PluralSight.Getting.Started.with.Tensorflow.2.0.BOOKWARE-KNiSO

Section

Appz

Group

KNiSO

Size

301,61 MB

Files

24

Date

2020-07-23

NFO

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                              PROUDLY PRESENTING:
                    
                     Getting.Started.with.Tensorflow.2.0



				 INFORMATION:

  Date............: 2020-07-23
  Rars............: 22 Rar Files
  Course Length...: 3 hrs 09 mins
  Website.........: https://tinyurl.com/y6kgsng3

			      Release Notes...:
                                                                              
  TensorFlow has long been a powerful and widely used framework for building and 
  training neural network models. In recent years though other frameworks such as 
  PyTorch have gained popularity specifically due to their intuitive programming 
  model which uses dynamic execution graphs. Now TensorFlow 2.0 offers all the ease 
  of use of other frameworks along with TensorFlow's performance and functionality. 
  TensorFlow's use of the Keras high-level API makes designing and training neural 
  networks very straightforward while eager execution makes prototyping and debugging 
  models simple. First, you will explore the basic features in TensorFlow 2.0 and how 
  its programming model differs from TensorFlow 1.x versions. You will understand the 
  basic working of a neural network and its active learning unit, the neuron. Next, 
  you will compare and contrast static and dynamic computation graphs and understand 
  the advantages and disadvantages of working with each kind of graph. You will get 
  hands-on exploring execution in TensorFlow 2.0 in eager execution mode and harness 
  the performance efficiencies of static graphs by using the tf.function decorator to 
  decorate ordinary Python functions. You will then learn how a neural network is 
  trained using gradient descent optimization and how the GradientTape() library in 
  TensorFlow calculates gradients automatically during the training phase of your 
  neural network model. Finally, you will learn how different APIs in Keras lend 
  themselves to different use-cases. Sequential models consisting of layers stacked 
  one on top of the other are simple and have long been supported by Keras. You will 
  also explore the Functional API and model subclassing in Keras and then use these 
  APIs to build regression as well as classification models When youÆre finished with 
  this course, you will have the skills and knowledge to harness the computational power 
  of the TensorFlow 2.0 framework and choose between the different model-building 
  strategies available in Keras.

			      Install Notes...: 
			 Unrar, Learn and Enjoy!                                                                            
                                                     
                         
			      GREETINGS:

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