TensorFlow: Basic to Advanced Training
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This course offers a comprehensive journey into TensorFlow, guiding learners from the basics to advanced applications of machine learning and deep learning with this powerful open-source framework. Starting with an introduction to machine learning and the unique capabilities of TensorFlow, students will gain foundational knowledge that sets the stage for more complex concepts. The course begins with installation and setup instructions to ensure every student is equipped with the necessary tools and environment for TensorFlow development. Early modules cover the essential building blocks of TensorFlow, including tensors, operations, computational graphs, and sessions. Through these topics, students will understand the core components of TensorFlow and how to utilize them effectively for simple projects and data operations.
As the course progresses, learners dive deeper into neural networks, exploring how to build, train, and optimize basic models. The intermediate section introduces Keras, the user-friendly API for TensorFlow, allowing students to design and train complex models more intuitively. Topics like convolutional neural networks (CNNs) and recurrent neural networks (RNNs) provide hands-on experience with real-world data types, such as images and sequences. The course then transitions to advanced topics, covering essential skills for deploying and scaling models. Students will learn to save, load, and serve TensorFlow models, enabling them to apply their knowledge in production environments. They’ll also explore distributed TensorFlow for scaling applications across multiple devices and TensorFlow Extended (TFX) for building end-to-end machine learning pipelines.
With practical projects and real-world applications woven throughout, students will have the chance to build models for tasks like image classification, sentiment analysis, and time series prediction, solidifying their skills through hands-on practice. By the end of the course, learners will be equipped not only with the technical knowledge but also the practical experience needed to implement, deploy, and manage TensorFlow models in professional environments. This course is ideal for anyone looking to advance their career in data science, machine learning, or artificial intelligence, empowering them with the expertise to tackle complex challenges in today’s data-driven world.
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7Introduction to TensorsVideo lesson
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8Tensor OperationsVideo lesson
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9Constants, Variables, and PlaceholdersVideo lesson
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10TensorFlow Computational GraphVideo lesson
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11Creating and Running a TensorFlow SessionVideo lesson
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12Managing Graphs and SessionsVideo lesson
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13Building a Simple Feedforward Neural NetworkVideo lesson
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14Activation FunctionsVideo lesson
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15Loss Functions and OptimizersVideo lesson
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16Introduction to Keras APIVideo lesson
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17Building Complex Models with KerasVideo lesson
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18Training and Evaluating ModelsVideo lesson
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19Introduction to CNNs(Convolutional Neural Networks)Video lesson
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20Building and Training CNNs with TensorFlowVideo lesson
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21Transfer Learning with Pre-trained CNNsVideo lesson
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22Introduction to RNNs(Recurrent Neural Networks)Video lesson
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23Building and Training RNNs with TensorFlowVideo lesson
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24Applications of RNNs: Language Modeling, Time Series PredictionVideo lesson
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25Saving and Loading ModelsVideo lesson
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26TensorFlow Serving for Model DeploymentVideo lesson
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27TensorFlow Lite for Mobile and Embedded DevicesVideo lesson
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28Introduction to Distributed Computing with TensorFlowVideo lesson
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29TensorFlow's Distributed Execution FrameworkVideo lesson
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30Scaling TensorFlow with TensorFlow Serving and KubernetesVideo lesson
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31Introduction to TFX(TensorFlow Extended)Video lesson
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32Building End-to-End ML Pipelines with TFXVideo lesson
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33Model Validation, Transform, and Serving with TFXVideo lesson
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34Image ClassificationVideo lesson
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35Natural Language ProcessingVideo lesson
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36Recommender SystemsVideo lesson
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37Object DetectionVideo lesson
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38Building a Sentiment Analysis ModelVideo lesson
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39Creating an Image Recognition SystemVideo lesson
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40Developing a Time Series Prediction ModelVideo lesson
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41Implementing a ChatbotVideo lesson
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42Generative Adversarial Networks (GANs)Video lesson
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43Reinforcement Learning with TensorFlowVideo lesson
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44Quantum Machine Learning with TensorFlow QuantumVideo lesson
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45TensorFlow Documentation and TutorialsVideo lesson
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46Online Courses and BooksVideo lesson
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47TensorFlow Community and ForumsVideo lesson
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