TensorFlow - Big Data Trunk

https://project.bigdatatrunk.com Quality Corporate and Classroom Training in Bay Area CA Tue, 28 Oct 2025 12:18:15 +0000 en-US hourly 1 https://wordpress.org/?v=7.0 AI Toolkit Scikit-learn, Tensor Flow and Keras https://project.bigdatatrunk.com/courses/ai-toolkit-scikit-learn-tensor-flow-and-keras/ https://project.bigdatatrunk.com/courses/ai-toolkit-scikit-learn-tensor-flow-and-keras/#respond Wed, 09 Nov 2022 02:52:21 +0000 https://www.bigdatatrunk.com/?post_type=lp_course&p=25531 TensorFlow has become the standard for building machine learning and AI models.

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Overview
Prerequisites
Audience
Curriculum

Description:

TensorFlow has become the standard for building machine learning and AI models. In this course you will learn how TensorFlow makes it easy for beginners and experts to create machine learning models. We will begin with understanding TensorFlow’s intui-tive Sequential API which enables you to piece your model together with simple building blocks. TensorFlow 2.0 uses the Keras API standard for defining and training neural networks which enables fast prototyping and is very user friendly. We will then advance into using Keras’ Functional API which enables a little more customizability and control over the design of your model. We will learn about convolutional and recurrent neural networks and use TensorFlow to build them and TensorBoard to visualize their performance. We will also explore how TensorFlow enables the serving and deploying of models.(NO CHANGE)

Course Code/Duration:

BDT260 / 3-Days

Learning Objectives:

After this course, you will be able to:
Use Google Colab
Understand the fundamental concepts of machine learning
Understand Neural Networks
Understand the differences between machine learning and deep learning
Understand TensorFlow and Keras
Understand how to use the Keras’ Sequential API
Understand Convolutional Neural Networks (CNNs)
Build a CNN in TensorFlow
Understand Recurrent Neural Networks (CNNs)
Build an RNN in TensorFlow
Visualize models using TensorBoard
Understand how to use Keras’ Functional API
Understand how to serve a TensorFlow model

Basic Python Programming experience

Candidates with Computer Science or Electrical Engineering degree or equivalent experience and pursuing a career in Robotics.

Course Outline:

Course Introduction
Overview of Machine Learning

Milestone 1: Intro to Google Colab

Introduction to neural networks
Introducing Perceptrons
Step Function
Updating the weights
Hidden Layers
Activation functions
Loss functions
Gradient descent
Back propagation
Vanishing gradient problem and ReLU
Understanding the intuition behind neural networks
Introduction to TensorFlow and Keras

Milestone 3: Building a CNN in TensorFlow

Image recognition
Hyperparameter tuning
Image augmentation
Visualize Models using TensorBoard
Keras functional API
Inputs
Layers
Model
Image Segmentation
Introduction to Recurrent Neural Networks (RNN)
Recurrence
Tanh activation function
LSTM

Milestone 4: Building an RNN in TensorFlow

Preprocessing text
Word Embeddings
Natural Language Processing
Sentiment analysis

Milestone 5: Deploying a TensorFlow Model

TensorFlow serving
Flask and TensorFlow
Conclusion: Next Steps

Structured Activity/Exercises/Case Studies:

Milestone 1: Google Colab
Milestone 2: Using TensorFlow to implement a neural network
Milestone 3: Building a CNN in TensorFlow
Milestone 4: Building an RNN in TensorFlow
Milestone 5: Deploying a TensorFlow Model

Training material provided: Yes (Digital format)

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]]> https://project.bigdatatrunk.com/courses/ai-toolkit-scikit-learn-tensor-flow-and-keras/feed/ 0 Deep Learning: Hands On using TensorFlow & Keras https://project.bigdatatrunk.com/courses/deep-learning-hands-on-using-tensorflow-keras/ https://project.bigdatatrunk.com/courses/deep-learning-hands-on-using-tensorflow-keras/#respond Wed, 16 Mar 2022 08:25:29 +0000 https://www.bigdatatrunk.com/?post_type=lp_course&p=23799 Artificial Intelligence and machine learning are the cornerstones of the next revolution in computing.

