![Hands-on Deep Learning with TensorFlow [Video]](https://forcode.su/wp-content/uploads/hands-deep-learning-tensorflow-video.jpg)
English | MP4 | AVC 1920×1080 | AAC 44KHz 2ch | 2h 11m | 856 MB
Build smart systems with ease using TensorFlow
Are you short on time to start from scratch to use deep learning to solve complex problems involving topics like neural networks and reinforcement learning? If yes, then this is the course to help you. This course is designed to help you to overcome various data science problems by using efficient deep learning models built in TensorFlow.The course begins with a quick introduction to TensorFlow essentials. Next, we start with deep neural networks for different problems and then explore the applications of Convolutional Neural Networks on two real datasets. If you’re facing time series problem then we will show you how to tackle it using RNN. We will also highlight how autoencoders can be used for efficient data representation. Lastly, we will take you through some of the important techniques to implement generative adversarial networks. All these modules are developed with step by step TensorFlow implementation with the help of real examples.By the end of the course you will be able to develop deep learning based solutions to any kind of problem you have, without any need to learn deep learning models from scratch, rather using tensorflow and it’s enormous power.
This is a hands-on course covering important deep learning techniques with TensorFlow and using practical examples. Throughout the course, you’ll learn to work with different algorithms and follow step-by-step instructions to implement them using different example real-world
What You Will Learn
- Use the power of TensorFlow to help you in your daily deep learning tasks
- Build a base for TensorFlow by implementing regression
- Solve prediction deep learning problems with TensorFlow
- Solve Image classification deep learning problems with TensorFlow
- Tackle the potential of RNN and LSTM Neural Networks with TensorFlow to solve time series problems
- Utilize the power of efficient data representation using autoencoders
- Effective ways to implement generative adversarial networks in the real world
- Get equipped to develop projects with deep learning
Table of Contents
01 The Course Overview
02 TensorFlow for Building Deep Learning Models
03 Basic Syntaxes, Function Optimization, Variables, and Placeholders
04 TensorBoard for Visualization
05 Start by Loading the Imported Dataset
06 Building the Layers of the Neural Network in TensorFlow
07 Optimizing the Softmax Cross Entropy Function
08 Using DNN Predicting Whether Breast Cancer Cells Are Benign or Not
10 Writing the TensorFlow Code to Add Convolutional and Pooling Layers
11 Using tf.train.AdamOptimizer API to Optimize CNN
12 Implementing CNN to Create a Face Recognition System
13 Understanding the RNN and the Need for LSTM
14 Implementing RNN
15 Monthly Riverflow Prediction of Turtle River in Ontario
16 Implement LSTM Project to Predict Decimal Number of Given Binary Representation
17 Encoder and Decoder for Efficient Data Representation
18 TensorFlow Code Using Linear Autoencoder to Perform PCA on a 4D Dataset
19 Using Stacked Autoencoders for Representation on MNIST Dataset
20 Build a Deep Autoencoder to Reduce Latent Space of LFW Face Dataset
21 Generator and Discriminator the Basics of GAN
22 Downloading and Setting Up the (Microsoft Research Asia) Geolife Project Dataset
23 Coding the Generator and Discriminator Using TensorFlow
24 Training GANs to Create Synthetic GPS Based Trajectories
Resolve the captcha to access the links!