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    TensorFlow 2.0 Masterclass: Hands-On Deep Learning and AI

    Posted By: ELK1nG
    TensorFlow 2.0 Masterclass: Hands-On Deep Learning and AI

    TensorFlow 2.0 Masterclass: Hands-On Deep Learning and AI
    MP4 | h264, 1280x720 | Lang: English | Audio: AAC, 44.1 KHz | 14h 27m | 6.05 GB

    Work on 6 Projects, Hands-On TensorFlow 2.0, Keras, Deep Learning and Artificial Intelligence ! What you'll learn
    Complete Understanding of TensorFlow 2.0 from the Scratch

    Work on 6 Projects, Hands-On TensorFlow 2.0, Keras, Deep Learning and Artificial Intelligence ! What you'll learn
    Complete Understanding of TensorFlow 2.0 from the Scratch
    Artificial Neural Networks (ANNs)
    Convolutional Neural Networks (CNNs)
    Recurrent Neural Networks (RNNs)
    Transfer Learning
    Natural Language Processing
    Data Analysis with Numpy, Pandas and Data Visualization with Matplotlib

    Description :
    Google has recently released TensorFlow 2.0, it has so many features that simplify the Model Development, Maintenance, Processes and Performance .

    Why TensorFlow 2.0 ?

    Whether you’re an expert or a beginner, TensorFlow 2.0 is an end-to-end platform that makes it easy for you to build and deploy ML models

    With the TensorFlow 2.0,

    1) Building the Model is very Easy

    2) Robust ML production anywhere

    3) Powerful experimentation for research

    With this course you will have the Complete Understanding of TensorFlow 2.0 from very Beginning !

    List of the Projects,

    Project 1: CNN for Digit Recognition

    Project 2: CNN for Breast Cancer Detection

    Project 3: CNN for Predicting the Bank Customer Satisfaction

    Project 4: CNN for Credit Card Fraud Detection

    Project 5: RNN - LSTM for IMDB Review Classification

    Project 6: Google Stock Price Prediction with RNN and LSTM

    Main Topics Covered in this Course,

    Part 1: Introduction (Section 1)

    Part 2: Artificial Neural Networks (Section 2 - Section 4)

    Part 3: Convolutional Neural Networks (Section 5 - Section 11)

    Part 4: Recurrent Neural Networks (Section 12 - Section 15)

    Part 5: Transfer Learning

    Part 6: Natural Language Processing (Section 16)

    Part 7: Data Analysis with Numpy, Pandas and Data Visualization with Matplotlib (Section 17 - Section 19)

    Who this course is for :
    Anyone who wants to start a career in the field of Data Science
    Anyone Passionate about Deep Learning and Artificial Intelligence