
Explore the basics of TensorFlow, Google's free open-source deep learning framework built on Python. Learn tensors, graphs, and sessions and how it enables CNNs, RNNs, image processing, and NLP.
Explore TensorFlow applications in voice and image recognition, time series, and text analytics, including sentiment analysis and recommendations, and learn how its interactive, scalable, open-source tools support large neural networks.
Discover TensorFlow basics by examining tensors, computation graphs, and sessions; compare low level and high level APIs, and learn how nodes and edges form calculations.
Differentiate constants and variables in TensorFlow, initialize global variables, and use placeholders with feed dictionaries to perform matrix multiplication in a session.
Explore TensorFlow's data flow graph, placeholders, and operations, the core components enabling graph-based computation and cross-platform deployment from desktop to mobile.
Explore in-memory data loading for small datasets and TensorFlow data pipelines for large ones. Build a simple pipeline with data, placeholders, dataset, and batch feeding via a session.
Learn to build an asynchronous, optimized tensorflow data pipeline guided by the ETN principle, with approaches for raw data and tensorflow datasets for training.
Explore tensor concepts in TensorFlow, including shapes, data types, and basic operators, and learn to create constants, placeholders, and vectors, arrays, and matrices in 1D, 2D, and 3D.
Learn the core linear algebra concepts essential for machine learning and deep learning, including scalars, vectors, matrices, and operations like matrix multiplication, identity, and diagonal or triangular forms.
Explore scalar, vector, and matrix operations: scale a matrix, multiply matrices and vectors, add or subtract matrices, and apply identities, inverses, determinants, and transposes in linear algebra.
Explore NumPy's linear algebra module in Python, covering dot product, matrix multiplication, determinant, solving linear equations, and inverses, with vectors and one- and two-dimensional arrays.
Explore practical matrix multiplication, dot products, and dot functions in two-dimensional matrices and vectors, with step-by-step examples from the linear algebra module.
Master matrix multiplication concepts with the matmul function across 2d matrices, stacks, and broadcasting. Preview determinant, inverse, and the solve function as next steps.
Learn to implement the determinant function for square matrices in Python, covering 2x2 and 3x3 cases with diagonal formula, and apply it to solve and invert matrices in linear algebra.
Learn to calculate determinants of square matrices, from 2x2 to 3x3, using the ad minus bc formula, and apply determinants to find inverses and solve linear systems.
Learn to compute the inverse of a matrix using numpy, verify with the identity, handle square versus non-square cases, and solve linear equations to obtain X, Y, and Z.
Introduce Python basics for deep learning with TensorFlow and explore Python operators—arithmetic, logical, relational, membership, identity, and bitwise—along with examples and practical demonstrations.
Master Python basics, including bitwise operators and binary representations, and explore data types: lists, tuples, dictionaries, and sets, plus indexing, slicing, and essential list methods such as append and extend.
Learn how to define and call Python functions, including built-in vs user-defined functions, pass arguments, return values, and use a list example to find the largest number, highlighting modular code.
Explore Python function arguments by examining strings and numbers, using return and input, and distinguish mutable lists from immutable strings through examples of call by value and call by reference.
Explore Python function arguments, including required, keyword, default, and variable-length forms. Learn how to pass arguments at function calls and understand interpreter behavior when inputs are missing.
Learn how to define and call Python functions using default, variable, and keyword arguments, with practical examples showing how default values work.
Explore Python functions by using default arguments, variable-length arguments, and keyword arguments, and see how defaults, *args, and named parameters control call behavior.
Learn to write a Python program that checks prime numbers using loops and the break and continue keywords. Explore prime definitions and how to skip or terminate iterations.
Discover how to import numpy as np, create ndarrays from lists, and set array attributes such as minimum dimensions and data types (float, complex, boolean).
Explore matplotlib basics for data visualization in Python, including plotting lines and scatter plots, axes, figures, labels, legends, and common figure operations.
Explore numpy attributes such as shape and dimensions, then practice reshaping arrays, using the range function, and inspecting size and itemsize, followed by creating arrays with empty, zeros, and ones functions.
