• views: 188
• date: 16 January 2024
• posted by: voska89

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.


Homepage

https://www.udemy.com/course/deep-learning-neural-networks-in-python-using-case-studies/

   
   
   

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