Learn how to automate AWS with Python scripts, manage AWS resources with Boto3 library, and more with Automating AWS with Python and Boto3 training course.
- 3 hours 36 minutes of training Videos
- 20 lectures
- Very exhaustive coverage to all the topics
- Unlimited Access
Over 1.5 million AWS experts have started their preparation with our Online Courses. Enroll now for Automating AWS with Python & Boto3 online course.
Getting Started
Topic-wise Content Distribution
Introduction
- Introduction & Prerequisites
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5m 27s
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Install AWS SDK7m 51s
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Setting up authentication16m 19s
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API Key Best practices11m 45s
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Testing setup10m 51s
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IAM Permissions for Development10m 23s
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AWS Boto3 Documentation14m 16s
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Starting an EC2 Instance with Python15m 53s
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Additional features for EC213m 52s
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Unit Test for Boto3 SDK24m 1s
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Waiters in Boto3 SDK5m 50s
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Enforcing S3 with Python9m 32s
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Exceptions in Boto3 SDK10m 47s
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Automating AWS Lambda to run Python11m 54s
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Amazon EC2 Report with Python10m 26s
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Pagination in Boto3 SDK7m 23s
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Filtering Amazon EC2 Query5m 7s
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Enforcing AWS CloudTrail with Python10m 42s
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Deploying the AWS Lambda and trigger10m 7s
- What Next?
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3m 49s
From web development to data science, Python is everywhere. Learn the basic and advanced concepts of Python with Python for Beginners training course.
- 6+ hours of Training Videos
- 52 lectures
- Very exhaustive coverage to all the topics
- Unlimited Access
Topic-wise Content Distribution
Section One-Introducing Python Programming
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Python Programming Overview3m 22s
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Python Installation7m
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Basic Syntax4m 28s
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Variable Types6m 59s
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Basic Operators6m 31s
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About String in Python5m 37s
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Print a string Program6m 16s
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Program to Extract Characters9m 13s
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String Concatenation Program6m 58s
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Palindrome String Program9m 9s
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Sort a string7m 10s
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About Lists in Python2m 38s
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Define Lists6m 1s
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Lists Basic Operations - Part 17m 43s
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Lists Basic Operations - Part 26m 49s
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About Tuples in Python2m 41s
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Basic Python Program for Tuples5m 35s
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Tuples Basic Operations – Part 18m 35s
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Tuples Basic Operations – Part 27m 33s
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About Set in Python2m 32s
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Print Set Program5m 19s
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Methods in Set5m 33s
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Use of Operators in set7m 29s
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About Dictionary in Python3m 12s
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Program to Extract Elements from dictionary4m 49s
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Program to Change Values5m 45s
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Program to Delete Items from dictionary6m 58s
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Dictionary Basic Operations8m 28s
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If loop Program7m 50s
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If - else loop Program6m 13s
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If- elif loop Program7m 54s
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If-else nested Program8m 45s
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About While, for and Range loop3m 27s
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For loop Program7m 1s
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Range Function Program7m 33s
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Range Function in a for loop5m 54s
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While loop Program8m 8s
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About Break and Continue Statement3m 29s
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Break Statement Program11m 28s
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Continue Statement Program9m 58s
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Program to define Functions7m 19s
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Default Parameters in function8m 24s
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Factorial Program using function6m 39s
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Lambda Program8m 50s
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Map Program7m 13s
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Math Program6m 10s
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Exception Program9m 22s
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Class in Python Program7m 4s
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Defining Object in python6m 6s
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Constructors in python Program10m 33s
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Project 1 – Guess the Number Game in python14m 58s
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Project 2 – Rock, Paper, Scissors Game in python18m 9s
This Introduction to Data Science with Python course aims to help you master Python programming concepts that are essential for Data Science.