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Overview
Prerequisites
Audience
Curriculum

Description:

Unlock the potential of Artificial Intelligence with our comprehensive Deep Learning course, featuring hands-on training using TensorFlow and Keras. As technology advances, mastering Deep Learning is essential to stay at the forefront of AI and machine learning. Deep Learning, a powerful subset of Machine Learning, excels in accuracy, especially with extensive data.

Our course caters to intermediate to advanced professionals, addressing the surging demand for skilled Deep Learning Engineers across diverse industries. Dive into the world of nested hierarchies, where concepts are intricately linked, and abstract ideas are derived from simpler ones. This certification training empowers you to harness the supremacy of Deep Learning, enabling you to predict future outcomes based on past data patterns. Join us on this journey towards AI excellence!

Course Code/Duration:

BDT190 / 3 Days

Learning Objectives:

After this course, you will be able to:

Grasp the fundamentals of Artificial Neural Networks (ANNs).
Explore Convolutional Neural Networks (CNNs) from basics to advanced concepts.
Understand when and why to use Recurrent Neural Networks (RNNs).
Apply CNNs in real-world scenarios.
Gain practical experience with RNNs in a real-life context.
Get hands-on training in TensorFlow and Keras.
Build and fine-tune CNN models using TensorFlow.
Master the applications of Long Short-Term Memory (LSTM) networks.
Learn real-time object detection using YOLO (You Only Look Once).

Basic programming knowledge and basic understanding of machine learning concept.

Anyone like any stream programmer, Analyst, data engineer, want to started their career or know more about Machine Learning and Deep Learning.

Course Outline:

1. Introduction and Basic Knowledge

Overview of Machine Learning
Different type of ML
IDE (Anaconda) Installation and Intro.
Simple Program of basic ML and Hands On.

2. Introduction of Neural Networks

Introducing Perceptrons
Step Function
Updating the weights
Hidden Layers
Activation functions
Loss functions
Gradient descent
Back propagation
Vanishing gradient problem and ReLU
Understanding the intuition behind neural networks

3. Introduction of TensorFlow and Keras

Why TensorFlow and Keras
Difference in Tensorflow and keras
Sample Code and Hands on Tensorflow and Keras

4. Introduction to Convolutional Neural Networks

Convolutional layers
Pooling layers
Kernel
Stride
Padding
Pooling
Flatten
Fully connected layers

5. Building CNN using TensorFlow

Image recognition
Hyperparameter tuning
Image augmentation
Visualize Modes TensorBoard

6. Recurrent Neural Network

Why RNN and where to use
Basic concept and architecture of RNN
Sample Code and Hands on

7. LSTM (Long Short Term Memory)

Why LSTM and where to use
Basic difference among ML, DL, RNN, LSTM
Basic concept and architecture of RNN

8. RNN: Building Code

Pre-processing text
Word Embeddings
Natural Language Processing

9. Real Time Object Detection

YOLO (You Only Look Once) basic and Installation
Hands on YOLO
Object Detection

Training material provided:

Yes (Digital format)

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]]> https://project.bigdatatrunk.com/courses/deep-learning-hands-on-using-tensorflow-keras/feed/ 0 Kickstart TensorFlow & Keras in a Day https://project.bigdatatrunk.com/courses/kickstart-tensorflow-keras-in-a-day/ https://project.bigdatatrunk.com/courses/kickstart-tensorflow-keras-in-a-day/#respond Thu, 03 Feb 2022 07:22:59 +0000 https://www.bigdatatrunk.com/?post_type=lp_course&p=23458 TensorFlow has become integral part of Machine and Deep Learning techniques.

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Overview
Prerequisite
Audience
Curriculum

Description:

Unlock the Power of TensorFlow & Keras in Just One Day! Discover why TensorFlow is a cornerstone of Machine and Deep Learning, with top experts commanding over 200K salaries and incredible demand. Dive into our course, where we'll lay the essential groundwork for constructing Artificial Neural Networks with TensorFlow. Gain an in-depth understanding of tensors and their manipulation techniques. Then, seamlessly transition into building your own neural network using Keras and TensorFlow, all while mastering the diverse Keras APIs. Kickstart your journey to TensorFlow and Keras expertise with our comprehensive training!

