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75

Computer Vision Applications with Deep Learning

Bridge the Gap Between the Basic CNN & Modern Architectures Including VGG, ResNet, and Inception

By Lazy Programmer | in Online Courses

In this course, you’ll see how you can turn a CNN into an object detection system, that not only classifies images but can locate each object in an image and predict its label. You could imagine that such a task is a basic prerequisite for self-driving vehicles. With 75 lectures, you'll be looking at SSD, neural style transfer, and facial recognition. Sign up and learn these advanced applications of CNNs.

  • Access 75 lectures & 7 hours of content 24/7
  • See how a CNN can be turned into an object detection system
  • Learn about a state-of-the-art algorithm called SSD
  • Understand the process of neural style transfer
  • Become informed & aware about facial recognition

Instructor

The Lazy Programmer is a data scientist, big data engineer, and full-stack software engineer. He has taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Humber College, and The New School. Multiple businesses have benefitted from their web programming expertise. He does all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies he has used are Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases, he has used MySQL, Postgres, Redis, MongoDB, and more.

Important Details

  • Length of time users can access this course: lifetime
  • Access options: web & mobile streaming
  • Certification of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Updates included
  • Experience level required: intermediate

Requirements

  • Know how to build, train & use a CNN using some library (preferably in Python)
  • Understand basic theoretical concepts behind convolution & neural networks
  • Decent Python coding skills, preferably in data science & the Numpy Stack

Course Outline

  • Welcome
    • Introduction - 2:35
    • Outline and Perspective - 6:49
    • Where to get help - 2:06
  • Review
    • Review of CNNs - 10:34
    • Where to get the code and data - 2:26
    • Fashion MNIST - 3:29
    • Review of CNNs in Code - 6:09
  • VGG and Transfer Learning
    • VGG Section Intro - 3:04
    • What's so special about VGG? - 7:00
    • Transfer Learning - 8:22
    • Relationship to Greedy Layer-Wise Pretraining - 2:19
    • Getting the data - 2:17
    • Code pt 1 - 9:23
    • Code pt 2 - 3:41
    • Code pt 3 - 3:27
    • VGG Section Summary - 1:47
  • ResNet (and Inception)
    • ResNet Section Intro - 2:49
    • ResNet Architecture - 12:45
    • Building ResNet - Strategy - 2:25
    • Building ResNet - Conv Block Details - 3:34
    • Building ResNet - Conv Block Code - 6:08
    • Building ResNet - Identity Block Details - 1:23
    • Building ResNet - First Few Layers - 2:27
    • Building ResNet - First Few Layers (Code) - 4:15
    • Building ResNet - Putting it all together - 4:19
    • Exercise: Apply ResNet - 1:16
    • Applying ResNet - 2:39
    • 1x1 Convolutions - 4:03
    • Optional: Inception - 6:47
    • Different sized images using the same network - 4:12
    • ResNet Section Summary - 2:27
  • Object Detection (SSD)
    • SSD Section Intro - 5:04
    • Object Localization - 6:36
    • What is Object Detection? - 2:53
    • How would you find an object in an image? - 8:40
    • The Problem of Scale - 3:47
    • The Problem of Shape - 3:52
    • SSD in Tensorflow - 9:57
    • Modifying SSD to work on Video - 5:04
    • Optional: Intersection over Union & Non-max Suppression - 5:06
    • SSD Section Summary - 2:52
  • Neural Style Transfer
    • Style Transfer Section Intro - 2:52
    • Style Transfer Theory - 11:23
    • Optimizing the Loss - 8:02
    • Code pt 1 - 7:46
    • Code pt 2 - 7:13
    • Code pt 3 - 3:50
    • Style Transfer Section Summary - 2:21
  • Facial Recognition
    • Facial Recognition Section Introduction - 3:38
    • Siamese Networks - 10:17
    • Code Outline - 5:01
    • Loading in the data - 4:40
    • Splitting the data into train and test - 4:24
    • Converting the data into pairs - 5:02
    • Generating Generators - 4:20
    • Creating the model and loss - 3:12
    • Accuracy and imbalanced classes - 7:07
    • Facial Recognition Section Summary - 3:28
  • Basics Review
    • (Review) Tensorflow Basics - 7:27
    • (Review) Tensorflow Neural Network in Code - 9:43
    • (Review) Keras Discussion - 6:48
    • (Review) Keras Neural Network in Code - 6:37
    • (Review) Keras Functional API - 4:26
  • Appendix
    • What is the Appendix? - 2:48
    • Windows-Focused Environment Setup 2018 - 20:20
    • How to How to install Numpy, Theano, Tensorflow, etc... - 17:30
    • Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced? - 22:04
    • How to Succeed in this Course (Long Version) - 10:24
    • How to Code by Yourself (part 1) - 15:54
    • How to Code by Yourself (part 2) - 9:23
    • Proof that using Jupyter Notebook is the same as not using it - 12:29
    • Python 2 vs Python 3 - 4:38
    • What order should I take your courses in? (part 1) - 11:18
    • What order should I take your courses in? (part 2) - 16:07
    • Where to get discount coupons and FREE deep learning material - 2:20