- 8+ hours of training videos
- Very extensive coverage to all the topics
- Unlimited access for 2 years
Topic-wise Content Distribution
Introduction
- Course Intro
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5m 35s
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Data Structures in Python6m 35s
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Basic Python libraries7m 58s
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Demo - NumPy15m 16s
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Demo - Pandas10m 34s
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Demo - Matplotlib11m 50s
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Demo - Pandas Case Study5m 7s
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Covariance and Correlation7m 25s
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Data Preprossesing for Machine Learning13m 8s
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The two types of ML Problems2m 22s
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Classification14m 48s
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Regression12m 10s
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K-means Clustering8m 53s
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Demo - Hierarchical-clustering9m 3s
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Outlier Detection7m 30s
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Linear Regression14m 28s
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Multiple Linear Regression13m 5s
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Optimization in Machine Learning16m 9s
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Batch Gradient Descent15m 21s
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Stochastic Gradient Descent11m 56s
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Logistic Regression - Sigmoid Function12m 55s
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Cost Function and Decision Boundary5m 1s
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Demo - Cost Function and Finding the Decision Boundaries16m 12s
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Deep Learning and Perceptron7m 34s
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Forward and Backward Propagation12m 27s
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Demo - Forward and Backward Propagation9m 41s
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TensorFlow9m 35s
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Demo - Image Classification using TensorFlow15m 56s
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PyTorch9m 33s
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Demo - Defining a Neural Network in PyTorch7m 53s
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Keras13m 47s
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Feedforward Neural Network11m 23s
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Demo - Activation Functions in Neural Networks8m 45s
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Perceptron Model15m 49s
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Recurrent Neural Networks (RNN)11m 38s
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LSTM and GRUs6m 11s
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Demo - Recurrent Neural Networks (RNN) with Keras11m 6s
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Convolutional Neural Networks16m 16s
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Demo - Computer Vision with OpenCV7m 17s
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Computer Vision6m 33s
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Demo - Convolutional Neural Networks with Max Pooling14m 11s
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Machine Learning on AWS Cloud14m 5s
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Demo - ML on AWS - Part 18m 41s
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Demo - ML on AWS - Part 210m 19s
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Demo - ML on AWS - Part 38m 26s
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Machine Learning on Azure6m 11s
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Demo - ML on Azure - Part 17m 5s
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Demo - ML on Azure - Part 213m 11s
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Machine Learning on GCP6m 57s
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Demo - ML on GCP - Part 18m 33s
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Demo - ML on GCP - Part 212m 11s
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Key differences between AWS, Azure and GCP2m 25s
Enroll in our Natural Language Processing course today to master the fundamentals of NLP, Machine Learning concepts, Python toolkits, and gain confidence. This course will take you through beginner to advanced-level concepts of NLP techniques, NLP Models to design and develop NLP-based applications using Python. After going through all the 24 videos you will have hands-on experience and complete understanding of how NLP works in Python.
NLP is a technique for deciphering and manipulating human language using algorithms. It is one of the most widely used techniques of machine learning. Professionals proficient in designing models that evaluate voice and language, find contextual patterns, and provide insights from text and audio will be in high demand as AI grows.
This course provides you necessary skills and knowledge of some of the NLP concepts and following are the important concepts covered:
- Use Text Analytics with Python
- Use Machine Learning concepts for NLP
- Use Python toolkits for NLP
- Use NLTK for Sentiment Analysis
- Learn about NLP Modules
- Learn Clustering and topic modeling
- Learn spaCy, Glove, SpamHam
- Parts-of-Speech Tagging
- Natural Language Analysis
- Bagging
- Text processing of data
If you are an application developer, you will be able to build applications that help to process and analyse NLP data. Whereas, if you are a Python developer, you will be able to build Python programs for NLP using NLTK. If you are a data scientist, then you will be able to use NLP techniques such as Sentiment analysis, topic modeling, POS Tagging, and so on. Finally, if you are an NLP developer, then you will expand your knowledge and skills on NLP using Python.
