English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 46h 50m | 8.35 GB
Learn essential Artificial Intelligence concepts from AI experts like Peter Norvig and Sebastian Thrun, including search, optimization, planning, pattern recognition, and more.
Learn from the world’s foremost AI experts, and develop a deep understanding of algorithms being applied to real-world problems in natural language processing, computer vision, bioinformatics, and more. Practice a structured approach for applying these techniques to new challenges, and emerge fully prepared to advance in the field.
Table of Contents
1 Welcome to Artificial Intelligence
2 Introduction to AI
3 Applying AI to Sudoku
4 Setting up with Anaconda
5 Solving Sudoku with AI
6 Introduction to Game Playing
7 Advanced Game Playing
8 Build an Adversarial Search Agent
9 Search
10 Simulated Annealing
11 Constraint Satisfaction
12 Logic and Reasoning
13 Planning
14 Create a Domain-Independent Planner
15 Probability
16 Bayes Nets
17 Inference in Bayes Nets
18 Hidden Markov Models
19 Use HMMs to Recognize ASL
20 Wrapping Up Term 1
21 Welcome to Artificial Intelligence
22 Review Anaconda Setup
23 Cloud Computing Setup Instructions
24 GPU Workspaces Demo
25 Deep Neural Networks
26 Convolutional Neural Networks
27 CNN Project Dog Breed Classifier
28 Intro to TensorFlow
29 Autoencoders
30 Recurrent Neural Networks
31 Long Short-Term Memory Networks (LSTM)
32 Implementing RNNs and LSTMs
33 Hyperparameters
34 Sentiment Prediction with RNN
35 RNN Project Time Series Prediction and Text Generation
36 Generative Adversarial Networks
37 Deep Convolutional GANs
38 Semisupervised Learning
39 Concentration Previews
40 Intro to Computer Vision
41 Intro to Natural Language Processing
42 Intro to Voice User Interfaces
43 Choose Your Concentration
44 Lesson Plans
45 Intro to Computer Vision
46 Mimic Me!
47 Image Representation and Analysis
48 Image Segmentation
49 Features and Object Recognition
50 CV Capstone Facial Keypoint Detection
51 Completing the Program
52 Lesson Plans
53 Intro to Natural Language Processing
54 Bookworm
55 Natural Language Processing
56 Text Processing
57 Feature Extraction
58 Modeling
59 Machine Translation
60 Embeddings and Word2Vec
61 Sequence to Sequence
62 Completing the Program
63 Lesson Plans
64 Intro to Voice User Interfaces
65 Alexa History Skill
66 Introduction to Speech Recognition
67 DNN Speech Recognizer
68 Completing the Program
69 Conduct a Job Search
70 Refine Your Entry-Level Resume
71 Refine Your Career Change Resume
72 Refine Your Prior Industry Experience Resume
73 Craft Your Cover Letter
74 Develop Your Personal Brand
75 LinkedIn Review
76 Udacity Professional Profile
77 GitHub Review
78 Image Representation Classification
79 Convolutional Filters and Edge Detection
80 Types of Features Image Segmentation
81 Feature Vectors
82 CNN Layers and Feature Visualization
83 Advanced CNN Architectures
84 YOLO
85 RNN’s
86 Long Short-Term Memory Networks (LSTMs)
87 Hyperparameters
88 Optional Attention Mechanisms
89 Image Captioning
90 Optional Cloud Computing with AWS
91 Introduction to Motion
92 Robot Localization
93 Mini-project 2D Histogram Filter
94 Introduction to Kalman Filters
95 Representing State and Motion
96 Matrices and Transformation of State
97 Simultaneous Localization and Mapping
98 Optional Vehicle Motion and Calculus
99 Applying Deep Learning Models
100 Feedforward and Backpropagation
101 Training Neural Networks
102 Deep Learning with PyTorch
103 Deep Learning for Cancer Detection with Sebastian Thrun
104 Sentiment Analysis
105 Fully-Convolutional Neural Networks Semantic Segmentation
106 3D CNN’s
107 C++ Getting Started
108 C++ Vectors
109 Practical C++
110 C++ Object Oriented Programming
111 Python and C++ Speed
112 C++ Intro to Optimization
113 C++ Optimization Practice
114 Welcome to Natural Language Processing
115 Intro to NLP
116 Text Processing
117 Spam Classifier with Naive Bayes
118 Part of Speech Tagging with HMMs
119 (Optional) IBM Watson Bookworm Lab
120 Feature extraction and embeddings
121 Topic Modeling
122 Sentiment Analysis
123 Sequence to Sequence
124 Deep Learning Attention
125 RNN Keras Lab
126 Cloud Computing Setup Instructions
127 Intro to Voice User Interfaces
128 (Optional) Alexa History Skill
129 Speech Recognition
130 Recurrent Neural Networks
131 Long Short-Term Memory Networks (LSTM)
132 Keras
133 Sentiment Analysis with Andrew Trask
134 Sentiment Prediction RNN
135 TensorFlow
136 Embeddings and Word2Vec
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