Best Deep Learning Training in France | Deep Learning Course with TensorFlow


Deep learning Training France | Deep learning Course France | Deep learning is the machine learning technique behind the most exciting capabilities in diverse areas like robotics, natural language processing, image recognition and artificial intelligence. In this course we will start with deep learning introduction and you’ll gain hands-on, practical knowledge of how to use deep learning with Keras 2.0, the latest version of a cutting edge library for deep learning. Mildaintrainings provides The Best Deep Learning Course france by Industry Experts.Deep learning training france Enroll & Get Certified now!

  • ✔ Course Duration : 4 DAYS / 32 hrs
  • ✔ Training Options : Live Online / Self-Paced / Classroom
  • ✔ Certification Pass : Guaranteed
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32 hrs

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20+ hrs



Deep learningtraining france is one of the most exciting and promising segments of Artificial Intelligence and machine learning technologies. This deep learning course france is designed to help you master deep learning techniques and build deep learning training france models using TensorFlow, the open-source software library developed by Google for the purpose of conducting machine learning and deep neural networks research.Deep learning training france It is one of the most popular software platforms used for deep learning training france and contains powerful tools to help you build and implement artificial neural networks.

Deep learning training france Advancements in deep learning training france are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. Deep learning course france With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results. Deep learning training france according to, the median salary for engineers with deep learning skills tops $120,000 per year.

What you will Learn

  • Understand the concepts of TensorFlow, its main functions, operations and the execution pipeline
  • Implement deep learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before
  • Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces
  • Build deep learning models in TensorFlow and interpret the results
  • Understand the language and fundamental concepts of artificial neural networks
  • Troubleshoot and improve deep learning models
  • Build your own deep learning project
  • Differentiate between machine learning, deep learning and artificial intelligence


Learning Objectives:
  • Machine Learning Primer
  • Machine Learning core concepts, scalable algorithms, project workflow
  • Objective Functions and Regularization
  • Understanding Objective Function of ML Algorithms
  • Metrics, Evaluation Methods and Optimizers
  • Popular Metrics in Detail: R2 Score, RMSE, Cross Entropy, Precision, Recall, F1 Score, ROC AUC, SGD, ADAM
  • Artificial Neural Network
  • ANN in detail, Forward Pass and Back Propagation
  • Machine Learning Vs Deep Learning
  • Core difference b/w ML and DL from implementation perspective
Learning Objectives:
  • Python Programming Primer
  • Installing Python, Programming Basics, Native Data types
  • Class, Inheritance and Magic Functions
  • Python Classes, Inheritance Concepts, Magic Functions
  • Special Functions in Python
  • Overview, Array, selecting data, Slicing, Iterating, Array Manipulations, Stacking, Splitting arrays, Key functions
  • Decorators and Special Functions
  • Decorators implementation with class
  • Context Manager ‘with’ in Python
  • Context Manager Application
  • Exception Handling
  • Try and Catch block
  • Python Package Management
  • Bundling and export python packages
Learning Objectives:
  • TensorFlow 2.0 Basics
  • TensorFlow core concepts, Tensors, core APIs
  • Concrete Functions, Datatypes, Control Statements
  • Polymorphic Functions, Concrete Functions, Datatypes, Control Statements, NumPy, Pandas
  • Autograph eager execution
  • tf.function autograph implementation
  • Sessions vs tf.function
  • Keras (TensorFlow 2.0 Built-in API) Overview
  • Sequential Models, configuring layers, loading data, train and test, complex models, call backs, save and restore Neural Network weights
  • Building Neural Networks in Keras
  • Building Neural networks from scratch in Keras
  • Implementing RNN, CNN in Keras
  • Building Recurrent Neural Networks for sequence data and Convolution Neural Networks for Image Classification
Learning Objectives:
  • Linear Algebra
  • Vectors, Matrices, Linear Transformation, Eigen Vectors, Matrix Operations, Special Matrices
  • Calculus – Derivatives: Calculus essentials, Derivatives and Partial Derivatives, Chain Rule, Derivatives of special functions
  • Probability Essentials: Probability basics and notations, Conditional probability, Essential Probability theorems for Machine Learning
  • Special functions: Relu, Sigmoid, SoftMax, Popular Loss Functions – Cross Entropy, Quadratic Loss Functions
Learning Objectives:
  • Deep Learning Network Concepts
  • Core concepts of Deep Learning Networks
  • Deep Dive into Activation Functions
  • Relu, Sigmoid, Tanh, SoftMax, Linear
  • Building simple Deep Learning Network
  • Simple DL network from starch
  • Tuning Deep Learning Network
  • Tuning Deep Learning Network Parameters for optimal performance, Stopping Criteria
  • Visualizing Training using TensorBoard
  • Visualizing Deep Learning Network using TensorBoard
Learning Objectives:
  • Deep Learning Architectures
  • Popular Deep learning Architectures – CNN, RNN, LSTM RNN, GRU RNN Introduction
  • Deep Dive into Convolutional Neural Network
  • Core Concepts of Convolutional Neural Network, Feature Maps, Relu Activation, Max Pooling
  • CNN Application – Image Classification
  • Image Classification implementation with CNN TensorFlow 2.0 (Keras)
  • Recurrent Neural Networks (RNN) Basics
  • RNN Architecture, BPTT Backprop through time, Mathematics of RNN
  • Vanishing Gradient and exploding Gradient problem, LTSM architecture, GRU Architecture.
  • LSTM RNN implementation in TensorFlow
  • LSTM RNN project.
Learning Objectives:
  • Big Data Platforms
  • Importing Big Data
  • PySpark functions for importing data from various sources and other big data frameworks
  • Machine Learning with PySpark
  • Implementing scalable ML models with PySpark
Learning Objectives:
  • Image Classification
  • Image Classification with CIFAR-10 Dataset
  • Human Face Detection
  • Traffic Sign Detection
  • Human Activity Detection
  • 20BN-something-something Dataset V2
  • Image Caption Generation


You can enroll for this classroom training online. Payments can be made using any of the following options and receipt of the same will be issued to the candidate automatically via email.
1. Online ,By deposit the mildain bank account
2. Pay by cash team training center location

Highly qualified and certified instructors with 20+ years of experience deliver more than 200+ classroom training.

Contact us using the form on the right of any page on the mildaintrainings website, or select the Live Chat link. Our customer service representatives will be able to give you more details.

You will never miss a lecture at Mildaintrainigs! You can choose either of the two options: View the recorded session of the class available in your LMS. You can attend the missed session, in any other live batch.

We have a limited number of participants in a live session to maintain the Quality Standards. So, unfortunately, participation in a live class without enrollment is not possible. However, you can go through the sample class recording and it would give you a clear insight about how are the classes conducted, quality of instructors and the level of interaction in a class.

Yes, you can cancel your enrollment if necessary prior to 3rd session i.e first two sessions will be for your evaluation. We will refund the full amount without deducting any fee for more details check our Refund Policy

Yes, the access to the course material will be available for lifetime once you have enrolled into the course.

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