Deep Learning: Neural Networks In Python Using Case Studies

Learn how a neural network is built from basic building blocks using Python

What you’ll learn
Learn how a neural network is built from basic building blocks (the neuron)
Learn how Deep Learning works
Code a neural network from scratch in Python and numpy
Describe different types of neural networks and the different types of problems they are used for

Requirements
Basic math (calculus derivatives, matrix arithmetic, probability)
Install Numpy and Python
Don’t worry about installing TensorFlow, we will do that in the lectures.
Being familiar with the content of my logistic regression course (cross-entropy cost, gradient descent, neurons, XOR, donut) will give you the proper context for this course

Description
Artificial intelligence is growing exponentially. There is no doubt about that. Self-driving cars are clocking up millions of miles, IBM Watson is diagnosing patients better than armies of doctors and Google Deepmind’s AlphaGo beat the World champion at Go – a game where intuition plays a key role. But the further AI advances, the more complex become the problems it needs to solve. And only Deep Learning can solve such complex problems and that’s why it’s at the heart of Artificial intelligence. Deep learning is increasingly dominating technology and has major implications for society. From self-driving cars to medical diagnoses, from face recognition to deep fakes, and from language translation to music generation, deep learning is spreading like wildfire throughout all areas of modern technology. But deep learning is not only about super-fancy, cutting-edge, highly sophisticated applications. Deep learning is increasingly becoming a standard tool in machine-learning, data science, and statistics. Deep learning is used by small startups for data mining and dimension reduction, by governments for detecting tax evasion, and by scientists for detecting patterns in their research data. Deep learning is now used in most areas of technology, business, and entertainment. And it’s becoming more important every year.Learn how Deep Learning works (not just some diagrams and magical black box code)Learn how a neural network is built from basic building blocks (the neuron)Code a neural network from scratch in Python and numpyCode a neural network using Google’s TensorFlowDescribe different types of neural networks and the different types of problems they are used forDerive the backpropagation rule from first principles

Overview
Section 1: Deep Learning: Convolutional Neural Network CNN using Python

Lecture 1 Introduction of Project

Lecture 2 Overview of CNN

Lecture 3 Installations and Dataset Structure

Lecture 4 Import libraries

Lecture 5 CNN Model and Layers Coding

Lecture 6 Data Preprocessing and Augmentation

Lecture 7 Understanding Data generator

Lecture 8 Prediction on Single Image

Lecture 9 Understanding Different Models and Accuracy

Section 2: Deep Learning: Artificial Neural Network ANN using Python

Lecture 10 Introduction of Project

Lecture 11 Setup Environment for ANN

Lecture 12 ANN Installation

Lecture 13 Import Libraries and Data Preprocessing

Lecture 14 Data Preprocessing

Lecture 15 Data Preprocessing Continue

Lecture 16 Data Exploration

Lecture 17 Encoding

Lecture 18 Encoding Continue

Lecture 19 Preparation of Dataset for Training

Lecture 20 Steps to Build ANN Part 1

Lecture 21 Steps to Build ANN Part 2

Lecture 22 Steps to Build ANN Part 3

Lecture 23 Steps to Build ANN Part 4

Lecture 24 Predictions

Lecture 25 Predictions Continue

Lecture 26 Resampling Data with Imbalance-Learn

Lecture 27 Resampling Data with Imbalance-Learn Continue

Section 3: Deep Learning: RNN, LSTM, Stock Price Prognostics using Python

Lecture 28 Introduction of Project

Lecture 29 Installation

Lecture 30 Libraries

Lecture 31 Dataset Explore

Lecture 32 Import Libraries

Lecture 33 Data Preprocessing

Lecture 34 Exploratory Data Analysis

Lecture 35 Exploratory Data Analysis Continue

Lecture 36 Feature Scaling

Lecture 37 Feature Scaling Continue

Lecture 38 More on Feature Scaling

Lecture 39 Building RNN

Lecture 40 Building RNN Continue

Lecture 41 Training of Network

Lecture 42 Prediction on Test Data

Lecture 43 Prediction on Test Data Continue

Lecture 44 Final Result Visualization

Section 4: Deep Learning: Project using Convolutional Neural Network CNN in Python

Lecture 45 Introduction to Project

Lecture 46 Google Collab

Lecture 47 Importing Packages and Data

Lecture 48 Preprocessing and Model Creation

Lecture 49 Training the Model and Prediction

Lecture 50 Model Creation using CNN

Lecture 51 CNN Model Prediction

Students interested in machine learning – you’ll get all the tidbits you need to do well in a neural networks course,Professionals who want to use neural networks in their machine learning and data science pipeline. Be able to apply more powerful models, and know its drawbacks.

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