Python For Deep Learning: Build Neural Networks In Python

Complete Deep Learning Course to Master Data science, Tensorflow, Artificial Intelligence, and Neural Networks

What you'll 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
Requirements
Experience with the basics of coding in Python
Basic mathematical skills
Readiness, flexibility, and passion for learning
Description
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.

Overview

Section 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

Section 2: Artificial Neural Networks (ANN)

Lecture 5 Introduction

Lecture 6 Anatomy and function of neurons

Lecture 7 An introduction to the neural network

Lecture 8 Architecture of a neural network

Section 3: Propagation of information in ANNs

Lecture 9 Feed-forward and Back Propagation Networks

Lecture 10 Backpropagation In Neural Networks

Lecture 11 Minimizing the cost function using backpropagation

Section 4: Neural Network Architectures

Lecture 12 Single layer perceptron (SLP) model

Lecture 13 Radial Basis Network (RBN)

Lecture 14 Multi-layer perceptron (MLP) Neural Network

Lecture 15 Recurrent neural network (RNN)

Lecture 16 Long Short-Term Memory (LSTM) networks

Lecture 17 Hopfield neural network

Lecture 18 Boltzmann Machine Neural Network

Section 5: Activation Functions

Lecture 19 What is the Activation Function?

Lecture 20 Important Terminologies

Lecture 21 The sigmoid function

Lecture 22 Hyperbolic tangent function

Lecture 23 Softmax function

Lecture 24 Rectified Linear Unit (ReLU) function

Lecture 25 Leaky Rectified Linear Unit function

Section 6: Gradient Descent Algorithm

Lecture 26 What is Gradient Decent?

Lecture 27 What is Stochastic Gradient Decent?

Lecture 28 Gradient Decent vs Stochastic Gradient Decent

Section 7: Summary Overview of Neural Networks

Lecture 29 How artificial neural networks work?

Lecture 30 Advantages of Neural Networks

Lecture 31 Disadvantages of Neural Networks

Lecture 32 Applications of Neural Networks

Section 8: Implementation of ANN in Python

Lecture 33 Introduction

Lecture 34 Exploring the dataset

Lecture 35 Problem Statement

Lecture 36 Data Pre-processing

Lecture 37 Loading the dataset

Lecture 38 Splitting the dataset into independent and dependent variables

Lecture 39 Label encoding using scikit-learn

Lecture 40 One-hot encoding using scikit-learn

Lecture 41 Training and Test Sets: Splitting Data

Lecture 42 Feature scaling

Lecture 43 Building the Artificial Neural Network

Lecture 44 Adding the input layer and the first hidden layer

Lecture 45 Adding the next hidden layer

Lecture 46 Adding the output layer

Lecture 47 Compiling the artificial neural network

Lecture 48 Fitting the ANN model to the training set

Lecture 49 Predicting the test set results

Section 9: Convolutional Neural Networks (CNN)

Lecture 50 Introduction

Lecture 51 Components of convolutional neural networks

Lecture 52 Convolution Layer

Lecture 53 Pooling Layer

Lecture 54 Fully connected Layer

Section 10: Implementation of CNN in Python

Lecture 55 Dataset

Lecture 56 Importing libraries

Lecture 57 Building the CNN model

Lecture 58 Accuracy of the model

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