Course Code/Duration:

BDT185 / 1 Day

Learning Objectives:

After this course, you will be able to:

TensorFlow Fundamentals
Working with Tensors
Artificial Neural Network (Regression)
Artificial Neural Network (Classification)
Transfer Learning

Basic knowledge of python and fundamentals of Machines Learning.

This course is designed for anyone interested to get started with using TensorFlow and build Artificial Neural Networks. It is geared towards Data Scientists, Data Engineers, Software Engineers, Software Architects, Quality Assurance Engineers.

Course Outline:

TensorFlow Fundamentals
1. Machine Learning v/s Deep Learning what’s the difference?
2. Understand what is TensorFlow?
3. Why use TensorFlow?
Working with Tensors
1. What are tensors?
2. Creating Tensors
3. Getting Tensor Attributes
4. Manipulating Tensors
5. Math operations on Tensors
6. NumPy & Tensors
7. Hands-on lab with Tensors
Artificial Neural Network – Regression
1. Learn to build a neural network for a regression problem
2. Understand building a sequential network with layers
3. Components that make up neural networks (loss function, architecture, optimization functions)
4. Hands-on lab with ANN for Regression
Artificial Neural Network – Classification
1. Build a neural network for a classification problem
2. Learn about the loss functions, metrics and optimizers used for Classification
3. Hands-on lab with ANN for Classification
Transfer Learning
1. Understand what is transfer learning with TensorFlow
2. Learn about transfer learning types
3. Using TensorFlow Hub for pre-trained models
4. Learn about using TensorFlow Callbacks
5. Learn about building a model with Keras Functional API
6. Hands-on lab with Transfer Learning

Training material provided:

Yes (Digital format)

Hands-on Lab: All the labs will be done in Google’s Colaboratory (Colab). Student must have a GMAIL Id that they can use.

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]]> https://project.bigdatatrunk.com/courses/kickstart-tensorflow-keras-in-a-day/feed/ 0 Byte-Sized Deep Learning Series: Applied Deep Learning for Natural Language Understanding https://project.bigdatatrunk.com/courses/byte-sized-deep-learning-series-applied-deep-learning-for-natural-language-understanding/ https://project.bigdatatrunk.com/courses/byte-sized-deep-learning-series-applied-deep-learning-for-natural-language-understanding/#respond Mon, 21 Jun 2021 07:48:35 +0000 https://www.bigdatatrunk.com/?post_type=lp_course&p=22548 This 90-minute session will explore the application of deep learning models known as transformers to solve common natural language understanding tasks.

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Overview
Prerequisite
Audience
Curriculum

Description:

This 90-minute session will explore the application of deep learning models known as transformers to solve common natural language understanding tasks (e.g., question answering, sentiment classification, text summarization, text generation). Learners will use the popular Hugging Face library.

Course Code/Duration:

BDT148 / 90 minutes

Learners should have a basic knowledge of Python programming.

This course is for those who would like to understand how to apply deep learning to common natural language understanding tasks.

Course Outline:

During this course, you will have the opportunity to:

Understand the transformer model and why it is superior to previous approaches to natural language understanding.
Learn how to use the popular Hugging Face library to solve common use cases.
Apply pre-trained models to answer questions related to a corpus of text, summarize text, generate novel text, and several other use cases including paraphrasing, sentiment classification, and text completion.

Training material provided: Yes (Digital format)

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]]> https://project.bigdatatrunk.com/courses/byte-sized-deep-learning-series-applied-deep-learning-for-natural-language-understanding/feed/ 0 Byte-Sized Deep Learning Series: Understanding Language https://project.bigdatatrunk.com/courses/byte-sized-deep-learning-series-understanding-language/ https://project.bigdatatrunk.com/courses/byte-sized-deep-learning-series-understanding-language/#respond Mon, 21 Jun 2021 07:44:58 +0000 https://www.bigdatatrunk.com/?post_type=lp_course&p=22546 This 90-minute session will explore the use of deep learning and the role of recurrent neural networks in language understanding.