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8 hours
Lessons
65

Advanced NLP & Sequence Models with Deep Learning

Understand API & Build Applications for Text Classification, Neural Machine Translation, and Stock Prediction

By Lazy Programmer | in Online Courses

You've learned about some of the fundamental building blocks of Deep NLP such as RNNs, CNNs, and word embedding algorithms such as word2vec and GloVe. With 65 lectures, this course will take you to a higher system level of thinking. Since you know how these things work, it’s time to build systems using these components. At the end of this course, you'll be able to build applications for problems like text classification, neural machine translation, stock prediction.

  • Access 65 lectures & 8 hours of content 24/7
  • Visualize what's happening in a machine learning model internally
  • Take a look at some advanced Deep NLP techniques: bidirectional RNNs, seq2seq & attention
  • Build applications for problems like text classification, neural machine translation & stock prediction

Instructor

The Lazy Programmer is a data scientist, big data engineer, and full-stack software engineer. He has taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Humber College, and The New School. Multiple businesses have benefitted from their web programming expertise. He does all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies he has used are Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases, he has used MySQL, Postgres, Redis, MongoDB, and more.

Important Details

  • Length of time users can access this course: lifetime
  • Access options: web & mobile streaming
  • Certification of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Updates included
  • Experience level required: intermediate

Requirements

  • Decent Python coding skills
  • Understand RNNs, CNNs & word embeddings
  • Know how to build, train & evaluate a neural network in Keras

Course Outline

  • Welcome to Advanced NLP and RNNs
    • Introduction - 2:41
    • Outline - 3:40
    • Where to get the code - 4:45
    • Where to get help - 2:06
  • Review of Recurrent Neural Networks, Convolutional Neural Networks, and Word Embeddings
    • Review Section Introduction - 4:24
    • What is a word embedding? - 15:10
    • Using word embeddings - 4:33
    • What is a CNN? - 13:36
    • Where to get the data - 5:06
    • CNN Code (part 1) - 15:08
    • CNN Code (part 2) - 6:14
    • What is an RNN? - 13:11
    • GRUs and LSTMs - 10:47
    • Different Types of RNN Tasks - 12:27
    • A Simple RNN Experiment - 6:29
    • RNN Code - 3:25
    • Review Section Summary - 4:49
  • Bidirectional RNNs
    • Bidirectional RNNs Motivation - 8:31
    • Bidirectional RNN Experiment - 5:09
    • Bidirectional RNN Code - 2:33
    • Image Classification with Bidirectional RNNs - 6:12
    • Image Classification Code - 5:45
    • Bidirectional RNNs Section Summary - 2:36
  • Sequence-to-sequence models (Seq2Seq)
    • Seq2Seq Theory - 7:29
    • Seq2Seq Applications - 3:27
    • Decoding in Detail and Teacher Forcing - 6:47
    • Poetry Revisited - 3:28
    • Poetry Revisited Code 1 - 8:29
    • Poetry Revisited Code 2 - 6:58
    • Seq2Seq in Code 1 - 7:55
    • Seq2Seq in Code 2 - 5:14
    • Seq2Seq Section Summary - 3:04
  • Attention
    • Attention Section Introduction - 2:28
    • Attention Theory - 18:04
    • Teacher Forcing - 2:09
    • Helpful Implementation Details - 11:21
    • Attention Code 1 - 9:48
    • Attention Code 2 - 3:50
    • Visualizing Attention - 2:26
    • Building a Chatbot without any more Code - 10:31
    • Attention Section Summary - 3:33
  • Stock Predictions
    • Stock Predictions Section Introduction - 4:51
    • Making the Dataset - 5:19
    • Forecasting - 7:29
    • A Simple Time Series - 9:58
    • Naive Forecast - 8:27
    • Stock Prediction (pt 1) - 3:45
    • Stock Prediction (pt 2) - 6:04
    • Stock Prediction (pt 3) - 4:50
    • Stock Prediction (pt 4) - 2:02
    • Stock Predictions Section Summary - 5:35
  • Basics Review
    • (Review) Keras Discussion - 6:48
    • (Review) Keras Neural Network in Code - 6:37
  • Appendix
    • What is the Appendix? - 2:48
    • Windows-Focused Environment Setup 2018 - 20:20
    • How to How to install Numpy, Theano, Tensorflow, etc... - 17:30
    • Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced? - 22:04
    • How to Succeed in this Course (Long Version) - 10:24
    • How to Code by Yourself (part 1) - 15:54
    • How to Code by Yourself (part 2) - 9:23
    • Proof that using Jupyter Notebook is the same as not using it - 12:29
    • Python 2 vs Python 3 - 4:38
    • What order should I take your courses in? (part 1) - 11:18
    • What order should I take your courses in? (part 2) - 16:07
    • Where to get discount coupons and FREE deep learning material - 2:20

