analyze data programmatically or Any Python programming enthusiast willing to add deep learning proficiency to their portfolio It is particularly useful for Programmers who are looking to add deep learning to their skillset or Professional mathematicians willing to learn how to analyze data programmatically or Any Python programming enthusiast willing to add deep learning proficiency to their portfolio.

Enroll now: Python for Deep Learning: Build Neural Networks in Python

Summary

Title: Python for Deep Learning: Build Neural Networks in Python

Price: $54.99

Average Rating: 4.22

Number of Lectures: 59

Number of Published Lectures: 59

Number of Curriculum Items: 59

Number of Published Curriculum Objects: 59

Original Price: $39.99

Quality Status: approved

Status: Live

What You Will Learn

Learn the fundamentals of the Deep Learning theory

Learn how to use Deep Learning in Python

Learn how to use different frameworks in Python to solve real-world problems using deep learning and artificial intelligence

Make predictions using linear regression, polynomial regression, and multivariate regression

Build artificial neural networks with Tensorflow and Keras

Who Should Attend

Programmers who are looking to add deep learning to their skillset

Professional mathematicians willing to learn how to analyze data programmatically

Any Python programming enthusiast willing to add deep learning proficiency to their portfolio

Target Audiences

Programmers who are looking to add deep learning to their skillset

Professional mathematicians willing to learn how to analyze data programmatically

Any Python programming enthusiast willing to add deep learning proficiency to their portfolio

Python is famed as one of the best programming languages for its flexibility. It works in almost all fields, from web development to developing financial applications. However, it’s no secret that Python’s best application is in deep learning and artificial intelligence tasks.

While Python makes deep learning easy, it will still be quite frustrating for someone with no knowledge of how machine learning works in the first place.

If you know the basics of Python and you have a drive for deep learning, this course is designed for you. This course will help you learn how to create programs that take data input and automate feature extraction, simplifying real-world tasks for humans.

There are hundreds of machine learning resources available on the internet. However, you’re at risk of learning unnecessary lessons if you don’t filter what you learn. While creating this course, we’ve helped with filtering to isolate the essential basics you’ll need in your deep learning journey.

It is a fundamentals course that’s great for both beginners and experts alike. If you’re on the lookout for a course that starts from the basics and works up to the advanced topics, this is the best course for you.

It only teaches what you need to get started in deep learning with no fluff. While this helps to keep the course pretty concise, it’s about everything you need to get started with the topic.

Course Curriculum

Chapter 1: Introduction to Deep Learning

Lecture 1: What is a Deep Learning ?

Lecture 2: Course Materials

Lecture 3: Why is Deep Learning Important?

Lecture 4: Software and Frameworks

Chapter 2: Artificial Neural Networks (ANN)

Lecture 1: Introduction

Lecture 2: Anatomy and function of neurons

Lecture 3: An introduction to the neural network

Lecture 4: Architecture of a neural network

Chapter 3: Propagation of information in ANNs

Lecture 1: Feed-forward and Back Propagation Networks

Lecture 2: Backpropagation In Neural Networks

Lecture 3: Minimizing the cost function using backpropagation

Chapter 4: Neural Network Architectures

Lecture 1: Single layer perceptron (SLP) model

Lecture 2: Radial Basis Network (RBN)

Lecture 3: Multi-layer perceptron (MLP) Neural Network

Lecture 4: Recurrent neural network (RNN)

Lecture 5: Long Short-Term Memory (LSTM) networks

Lecture 6: Hopfield neural network

Lecture 7: Boltzmann Machine Neural Network

Chapter 5: Activation Functions

Lecture 1: What is the Activation Function?

Lecture 2: Important Terminologies

Lecture 3: The sigmoid function

Lecture 4: Hyperbolic tangent function

Lecture 5: Softmax function

Lecture 6: Rectified Linear Unit (ReLU) function

Lecture 7: Leaky Rectified Linear Unit function

Chapter 6: Gradient Descent Algorithm

Lecture 1: What is Gradient Decent?

Lecture 2: What is Stochastic Gradient Decent?

Lecture 3: Gradient Decent vs Stochastic Gradient Decent

Chapter 7: Summary Overview of Neural Networks

Lecture 1: How artificial neural networks work?

Lecture 2: Advantages of Neural Networks

Lecture 3: Disadvantages of Neural Networks

Lecture 4: Applications of Neural Networks

Chapter 8: Implementation of ANN in Python

Lecture 1: Introduction

Lecture 2: Exploring the dataset

Lecture 3: Problem Statement

Lecture 4: Data Pre-processing

Lecture 5: Loading the dataset

Lecture 6: Splitting the dataset into independent and dependent variables

Lecture 7: Label encoding using scikit-learn

Lecture 8: One-hot encoding using scikit-learn

Lecture 9: Training and Test Sets: Splitting Data

Lecture 10: Feature scaling

Lecture 11: Building the Artificial Neural Network

Lecture 12: Adding the input layer and the first hidden layer

Lecture 13: Adding the next hidden layer

Lecture 14: Adding the output layer

Lecture 15: Compiling the artificial neural network

Lecture 16: Fitting the ANN model to the training set

Lecture 17: Predicting the test set results

Chapter 9: Convolutional Neural Networks (CNN)

Lecture 1: Introduction

Lecture 2: Components of convolutional neural networks

Lecture 3: Convolution Layer

Lecture 4: Pooling Layer

Lecture 5: Fully connected Layer

Chapter 10: Implementation of CNN in Python

Lecture 1: Dataset

Lecture 2: Importing libraries

Lecture 3: Building the CNN model

Lecture 4: Accuracy of the model

Chapter 11: BONUS Section – Dont Miss Out

Lecture 1: BONUS Section – Dont Miss Out

Instructors

Meta Brains
Lets code & build the metaverse together!

Skool of AI
Unlock Your AI Potential

Rating Distribution

1 stars: 21 votes

2 stars: 27 votes

3 stars: 171 votes

4 stars: 403 votes

5 stars: 470 votes

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