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Overview
Prerequisite
Audience
Curriculum

Session Description:

Course Code/Duration:

BDT151 / 90 minutes

Learning Objectives:

During this course, you will have the opportunity to:

Understand the transformer model and why it is superior to previous approaches to natural language understanding.
Learn how to use the popular Hugging Face library to solve common use cases.
Apply pre-trained models to answer questions related to a corpus of text, summarize text, generate novel text, and several other use cases including paraphrasing, sentiment classification, and text completion.

Training material provided: Yes (Digital format)

Learners should have a basic knowledge of Python programming.

This course is for those who would like to understand how to apply deep learning to common natural language understanding tasks.

The post Byte-Sized Deep Learning Series: Understanding Language first appeared on Big Data Trunk.

]]> https://project.bigdatatrunk.com/courses/byte-sized-deep-learning-series-understanding-language/feed/ 0 Byte-Sized Deep Learning Series: Image Recognition https://project.bigdatatrunk.com/courses/byte-sized-deep-learning-series-image-recognition/ https://project.bigdatatrunk.com/courses/byte-sized-deep-learning-series-image-recognition/#respond Mon, 21 Jun 2021 07:41:28 +0000 https://www.bigdatatrunk.com/?post_type=lp_course&p=22544 This course is for those who are interested in gaining an understanding of how deep learning is used to recognize and classify images

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Overview
Prerequisite
Audience
Curriculum

Description:

This 90-minute session will explore the use of deep learning and the role of convolution neural networks in computer vision and image classification. Learners will use TensorFlow to build and train deep learning models to classify images.

Course Code/Duration:

BDT149 / 90 minutes

Learners should have a basic knowledge of Python programming.

This course is for those who are interested in gaining an understanding of how deep learning is used to recognize and classify images (e.g., medical imaging, face recognition, image classification).

Course Outline:

During this course, you will have the opportunity to:

Explore deep learning and the advantages of deep learning models.
Understand convolutional neural networks and how they learn to recognize images.
Use TensorFlow to build and train a deep neural network to classify images.

Training Material Provided: Yes (Digital Format)

The post Byte-Sized Deep Learning Series: Image Recognition first appeared on Big Data Trunk.

]]> https://project.bigdatatrunk.com/courses/byte-sized-deep-learning-series-image-recognition/feed/ 0 Byte-Sized Deep Learning Series: Introducing Neural Networks https://project.bigdatatrunk.com/courses/byte-sized-deep-learning-series-introducing-neural-networks/ https://project.bigdatatrunk.com/courses/byte-sized-deep-learning-series-introducing-neural-networks/#respond Mon, 21 Jun 2021 07:18:53 +0000 https://www.bigdatatrunk.com/?post_type=lp_course&p=22543 This course is for those who would like to understand how to apply deep learning to common natural language understanding tasks.

The post Byte-Sized Deep Learning Series: Introducing Neural Networks first appeared on Big Data Trunk.

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Overview
Prerequisites
Audience
Curriculum

Session Description:

Dive into Neural Networks and Deep Learning in 90 Minutes! Discover how they work, and harness the power of TensorFlow to build and train your own neural network for data-driven insights.

Course Code/Duration:

BDT148 / 90 minutes

Learning Objectives:

During this course, you will have the opportunity to:

Explore machine learning, the uniqueness of neural networks,and how data enables machines to learn.
Understand how deep learning is different from traditional machine learning.
Understand how neural networks learn and their role in deep learning.
Apply your learning by using TensorFlow to build and train a neural network to learn from data.

Learners should have a basic knowledge of Python programming.

This course is for those who are interested in gaining an understanding of machine learning and neural networks, foundational to the use of deep learning.

Course Outline:

Exploring Machine Learning and Neural Networks
Understanding the Difference between Deep Learning and Traditional Machine Learning
Learning about Neural Networks and Their Role in Deep Learning
Hands-on Lab: Building and Training a Neural Network with TensorFlow

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]]> https://project.bigdatatrunk.com/courses/byte-sized-deep-learning-series-introducing-neural-networks/feed/ 0 Creating and Deploying Machine Learning Models on GCP https://project.bigdatatrunk.com/courses/creating-and-deploying-machine-learning-models-on-gcp-scikit-learn-and-tensorflow-models/ https://project.bigdatatrunk.com/courses/creating-and-deploying-machine-learning-models-on-gcp-scikit-learn-and-tensorflow-models/#respond Mon, 21 Dec 2020 02:27:55 +0000 https://www.bigdatatrunk.com/?post_type=lp_course&p=21811 Machine learning has become an integral part of virtually every industry. And being able to create machine learning models and gain insight from data is an invaluable skill. Moreover, being able to deploy these models is imperative.