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10 hours
Lessons
75

Recommender System Applications with Deep Learning

The Most In-Depth Course on Recommendation Systems with Deep Learning, Machine Learning, Data Science & AI Techniques

By Lazy Programmer | in Online Courses

Believe it or not, almost all online businesses today make use of recommender systems in some way or another. Recommender systems form the very foundation of the internet's top three websites. Google uses Search Results, YouTube uses Video Dashboard, and Facebook uses Newsfeed. This 10-hour course is a big bag of tricks that make recommender systems work across multiple platforms. You'll be looking at popular news feed algorithms, like Reddit, Hacker News, and Google PageRank, as well as Bayesian recommendation techniques that are being used by a large number of media companies today.

  • Access 75 lectures & 10 hours of content 24/7
  • Understand state-of-the-art algorithms like matrix factorization & deep learning
  • Learn a bag full of tricks to improve baseline results
  • Learn how techniques from natural language processing (NLP) have been used in recommenders
  • Perform matrix factorization using big data in Spark

Instructor

The Lazy Programmer is a data scientist, big data engineer, and full-stack software engineer. He has taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Humber College, and The New School. Multiple businesses have benefitted from their web programming expertise. He does all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies he has used are Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases, he has used MySQL, Postgres, Redis, MongoDB, and more.

Important Details

  • Length of time users can access this course: lifetime
  • Access options: web & mobile streaming
  • Certification of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Updates included
  • Experience level required: intermediate

Requirements

  • Basic arithmetic
  • Calculus, linear algebra & probability
  • Proficiency in Python & the Numpy Stack
  • Basic knowledge of using Keras

