English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 1h 25m | 215 MB
Build Intelligent Applications using Deep Learning models in Tensorflow
Use Tensorflow’s capabilities to perform efficient deep learning on Images
Tensorflow is Google’s popular offering for machine learning and deep learning. It has quickly become a popular choice of tool for performing fast, efficient, and accurate deep learning. This course presents the implementation of practical, real-world projects, teaching you how to leverage Tensforflow’s capabilties to perform efficient deep learning.
In this video, you will be acquainted with the different paradigms of performing deep learning such as deep neural nets, convolutional neural networks, recurrent neural networks, and more, and how they can be implemented using Tensorflow.
This will be demonstrated with the help of end-to-end implementations of three real-world projects on popular topic areas such as natural language processing, image classification, fraud detection, and more. By the end of this course, you will have mastered all the concepts of deep learning and their implementation with Tensorflow and Keras.
What You Will Learn
- Set up a Machine Learning environment
- Work with Docker and Keras
- Process images for machine vision
- Process text for Natural language understanding
- Work with tabular data to make financial predictions
- Generate synthetic test data with machine learning
Table of Contents
01 The Course Overview
02 Installing Docker
03 The Machine Learning Dockerfile
04 Sharing Data
05 Machine Learning REST Service
06 MNIST Digits
07 Tensors – Just Multidimensional Arrays
08 Turning Images into Tensors
09 Turning Categories into Tensors
10 Classical_Dense Neural Network
11 Activation and Non Linearity
12 Softmax
13 Training and Testing Data
14 Dropout and Flatten
15 Solvers
16 Hyperparameters
17 Grid Search
18 Convolutions
19 Pooling
20 Convolutional Neural Network
21 Deep Neural Network
22 REST API Definition
23 Trained Models in Docker Containers
24 Making Predictions
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