Master advanced two-dimensional NumPy slicing and indexing, build 2D arrays, map values across rows and columns, and filter elements with conditional selection.
A warm welcome to the Deep Learning with TensorFlow course by Uplatz.
TensorFlow is an end-to-end open-source machine learning / deep learning platform. It has a comprehensive ecosystem of libraries, tools, and community resources that lets AI/ML engineers, scientists, analysts build and deploy ML-powered deep learning applications. The name TensorFlow is derived from the operations which neural networks perform on multidimensional data arrays or tensors. Deep learning is a subfield of machine learning that is a set of algorithms that is inspired by the structure and function of the brain.
TensorFlow is a machine learning framework that Google created and used to design, build, and train deep learning models. You can use the TensorFlow library do to numerical computations, which in itself doesn’t seem all too special, but these computations are done with data flow graphs. In these graphs, nodes represent mathematical operations, while the edges represent the data, which usually are multidimensional data arrays or tensors, that are communicated between these edges.
In simple words, TensorFlow is an open-source and most popular deep learning library for research and production. TensorFlow in Python is a symbolic math library that uses dataflow and differentiable programming to perform various tasks focused on training and inference of deep neural networks. TensorFlow manages to combine a comprehensive and flexible set of technical features with great ease of use.
There have been some remarkable developments lately in the world of artificial intelligence, from much publicized progress with self-driving cars to machines now composing imitations or being really good at video games. Central to these advances are a number of tools around to help derive deep learning and other machine learning models, with Torch, Caffe, and Theano amongst those at the fore. However, since Google Brain went open source in November 2015 with their own framework, TensorFlow, the popularity of this software library has skyrocketed to be the most popular deep learning framework.
TensorFlow enables you to build dataflow graphs and structures to define how data moves through a graph by taking inputs as a multi-dimensional array called Tensor. It allows you to construct a flowchart of operations that can be performed on these inputs, which goes at one end and comes at the other end as output.
Top organizations such as Google, IBM, Netflix, Disney, Twitter, Micron, all use TensorFlow.
Uplatz provides this extensive course on TensorFlow. This TensorFlow course covers TensorFlow basics, components, pipelines to advanced topics like linear regression, classifier, create, train and evaluate a neural network like CNN, RNN, auto encoders etc. with TensorFlow examples.
The TensorFlow training is designed in such a way that you'll be able to easily implement deep learning project on TensorFlow in an easy and efficient way. In this TensorFlow course you will learn the fundamentals of neural networks and how to build deep learning models using TensorFlow. This TensorFlow training provides a practical approach to deep learning for software engineers. You'll get hands-on experience building your own state-of-the-art image classifiers and other deep learning models. You'll also use your TensorFlow models in the real world on mobile devices, in the cloud, and in browsers. Finally, you'll use advanced techniques and algorithms to work with large datasets. You will acquire skills necessary to start creating your own AI applications and models.
You’ll master deep learning concepts and models using TensorFlow frameworks and implement deep learning algorithms, preparing you for a career as Deep Learning Engineer. Learn how to build a neural network and how to train, evaluate and optimize it with TensorFlow.
TensorFlow is completely based on Python. This course also provides a sound introduction to Python programming concepts, NumPy, Matplotlib, and Pandas so that you can acquire those skills in this course itself before moving on to learn the TensorFlow concepts. The aim of this TensorFlow tutorial is to describe all TensorFlow objects and method.
This TensorFlow course also includes a comprehensive description of TensorBoard visualization tool. You will gain an understanding of the mechanics of this tool by using it to solve a general numerical problem, quite outside of what machine learning usually involves, before introducing its uses in deep learning with a simple neural network implementation.
TensorFlow Architecture
TensorFlow architecture works in three parts:
Preprocessing the data
Build the model
Train and estimate the model
It is called TensorFlow because it takes input as a multi-dimensional array, also known as tensors. You can construct a sort of flowchart of operations (called a Graph) that you want to perform on that input. The input goes in at one end, and then it flows through this system of multiple operations and comes out the other end as output.
This is why it is called TensorFlow because the tensor goes in it flows through a list of operations, and then it comes out the other side.
TensorFlow - Course Syllabus