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Overview
Prerequisites
Audience
Curriculum

Description:

Unlock the power of machine learning across industries with this comprehensive class. Learn to create machine learning models using Scikit-Learn and delve into deep learning with TensorFlow. Discover the art of model saving and version management, and seamlessly deploy your models using Google Cloud Platform's AI Platform for actionable insights and predictions.

Course Code/Duration:

BDT71 / 1 Day

Learning Objectives:

After this course, you will be able to:

Understand How to Deploy Models on Google Cloud Platform (GCP)
Understand How Machines Learn
Understand Structured and Unstructured Data
Compare AI vs. Machine Learning vs. Deep Learning
Use Common Machine Learning Algorithms
Use Scikit-Learn to Create and Train Machine Learning Models
Use TensorFlow and Keras to Create and Train Deep Learning Models
Use AI Platform on GCP
Use Cloud Storage on GCP
Use AI Platform Notebooks on GCP to Build and Train Scikit-Learn and TensorFlow Machine Learning Models
Use GCP to Deploy Trained Machine Learning Models
Understand the fundamental techniques through Demos and hands-on labs

Python experience
Basic understanding of Machine Learning

This course is designed for Software Architects, Developers, Data Engineer, Data Analyst and Machine Learning Engineer.

Course Outline:

Course Introduction
Compare AI vs ML vs DL
Understanding how machines learn
Structured vs. Unstructured data

Lab:

Installing Anaconda and TensorFlow
Common machine learning algorithms
Using Scikit-Learn to create and train machine learning models
- .fit()
- score()
- .predict()

Lab:

Using scikit-learn to build a linear and a logistic regression model
Saving a Scikit-Learn Model
- pickle (model.pkl)
- joblib (model.joblib)
Using GCP Cloud Storage to Store Saved Models

Lab:

Creating a Cloud Storage bucket on GCP and uploading models
Introducing Keras/TensorFlow
- TensorFlow intro
- Using Keras

Lab:

Using Keras to builda linear regression and a neural network model
Saving a TensorFlow Model
Saving the model in tensorflow format
Storing model in GCP Cloud Storage
Create Different Model Versions for Deployment
Introducing AI Platform
AI Platform Notebooks
AI Platform Models
Deploy Models on GCP

Lab:

Create an AI Platform model resource and version resource Serve Models from GCP

Lab:

Create input data and query deployed model for predictions Next steps

Structured Activity/Exercises/Case Studies:

Installing Anaconda and TensorFlow
Using scikit-learn to build a linear and a logistic regression model
Creating a Cloud Storage bucket on GCP and uploading models
Using Keras to builda linear regression and a neural network model
Create an AI Platform model resource and version resource
Create input data and query deployed model for predictions

Training material provided:

Yes (Digital format)

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Description:

Course Code/Duration:

Learning Objectives:

Course Outline:

Milestone 1: Intro to Google Colab

Milestone 2: Using TensorFlow to implement a neural network

Milestone 3: Building a CNN in TensorFlow

Milestone 4: Building an RNN in TensorFlow

Milestone 5: Deploying a TensorFlow Model

Structured Activity/Exercises/Case Studies:

Training material provided: Yes (Digital format)

Description:

Course Code/Duration:

Learning Objectives:

Course Outline:

1. Introduction and Basic Knowledge

2. Introduction of Neural Networks

3. Introduction of TensorFlow and Keras

4. Introduction to Convolutional Neural Networks

5. Building CNN using TensorFlow

6. Recurrent Neural Network

7. LSTM (Long Short Term Memory)

8. RNN: Building Code

9. Real Time Object Detection

Training material provided:

Description:

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Session Description:

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Learning Objectives:

Training material provided: Yes (Digital format)

Session Description:

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Description:

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Learning Objectives:

Course Outline:

Lab:

Lab:

Lab:

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