Course Outline

  • Welcome
    • Introduction - 3:09
    • Outline of the course - 4:39
    • Where to get the code - 5:05
    • Where to get help - 2:06
  • Simple Recommendation Systems
    • Section Introduction and Outline - 4:19
    • Perspective for this Section - 3:41
    • Basic Intuitions - 5:14
    • Associations - 4:43
    • Hacker News - Will you be penalized for talking about the NSA? - 7:28
    • Reddit - Should censorship based on politics be allowed? - 8:54
    • Problems with Average Rating & Explore vs. Exploit (part 1) - 10:58
    • Problems with Average Rating & Explore vs. Exploit (part 2) - 7:39
    • Bayesian Approach part 1 (Optional) - 11:07
    • Bayesian Approach part 2 (Sampling and Ranking) - 5:57
    • Bayesian Approach part 3 (Gaussian) - 8:23
    • Bayesian Approach part 4 (Code) - 12:01
    • Demographics and Supervised Learning - 7:22
    • PageRank (part 1) - 11:12
    • PageRank (part 2) - 11:55
    • Evaluating a Ranking - 4:39
    • Section Conclusion - 4:10
  • Collaborative Filtering
    • Collaborative Filtering Section Introduction - 11:38
    • User-User Collaborative Filtering - 13:51
    • Collaborative Filtering Exercise Prep - 10:21
    • Data Preprocessing - 15:26
    • User-User Collaborative Filtering in Code - 16:06
    • Item-Item Collaborative Filtering - 9:15
    • Item-Item Collaborative Filtering in Code - 7:07
    • Collaborative Filtering Section Conclusion - 5:34
  • Matrix Factorization and Deep Learning
    • Matrix Factorization Section Introduction - 4:08
    • Matrix Factorization - First Steps - 15:27
    • Matrix Factorization - Training - 8:56
    • Matrix Factorization - Expanding Our Model - 8:04
    • Matrix Factorization - Regularization - 6:18
    • Matrix Factorization - Exercise Prompt - 1:15
    • Matrix Factorization in Code - 6:17
    • Matrix Factorization in Code - Vectorized - 10:14
    • SVD (Singular Value Decomposition) - 7:48
    • Probabilistic Matrix Factorization - 6:06
    • Bayesian Matrix Factorization - 5:34
    • Matrix Factorization in Keras (Discussion) - 7:32
    • Matrix Factorization in Keras (Code) - 7:14
    • Deep Neural Network (Discussion) - 2:51
    • Deep Neural Network (Code) - 2:43
    • Residual Learning (Discussion) - 2:03
    • Residual Learning (Code) - 1:59
    • Autoencoders (AutoRec) Discussion - 10:14
    • Autoencoders (AutoRec) Code - 11:45
  • Big Data Matrix Factorization with Spark Cluster on AWS / EC2
    • Big Data and Spark Section Introduction - 7:16
    • Setting up Spark in your Local Environment - 7:36
    • Matrix Factorization in Spark - 10:28
    • Spark Submit - 6:26
    • Setting up a Spark Cluster on AWS / EC2 - 12:38
    • Making Predictions in the Real World - 2:46
  • Bonus: TF-IDF
    • TF-IDF Theory - 9:55
    • TF-IDF Code - 9:18
  • Basics Review
    • Keras Discussion - 6:48
    • Keras Neural Network in Code - 6:37
    • Keras Functional API - 4:26
    • Tensorflow Basics - 7:27
    • Tensorflow Neural Network in Code - 9:43
    • Confidence Intervals - 10:11
    • Gaussian Conjugate Prior - 5:41
  • Appendix
    • What is the Appendix? - 2:48
    • Windows-Focused Environment Setup 2018 - 20:20
    • How to How to install Numpy, Theano, Tensorflow, etc... - 17:30
    • Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced? - 22:04
    • How to Succeed in this Course (Long Version) - 10:24
    • How to Code by Yourself (part 1) - 15:54
    • How to Code by Yourself (part 2) - 9:23
    • Proof that using Jupyter Notebook is the same as not using it - 12:29
    • Python 2 vs Python 3 - 4:38
    • What order should I take your courses in? (part 1) - 11:18
    • What order should I take your courses in? (part 2) - 16:07
    • Where to get discount coupons and FREE deep learning material - 2:20

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6 hours
Lessons
44

The Deep Learning Masterclass: Classify Images with Keras

Catalyze Your Foray Into AI by Building a Model That Classifies Images

By Mammoth Interactive | in Online Courses

A subset of machine learning, deep learning focuses on how machines use neural networks to learn from data. These neural networks are used to perform tasks and are adjusted to create a better outcome each time, paving the way for groundbreaking machines that learn on their own! This master class takes you through machine learning, neural networks, and several core tools, like Keras, TensorFlow, and Python, as you work toward creating a model that can classify images.

  • Access 44 lectures & 6 hours of content 24/7
  • Walk through the essentials for using Python, Keras, TensorFlow & more machine learning tools
  • Expand your understanding of machine learning, neural networks & convolutions
  • Dive into creating your own image classifier model from scratch

Instructor

John Bura has been programming games since 1997 and teaching since 2002. John is the owner of the game development studio Mammoth Interactive. This company produces XBOX 360, iPhone, iPad, Android, HTML 5, ad-games and more. Mammoth Interactive recently sold a game to Nickelodeon! John has been contracted by many different companies to provide game design, audio, programming, level design, and project management.

