Last updated 9/2018MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHzLanguage: English | Size: 4.05 GB | Duration: 8h 7m

Harness the power of deep learning with Google’s TensorFlow!

What you'll learn

Gain proficiency in building deep learning projects using TensorFlow without any need to delve into writing models from scratch

Build a base for TensorFlow by implementing regression

Solve prediction and image classification deep learning problems with TensorFlow

Tackle the potential of RNN and LSTM neural networks with TensorFlow to solve series problems

Gain hands-on experience designing, training, and deploying your deep learning models with TensorFlow and Keras to handle large volumes of data and complex neural network architectures

Design and expent with complex neural network architectures using low-level TensorFlow while also using TensorFlow’s high level APIs and Keras

Scale out training and prediction using different distributed techniques such as data parallelism using GPUs on your local machine and in the cloud using Google Cloud ML Ee

Requirements

Familiarity with Python programming is required.

Description

Deep learning is the intersection of statistics, artificial intelligence, and data to build accurate models. With deep learning going mainstream, making sense of data and getting accurate results using deep networks is possible. Tensorflow is Google’s popular offering for machine learning and deep learning. It has become a popular choice of tool for perfog fast, efficient, and accurate deep learning. TensorFlow is one of the most comprehensive libraries for implementing deep learning.This comprehensive 2-in-1 course is your step-by-step guide to exploring the possibilities in the field of deep learning, making use of Google's TensorFlow. You will learn about convolutional neural networks, and logistic regression while training models for deep learning to gain key insights into your data with the help of insightful examples that you can relate to and show how these can be exploited in the real world with complex raw data. You will also learn how to scale and deploy your deep learning models on the cloud using tools and frameworks such as asTensorFlow, Keras, and Google Cloud MLE. This learning path presents the implementation of practical, real-world projects, teaching you how to leverage TensorFlow’s capabilities to perform efficient deep learning.This training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible.The first course, Hands-on Deep Learning with TensorFlow, is designed to help you overcome various data science problems by using efficient deep learning models built in TensorFlow. You will b with a quick introduction to TensorFlow essentials. You will then learn deep neural networks for different problems and explore the applications of convolutional neural networks on two real datasets. You will also learn how autoencoders can be used for efficient data representation. Finally, you will understand some of the important techniques to implement generative adversarial networks.The second course, Applied Deep Learning with TensorFlow and Google Cloud AI, will help you get the most out of TensorFlow and Keras to accelerate the training of your deep learning models and deploy your model at scale on the Cloud. Tools and frameworks such as TensorFlow, Keras, and Google Cloud MLE are used to showcase the strengths of various approaches, trade-offs, and building blocks for creating, training and evaluating your distributed deep learning models with GPU(s) and deploying your model to the Cloud. You will learn how to design and train your deep learning models and scale them out for larger datasets and complex neural network architectures on multiple GPUs using Google Cloud ML Ee. You will also learn distributed techniques such as how parallelism and distribution work using low-level TensorFlow and high-level TensorFlow APIs and Keras.By the end of this Learning Path, you will be able to develop, train, and deploy your models using TensorFlow, Keras, and Google Cloud Machine Learning Ee.Meet Your Expert(s):We have the best work of the following esteemed author(s) to ensure that your learning journey is smooth:Salil Vishnu Kapur is a Data Science Researcher at the Institute for Big Data Analytics, Dalhousie University. He is extremely passionate about machine learning, deep learning, data mining, and Big Data analytics. Currently working as a Researcher at Deep Vision and prior to that worked as a Senior Analyst at Capgi for around 3 years with these technologies. Prior to that Salil was an intern at IIT Bombay through the FOSSEE Python TextBook Companion Project and presently with the Department of Fisheries and Transport Canada through Dalhousie University.Christian Fanli Ramsey is an applied data scientist at IDEO. He is currently working at Greenfield Labs a research center between IDEO and Ford that focuses on the future of mobility. His primary focus on understanding complex emotions, stress levels and responses by using deep learning and machine learning to measure and classify psychophysiological signals.Haohan Wang is a deep learning researcher. Her focus is using machine learning to process psychophysiological data to understand people’s emotions and mood states to provide support for people’s well-being. She has a background in statistics and finance and has continued her studies in deep learning and neurobiology.Christian and Haohan together they make dyad machina and their focus area is at the interaction of deep learning and psychophysiology, which means they mainly focus on 2 areas: - They want to help further intelligent systems to understand emotions and mood states of their users so they can react accordingly - They also want to help people understand their emotions, stress responses, mood states and how they vary over in order to help people become more emotionally aware and resilient

