Tensorflow: Basic To Advanced Training

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Flexible, Scalable, Open-Source Machine Learning Framework

What you'll learn

Core TensorFlow concepts from setup to model building, enabling them to confidently create machine learning projects.

Techniques for building CNNs and RNNs for image, language, and sequence data, equipping them to tackle various ML problems.

Skills to deploy TensorFlow models to production, including scaling with distributed computing and deploying on mobile.

Practical experience with real-world ML applications, building models for image recognition, sentiment analysis, and more.

Requirements

Basic programming knowledge, ideally in Python

Understanding of fundamental math concepts like linear algebra and probability

Familiarity with machine learning basics is helpful but not required

A computer with internet access for installing TensorFlow and coding projects

Description

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.

Overview

Section 1: Introduction to Machine Learning and TensorFlow

Lecture 1 What is Machine Learning?

Lecture 2 Introduction to TensorFlow

Lecture 3 TensorFlow vs. Other Machine Learning frameworks

Lecture 4 Installing TensorFlow

Lecture 5 Setting up your Development Environment

Lecture 6 Verifying the Installation

Section 2: Basics of TensorFlow

Lecture 7 Introduction to Tensors

Lecture 8 Tensor Operations

Lecture 9 Constants, Variables, and Placeholders

Lecture 10 TensorFlow Computational Graph

Lecture 11 Creating and Running a TensorFlow Session

Lecture 12 Managing Graphs and Sessions

Lecture 13 Building a Simple Feedforward Neural Network

Lecture 14 Activation Functions

Lecture 15 Loss Functions and Optimizers

Section 3: Intermediate TensorFlow

Lecture 16 Introduction to Keras API

Lecture 17 Building Complex Models with Keras

Lecture 18 Training and Evaluating Models

Lecture 19 Introduction to CNNs(Convolutional Neural Networks)

Lecture 20 Building and Training CNNs with TensorFlow

Lecture 21 Transfer Learning with Pre-trained CNNs

Lecture 22 Introduction to RNNs(Recurrent Neural Networks)

Lecture 23 Building and Training RNNs with TensorFlow

Lecture 24 Applications of RNNs: Language Modeling, Time Series Prediction

Section 4: Advanced TensorFlow

Lecture 25 Saving and Loading Models

Lecture 26 TensorFlow Serving for Model Deployment

Lecture 27 TensorFlow Lite for Mobile and Embedded Devices

Lecture 28 Introduction to Distributed Computing with TensorFlow

Lecture 29 TensorFlow's Distributed Execution Framework

Lecture 30 Scaling TensorFlow with TensorFlow Serving and Kubernetes

Lecture 31 Introduction to TFX(TensorFlow Extended)

Lecture 32 Building End-to-End ML Pipelines with TFX

Lecture 33 Model Validation, Transform, and Serving with TFX

Section 5: Practical Applications and Projects

Lecture 34 Image Classification

Lecture 35 Natural Language Processing

Lecture 36 Recommender Systems

Lecture 37 Object Detection

Lecture 38 Building a Sentiment Analysis Model

Lecture 39 Creating an Image Recognition System

Lecture 40 Developing a Time Series Prediction Model

Lecture 41 Implementing a Chatbot

Section 6: Further Learning and Resources

Lecture 42 Generative Adversarial Networks (GANs)

Lecture 43 Reinforcement Learning with TensorFlow

Lecture 44 Quantum Machine Learning with TensorFlow Quantum

Lecture 45 TensorFlow Documentation and Tutorials

Lecture 46 Online Courses and Books

Lecture 47 TensorFlow Community and Forums

Section 7: Summary of Tensor Flow

Lecture 48 Summary of Key Concepts

Lecture 49 Next Steps in Your TensorFlow Journey

Aspiring Data Scientists and ML Engineers who want to build a solid foundation in TensorFlow for real-world machine learning projects,Developers and Programmers interested in expanding their skills to include machine learning and neural networks,Students and Professionals in data science, AI, or related fields, looking to add TensorFlow to their toolkit,Self-Learners who enjoy hands-on projects and are ready to dive into practical, scalable applications in machine learning