By the end of this course, you will be able to demonstrate the skills to use Python for designing NLP applications. You will also get unlimited access to 3+ hours of all 24 video lectures developed by our experts. Earn a course completion certificate once you complete this course to showcase your technical skills and for credibility.
- Growing demand: According to MarketsandMarkets, the NLP market is growing at a Compound Annual Growth Rate (CAGR) of 20.3%, between 2020 and 2025 period. You can become one among the NLP professionals in this rapidly growing market that is worth around USD 11-26 billion.
- NLP skills: Learning NLP using Python is easier, there are a large number of libraries to develop NLP based applications and community support is high. This helps you to enrich your knowledge and skills to become an expert.
- Career growth: With NLP skill sets, you can find job roles in top companies like Accenture, Qualcomm Technologies, Huawei Technologies, Thomson Reuters, and so on.
- 24 full length video lectures
- Auto-updates to the course content
- 24x7 Support from our Subject Matter Experts
- Lifetime validity and unlimited access
- Course completion certificate
Topic-wise Content Distribution
Course Lectures
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Introduction19m 51s
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Corpus10m 28s
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Feature Extraction17m 38s
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Demo Installation6m 31s
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Demo Sentiment Analysis19m 2s
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Demo Text Processing20m 2s
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Text Analytics15m 30s
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Machine learning25m 10s
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Natural Language13m 47s
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Python Toolkits19m 4s
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POSTagging11m 58s
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SpamHam14m 17s
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ClusteringAndTopics7m 59s
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Bagging5m 19s
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Deep learning 1: Introduction16m 33s
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Deep learning 2: NLP models16m 52s
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Demo setup11m 51s
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Demo clustering and topic modeling 120m 27s
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Demo clustering and topic modeling 221m 47s
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Demo Sentiment Analysis19m 2s
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Demo NLTK12m 48s
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Demo sPacy23m 39s
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Demo Glove12m 43s
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Demo SpamHam18m 13s
The “TensorFlow for Deep Learning with Python” course is designed to help you gain knowledge of Deep Learning models using TensorFlow. You will learn the architectures of common deep learning models, implement them in TensorFlow 2.0 and learn to optimize them.
Anyone interested in this program needs to have:
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- Basic knowledge of Python, math
- Suggested: Basic understanding of AI, ML
- Basics of Deep Learning
- Learn how to build in TensorFlow 2.0:
- Multilayer Perceptron
- Convolutional Neural Network
- Recurrent Neural Network
- Generative adversarial network
- Auto Encoder
- Variational Autoencoder
- Re-enforcement Learning
- Metrics, model tuning, loss functions
- TensorFlow Lite
- Mathematicians, computer scientists
- Anyone interested in AI, ML and TenorFlow 2.0
Topic-wise Content Distribution
Course Lectures
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Introduction to deep learning17m 18s
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Recurrent neural networks: Introduction13m 3s
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Recurrent neural networks: Variants18m 5s
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Recurrent neural network: Advanced9m 22s
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Reinforcement Learning: Bsics13m 15s
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Reinforcement Learning: Example6m 32s
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Generative Adversarial Networks: Basics16m 30s
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Generative Adversarial Networks: Types9m 39s
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Convolutional neural networks: Basics15m 56s
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Convolutional neural networks: Layers18m 41s
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Convolutional neural networks: Architectures13m 41s
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Autoencoders10m 12s
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Large Deep Networks15m 52s
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TensorFlow Lite9m 14s
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Model Evaluation: Metrics13m 11s
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Model Evaluation: Optimization methods18m 27s
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Loss functions13m 27s
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Variational autoencoder9m 47s
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demo CNN layer analysis8m 31s
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demo RNN 1: Build model18m 7s
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demo RNN 2: Analyze results12m 1s
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demo Variational autoencoder6m 25s
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demo Autoencoder5m 16s
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demo Regression9m 51s
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demo Generative Adversarial Network8m 29s
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demo Regularization7m 20s