Overview

Section 1: Hands-on Deep Learning with TensorFlow

Lecture 1 The Course Overview

Lecture 2 TensorFlow for Building Deep Learning Models

Lecture 3 Basic Syntaxes, Function Optimization, Variables, and Placeholders

Lecture 4 TensorBoard for Visualization

Lecture 5 Start by Loading the Imported Dataset

Lecture 6 Building the Layers of the Neural Network in TensorFlow

Lecture 7 Optimizing the Softmax Cross Entropy Function

Lecture 8 Using DNN Predicting Whether Breast Cancer Cells Are Be or Not

Lecture 9 Importing the Two Datasets Using TensorFlow and Sklearn API

Lecture 10 Writing the TensorFlow Code to Add Convolutional and Pooling Layers

Lecture 11 Using tf.train.AdamOptimizer API to Optimize CNN

Lecture 12 Implementing CNN to Create a Face Recognition System

Lecture 13 Understanding the RNN and the Need for LSTM

Lecture 14 Implementing RNN

Lecture 15 Monthly Riverflow Prediction of Turtle River in Ontario

Lecture 16 Implement LSTM Project to Predict Decimal Number of Given Binary Representation

Lecture 17 Encoder and Decoder for Efficient Data Representation

Lecture 18 TensorFlow Code Using Linear Autoencoder to Perform PCA on a 4D Dataset

Lecture 19 Using Stacked Autoencoders for Representation on MNIST Dataset

Lecture 20 Build a Deep Autoencoder to Reduce Latent Space of LFW Face Dataset

Lecture 21 Generator and Discriminator the Basics of GAN

Lecture 22 ing and Setting Up the (Microsoft Research Asia) Geolife Project Dataset

Lecture 23 Coding the Generator and Discriminator Using TensorFlow

Lecture 24 Training GANs to Create Synthetic GPS Based Trajectories

Section 2: Applied Deep Learning with TensorFlow and Google Cloud AI

Lecture 25 The Course Overview

Lecture 26 Installation

Lecture 27 Introduction

Lecture 28 Keras Backends

Lecture 29 Design and Compile a Model

Lecture 30 Model Training, Evaluation, and Prediction

Lecture 31 Training with Data Augmentation

Lecture 32 Training with Transfer Learning and Data Augmentation

Lecture 33 Introduction to TensorFlow

Lecture 34 Introduction to TensorBoard

Lecture 35 Types of Parallelism in Deep Learning – Synchronous and Asynchronous

Lecture 36 Distributed TensorFlow

Lecture 37 Configuring Keras to use TensorFlow for Distributed Problems

Lecture 38 Introduction

Lecture 39 Introduction to Google Cloud Machine Learning Ee

Lecture 40 Datasets, Feature Columns, and Estimators

Lecture 41 Representing Data in TensorFlow

Lecture 42 Quick Dive into TensorFlow Estimators

Lecture 43 Creating Data Input Pipelines

Lecture 44 Setting Up Our Estimator

Lecture 45 Packaging Our Model

Lecture 46 Training with Google Cloud ML Ee

Lecture 47 Hyperparameter Tuning in the Cloud

Lecture 48 Deploying Our Model for Prediction

Lecture 49 Creating Our Prediction API

Lecture 50 Wrapping Up

Lecture 51 Course Summary

This learning path is aimed at data science professionals to give them a solid background in how to scale-out deep learning in particular, how to handle large volumes of data and complex neural network architectures and how to deploy their models on the Cloud for production level systems.

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