Important Details

  • Length of time users can access this course: lifetime
  • Access options: web & mobile streaming
  • Certification of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Updates included
  • Experience level required: beginner

Requirements

  • Internet access required

Course Outline

  • DAY 1: Learn to Use PyCharm
    • 00. Bootcamp Intro - 5:42
    • 00. Intro to PyCharm - 3:55
    • 01. Downloading and Installing - 9:28
    • 02. Exploring PyCharm Interface - 8:32
    • 03. Add and Run Python Files - 7:25
    • 04. Building and Running a Simple Program - 10:05
  • DAY 2: Learn Python Language Basics
    • 00. Introduction - 5:13
    • 01. Variables Syntax And Basic Types - 8:33
    • 02. Variable Operations - 9:29
    • 03. Tuples and Lists - 11:54
    • 04. Dictionaries - 6:36
    • 05. If Statements - 10:03
    • 06. While and For In Loops - 10:43
    • 07. Function Implementation and Execution - 10:05
    • 08. Parameters and Return Values - 7:47
    • 09. Intro to Classes and Objects - 12:40
    • 10. Subclasses and Superclasses - 13:06
    • 11. Summary and Outro - 3:37
  • DAY 3: Understand Machine Learning Neural Networks
    • 00. Intro to Day 3 - 2:01
    • 01. Intro to Machine Learning - 11:23
    • 02. Intro to Neutral Networks - 10:23
    • 03. Intro to Convolutions - 14:10
  • DAY 4: Explore the Keras API
    • 00. Intro to Day 4 - 1:49
    • 01. Intro To TensorFlow And Keras - 9:06
    • 02. Understanding Keras Syntax - 19:13
    • 03. Intro to Activation Functions - 13:26
  • DAY 5: Format Datasets and Examine CIFAR-10
    • 00. Intro to Day 5 - 1:53
    • 01. Exploring CIFAR10 Dataset - 8:36
    • 02. Understanding Specific Data Points - 17:43
    • 03. Formatting Input Images - 12:04
  • DAY 6: Build the Image Classifier Model
    • 00. Intro to Day 6 - 2:23
    • 01. Building the Model - 18:18
    • 02. Compiling and Training the Model - 12:38
    • 03. Gradient Descent and Optimizers - 14:50
  • DAY 7: Save and Load Trained Models
    • 00. Intro to Day 7 - 2:08
    • 01. Saving and Loading Model to H5 - 15:20
    • 02. Saving Model to Protobuf File - 17:50
    • 03. BootCamp Summary - 5:40
  • Source Material
    • Source Code: Learn Python Language Basics
    • Texts Assets: Understand Machine Learning Neural Networks
    • Texts Assets: Explore the Keras API
    • Asset Files: Format Datasets and Examine CIFAR-10
    • Asset Files: Build the Image Classifier Model
    • Asset Files: Save and Load Trained Models

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2 hours
Lessons
23

Sentiment Analysis Through Deep Learning with Keras & Python

Learn to Apply Sentiment Analysis to Your Problems Through a Practical, Real World Use Case

By Mohammad Nauman | in Online Courses

Every company on the face of the earth wants to know what its customers feel about its products and services — and sentiment analysis is the easiest way and most accurate way of finding out the answer to this question. This course will make doing sentiment analysis really easy. It will cover 60 line sentiment analysis engines, basic machine learning with minimal math, real-life applications, and mistakes to avoid. By learning to do sentiment analysis, you would be making yourself invaluable to any company, especially those which are interested in quality assurance of their products and those working with business intelligence.

  • Access 23 lectures & 2 hours of content 24/7
  • Understand how to write industry-grade sentiment analysis engines w/ very little effort
  • Learn the basics of machine learning w/ minimal math
  • Understand not only the theoretical & academic aspects of sentiment analysis but also how to use it in your own field
  • Get tips on avoiding mistakes made by new-comers to the field & the best practices to get you to your goal w/ minimal effort

Instructor

Mohammad Nauman has a Ph.D. in Computer Sciences and a PostDoc from the Max Planck Institute for Software Systems. He has been programming since early 2000 and has worked with many different languages, tools, and platforms. He has extensive research experience with many state-of-the-art models to my name. His research in Android security has led to some major shifts in the Android permission model.

Important Details

  • Length of time users can access this course: lifetime
  • Access options: web & mobile streaming
  • Certification of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Updates included
  • Experience level required: beginner

Requirements

  • Python basics

Course Outline

  • Introduction to Sentiment Analysis with Deep Learning
    • Bird’s Eye View of Deep Sentiment Analysis - 13:49
    • Resource Download
    • MNIST Dataset Description - 8:11
    • Learning and Prediction Pipeline - 7:01
  • Bare Essentials of Theory
    • Machine Learning Pipeline - 9:13
    • Regression - 13:01
    • Neural Networks - a Modular Approach - 14:29
    • Recap and Supporting Talk - 2:43
  • Getting Started with Keras
    • Windows installation and hurdles - 6:55
    • Mac and Linux installation - 3:41
    • Data Preparation with Keras - 10:10
    • Learning and Evaluation with Keras - 10:32
  • Sentiment Analysis Case Study
    • Understanding the Sentiment Data - 10:36
    • Structure of Data for Deep Learning - 4:37
    • Model, Embedding and Applying to Real World - 10:41
  • Convolutional Neural Networks with Keras
    • Basics of Convolutional Neural Networks - 10:13
    • ConvNet with Keras - 8:30
    • Pooling and Translation Invariance - 4:25
    • Dropout and Regularization - 3:51
    • Using the functional API with CNN - 4:27
  • Revisiting the Sentiment Analysis Model
    • CNN, LSTM and Other Models for Sentiment Analysis - 5:57
  • Finishing Up
    • Saving and loading model weights - 6:30
    • Parting words and future directions - 3:48

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3 hours
Lessons
29

Deep Learning Using Keras Through a Real-World Case Study

Use Python to Understand Deep Learning with Practical, Code-Based Demonstrations

By Mohammad Nauman | in Online Courses

Have you ever wondered why deep learning is taking over the world? Why it has revolutionized so many fields in research and industry? In this course, you will get answers to these questions through a real-world use case. This course aims to achieve the best of both worlds: it will show you why machine learning and deep learning is powerful but will not focus on theory. It will focus on practicals and deal with issues that newcomers to this field face. There is very little theory and that only when absolutely necessary.

  • Access 29 lectures & 3 hours of content 24/7
  • Understand machine learning & deep learning from a practical viewpoint
  • Know all the problems that you might face & learn how to avoid them
  • Cover the basic models of Keras as well as the advanced models that few people have an understanding of

Instructor

Mohammad Nauman has a Ph.D. in Computer Sciences and a PostDoc from the Max Planck Institute for Software Systems. He has been programming since early 2000 and has worked with many different languages, tools, and platforms. He has extensive research experience with many state-of-the-art models to my name. His research in Android security has led to some major shifts in the Android permission model.

Important Details

  • Length of time users can access this course: lifetime
  • Access options: web & mobile streaming
  • Certification of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Updates included
  • Experience level required: beginner

Requirements

  • Basic understanding of Python

Course Outline

  • Intro and Essentials
    • Meet Your Instructor - 1:37
    • Reference: All Materials
    • Basic Pipeline for Machine Learning - 13:10
    • Get Help Here - 0:15
    • How is Machine Learning Used to Solve New Problems - 7:01
  • Some Necessary Background
    • Theoretical Aspects of ML in One Small Video - 9:13
    • Using Statistics to Learn How Machines Learn - 13:01
    • Making Yes/No Decisions and Why They Are Important - 14:29
    • More Information - 2:43
  • Keras Setup and Intro
    • Windows Setup and How Not To Have Headaches - 6:55
    • Linux and Mac Setup - 3:41
    • Basics of Keras - Getting Ready to Run - 10:10
    • Learning and Making Predictions with Keras - 10:32
  • Case Study from Bioinformatics
    • Real World Bioinformatics Case Study Motivation - 8:32
    • Getting Data Into a Workable Shape - One of the Most Difficult Tasks in ML - 15:51
    • Data Wrangling - How to Get it Right - 7:45
    • Making Sure Machines Cannot Memorize Data - 3:11
    • The Real Problem of Shapes - Getting your Head Around it - 4:32
    • Basic Keras Way of Defining Models - 8:58
    • Detailed and Advanced Model Defining Techniques with Keras - 5:25
  • CNNs and Advanced Graph Based Models
    • Convolutional Neural Networks - 10:13
    • Getting Started with CNN Code - The Easy Way - 8:30
    • Achieving Translation Invariance - 4:25
    • Achieve Regulzation With Ease and Efficiency - Dropout - 3:51
    • Advanced Graph-based Models with Keras - 4:27
    • Google's Inception Module in Depth - 9:36
    • Getting Rid of Vanishing Gradients - Residual Connections - 5:08
  • Extra Material
    • Progress Saving for Future Reuse - 6:30
    • Where to Go From Here - 3:55

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Terms

  • Unredeemed licenses can be returned for store credit within 30 days of purchase. Once your license is redeemed, all sales are final.
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